/************************************************************************* 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