mirror of
https://asciireactor.com/otho/psdlag-agn.git
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284 lines
8.8 KiB
C++
284 lines
8.8 KiB
C++
/*
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* mod.hpp
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*
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* Created on: May 31, 2013
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* Author: azoghbi
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*/
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#ifndef MOD_HPP_
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#define MOD_HPP_
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#include "def.hpp"
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#include <specialfunctions.h>
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#include <solvers.h>
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class mod {
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void _CSfq( vec , vector<vec2>& , vector<vec2>& );
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public:
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int n,nfq,npar;
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double dt,f1,f2;
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vec lc,lce,t,FqL;
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vector<vec2> Cfq,Sfq,Cv,Cfq2,Sfq2;
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vec2 C,Ci,I,yyT,yyTmC,dC,C2,C3;
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vec Cilc;
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ae_int_t info; densesolverreport rep;
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mod();
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virtual ~mod();
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virtual void _C( vec );
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virtual void _dC( vec , int );
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virtual void step_pars( int , vec& , vec& );
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virtual void print_pars( vec& , vec& );
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virtual void print_pars( vec& , vec& , vec& );
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virtual void what_pars( int&, int& );
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void setlc(){ lc.setlength(n); lce.setlength(n); t.setlength(n);}
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void init( vec , int );
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double loglikelihood( vec );
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void dlikelihood( vec , double& , vec& , vec2& );
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void optimize( vec& , vec& );
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};
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// ----------------------------------------- //
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// ---------- mod container ---------------- //
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// ----------------------------------------- //
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template <class M>
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class Mod {
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int nmod,nfq;
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vector<M> mods;
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public:
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int npar;
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Mod( vector<lcurve> inlc , vec fqL ){
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int i;
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nmod = inlc.size();
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for( i=0 ; i<nmod ; i++ ){ mods.push_back( M( inlc[i] , fqL ) ); }
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npar = mods[0].npar;
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nfq = mods[0].nfq;
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}
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Mod( vector<lcurve> lc1, vector<lcurve> lc2 , vec fqL , vec pars ){
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int i;
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nmod = lc1.size();
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for( i=0 ; i<nmod ; i++ ){ mods.push_back( M( lc1[i] , lc2[i] , fqL , pars ) ); }
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npar = mods[0].npar;
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nfq = mods[0].nfq;
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}
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Mod( vector<lcurve> lc1, vector<lcurve> lc2 , vec fqL ){
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int i;
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nmod = lc1.size();
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for( i=0 ; i<nmod ; i++ ){ mods.push_back( M( lc1[i] , lc2[i] , fqL ) ); }
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npar = mods[0].npar;
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nfq = mods[0].nfq;
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}
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virtual ~Mod(){}
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double loglikelihood( vec p ){
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double l=0; for( int i=0 ; i<nmod ; i++ ){ l += mods[i].loglikelihood(p); }
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return l;
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}
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void dlikelihood( vec p , double& loglike , vec& grad , vec2& hess ){
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int i,j,nm;vec g; vec2 h; double l;g.setlength(npar);h.setlength(npar,npar);
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loglike = 0; for( i=0 ; i<npar ; i++ ){ grad[i]=0; for(j=0;j<npar;j++){ hess(i,j) = 0;}}
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for( nm=0 ; nm<nmod ; nm++ ){
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mods[nm].dlikelihood( p , l , g , h );
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loglike += l;
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for( i=0 ; i<npar ; i++ ){ grad[i] += g[i]; for(j=0;j<npar;j++){ hess(i,j) += h(i,j);}}
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}
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}
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void step_pars( int n, vec& dpar , vec& pars ){ mods[0].step_pars( n , dpar , pars );}
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void print_pars( vec& pars , vec& errs ){ mods[0].print_pars( pars , errs );}
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void print_pars( vec& pars , vec& errs1 , vec& errs2 ){ mods[0].print_pars( pars , errs1 , errs2 );}
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void what_pars( int& ip1 , int& ip2 ){ mods[0].what_pars( ip1 , ip2 );}
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double optimize( vec& pars , vec& errs ){
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int nmax = 400;
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double tol = 1e-3;
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ae_int_t info; densesolverreport rep;
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int i,j,n,sing=0,np;
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double loglike,dpmax,dl,prevl=-1e20;
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vec tmppars,dpar,grad,prevp; vec2 hess,hessi,ii;
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tmppars.setlength(npar); dpar.setlength(npar); grad.setlength(npar); prevp.setlength(npar);
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hess.setlength( npar , npar );ii.setlength(npar,npar);hessi.setlength(npar,npar);
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for( i=0 ; i<npar ; i++ ){
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tmppars[i] = pars[i]; dpar[i] = 0.01*pars[i]; ii(i,i)=1;for(j=0;j<i;j++){ii(i,j)=0;ii(j,i)=0;}
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prevp[i] = pars[i];
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}
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for( n=1 ; n<= nmax ; n++ ){
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dlikelihood( tmppars , loglike , grad , hess );
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// ---------- //
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if( loglike != loglike ){ for( i=0 ; i<npar ; i++ ){ dpar[i]/=10;tmppars[i] = prevp[i] + dpar[i]; } continue;}
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dl = loglike - prevl;
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// ---------- //
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vector<int> ic,iv; sing = 0;
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for( i=0; i <npar ; i++ ){ if( fabs(grad[i]) < 1e-5 ){ sing=1;ic.push_back(i); }else { iv.push_back(i);}}
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if( sing == 1 and iv.size()!=0 ){
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np = iv.size();
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vec g; vec2 h,hi,iii; g.setlength(np); h.setlength(np,np); hi.setlength(np,np);iii.setlength(np,np);
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for( i=0 ; i<np ; i++ ){
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iii(i,i) = 1; h(i,i) = hess(iv[i],iv[i]);
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g[i] = grad[iv[i]]; for(j=0;j<i;j++){ h(i,j) = hess(iv[i],iv[j]);h(j,i)=h(i,j);iii(i,j)=0;iii(j,i)=0;}
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}
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spdmatrixcholesky( h , np , false );
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spdmatrixcholeskysolvem( h , np , false , iii , np , info , rep , hi );
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dpmax = -1e20;
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for( i=0 ; i<np ; i++ ){
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dpar[iv[i]] = 0; for( j=0 ; j<np ; j++ ){ dpar[iv[i]] += g[j] * hi(i,j);}
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dpmax = max( dpmax , fabs(dpar[iv[i]]) );
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}
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for( i=0 ; i<int(ic.size()); i++ ){ dpar[ic[i]] = 0;}
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}else {
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// --------- //
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spdmatrixcholesky( hess , npar , false );
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spdmatrixcholeskysolvem( hess , npar , false , ii , npar , info , rep , hessi );
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dpmax = -1e20;
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for( i=0 ; i<npar ; i++ ){
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dpar[i] = 0; for( j=0 ; j<npar ; j++ ){ dpar[i] += grad[j] * hessi(i,j);}
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dpmax = max( dpmax , fabs(dpar[i]) );
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}
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}
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for(i=0;i<npar;i++){prevp[i] = tmppars[i];}
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prevl = loglike;
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if ( dl < 2 and n>10 ){for(i=0;i<npar;i++){dpar[i]/=2;}}
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step_pars( n , dpar , tmppars );
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cout << n << " " << dpmax << " " << loglike << " "; for(j=0;j<npar;j++){cout << tmppars[j] << " ";} cout << endl;
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if ( dpmax<tol ) break;
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}
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for( i=0 ; i<npar ; i++ ){
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pars[i] = tmppars[i];
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errs[i] = sqrt(hessi(i,i));
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}
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cout << setfill('-') << setw(40) << "\n";
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for(i=0;i<npar;i++){cout << pars[i] << " ";} cout << endl;
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for(i=0;i<npar;i++){cout << errs[i] << " ";} cout << endl;
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for(i=0;i<npar;i++){cout << grad[i] << " ";} cout << endl;
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cout << setfill('-') << setw(40) << "\n" << setfill(' ');
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print_pars( pars, errs );
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return loglike;
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}
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double opti( vec pars , vec errs ,int k ){
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int nmax = 400;
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double tol = 1e-3;
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ae_int_t info; densesolverreport rep;
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int i,j,n,np;
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double loglike,dpmax,dl,prevl=-1e20;
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vec tmppars,dpar,grad,prevp; vec2 hess,hessi,ii;
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tmppars.setlength(npar); dpar.setlength(npar); grad.setlength(npar); prevp.setlength(npar);
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hess.setlength( npar , npar );ii.setlength(npar,npar);hessi.setlength(npar,npar);
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vector<int> ic,iv; np = npar - 1;
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vec g; vec2 h,hi,iii; g.setlength(np); h.setlength(np,np); hi.setlength(np,np);iii.setlength(np,np);
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ic.push_back(k);
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for( i=0 ; i<npar ; i++ ){
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tmppars[i] = pars[i]; dpar[i] = 0.01*pars[i]; ii(i,i)=1;for(j=0;j<i;j++){ii(i,j)=0;ii(j,i)=0;}
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prevp[i] = pars[i];
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if(i==k){continue;} iv.push_back(i);
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}
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for( n=1 ; n<= nmax ; n++ ){
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dlikelihood( tmppars , loglike , grad , hess );
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// ---------- //
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if( loglike != loglike ){ for( i=0 ; i<npar ; i++ ){ tmppars[i] = prevp[i] + dpar[i]/10; } continue;}
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dl = loglike - prevl;
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// ---------- //
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for( i=0 ; i<np ; i++ ){
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iii(i,i) = 1; h(i,i) = hess(iv[i],iv[i]);
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g[i] = grad[iv[i]]; for(j=0;j<i;j++){ h(i,j) = hess(iv[i],iv[j]);h(j,i)=h(i,j);iii(i,j)=0;iii(j,i)=0;}
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}
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spdmatrixcholesky( h , np , false );
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spdmatrixcholeskysolvem( h , np , false , iii , np , info , rep , hi );
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dpmax = -1e20;
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for( i=0 ; i<np ; i++ ){
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dpar[iv[i]] = 0; for( j=0 ; j<np ; j++ ){ dpar[iv[i]] += g[j] * hi(i,j);}
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dpmax = max( dpmax , fabs(dpar[iv[i]]) );
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}
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for( i=0 ; i<int(ic.size()); i++ ){ dpar[ic[i]] = 0;}
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for(i=0;i<npar;i++){prevp[i] = tmppars[i];}
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prevl = loglike;
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if ( dl < 2 and n>10 ){for(i=0;i<npar;i++){dpar[i]/=2;}}
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step_pars( n , dpar , tmppars );
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cout << n << " " << dpmax << " " << loglike << " "; for(j=0;j<npar;j++){cout << tmppars[j] << " ";} cout << endl;
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if ( dpmax<tol ) break;
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}
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return loglike;
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}
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void errors_avg( vec& pars , vec& errs ){
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vec errs1,errs2;errs1.setlength(npar);errs2.setlength(npar);
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errors( pars , errs1 , 1 );
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errors( pars , errs2 , -1 );
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for(int i=0;i<npar;i++){errs[i] = (errs1[i]+errs2[i])/2;}
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}
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void errors( vec& pars , vec& errs1 , vec& errs2 ){
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errors( pars , errs1 , 1 );
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errors( pars , errs2 , -1 );
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}
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void errors( vec& pars , vec& errs , int sign=-1){
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double tol = 1e-3 , delchi = 1;
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int i,ip,ip1,ip2;
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double pu,pd,phalf=0,bestlike,tmplike,dl;
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vec pars0,errs0,tmpp; pars0.setlength(npar);errs0.setlength(npar);tmpp.setlength(npar);
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bestlike = optimize( pars , errs );
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for(i=0;i<npar;i++){pars0[i] = pars[i];errs0[i]=errs[i];}
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what_pars( ip1 , ip2 );
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for( ip=ip1 ; ip<ip2 ; ip++ ){
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cout << endl << "******* par " << ip << " *******" << endl << endl;
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pd = pars[ip];
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for( i=0 ;i<npar ; i++ ){tmpp[i] = pars0[i]; }
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i=2; dl = 0;
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while( dl < delchi ){
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pu = pars[ip] + sign*i*errs0[ip];
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tmpp[ip] = pu;
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tmplike = opti( tmpp , errs , ip );
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dl = 2*(bestlike - tmplike);
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i++;
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}
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while( fabs(dl-delchi)>tol ){
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phalf = (pu+pd)/2.0;
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tmpp[ip] = phalf;
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tmplike = opti( tmpp , errs , ip );
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dl = 2*(bestlike - tmplike);
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cout << "+++ " << bestlike << " " << tmplike << " " << pars[ip] << " "<< phalf << " " << dl << endl;
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if( dl<delchi ){ pd = phalf;}else{ pu = phalf;}
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}
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errs0[ip] = sign* ( phalf - pars0[ip] );
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}
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for( i=0 ; i<npar ; i++ ){ pars[i] = pars0[i]; errs[i] = errs0[i];}
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print_pars( pars0 , errs0 );
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//optimize( pars0 , errs );
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}
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};
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#endif /* MOD_HPP_ */
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