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Diffstat (limited to 'mgizapp/src/ForwardBackward.cpp')
| -rw-r--r-- | mgizapp/src/ForwardBackward.cpp | 242 |
1 files changed, 242 insertions, 0 deletions
diff --git a/mgizapp/src/ForwardBackward.cpp b/mgizapp/src/ForwardBackward.cpp new file mode 100644 index 0000000..969316a --- /dev/null +++ b/mgizapp/src/ForwardBackward.cpp @@ -0,0 +1,242 @@ +/* + +Copyright (C) 1999,2000,2001 Franz Josef Och (RWTH Aachen - Lehrstuhl fuer Informatik VI) + +This file is part of GIZA++ ( extension of GIZA ). + +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; 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. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, +USA. + +*/ +#ifndef NO_TRAINING +#include "ForwardBackward.h" +#include "Globals.h" +#include "myassert.h" +#include "HMMTables.h" +#include "mymath.h" + + +double ForwardBackwardTraining(const HMMNetwork&net,Array<double>&g,Array<Array2<double> >&E){ + const int I=net.size1(),J=net.size2(),N=I*J; + Array<double> alpha(N,0),beta(N,0),sum(J); + for(int i=0;i<I;i++) + beta[N-I+i]=net.getBetainit(i); + double * cur_beta=conv<double>(beta.begin())+N-I-1; + for(int j=J-2;j>=0;--j) + for(int ti=I-1;ti>=0;--ti,--cur_beta) { + const double *next_beta=conv<double>(beta.begin())+(j+1)*I; + const double *alprob=&net.outProb(j,ti,0),*next_node=&net.nodeProb(0,j+1); + for(int ni=0;ni<I;++ni,(next_node+=J)){ + massert(cur_beta<next_beta&& &net.outProb(j,ti,ni)==alprob); + massert(next_node == &net.nodeProb(ni,j+1)); + /* if( VERB&&(*next_beta)*(*alprob)*(*next_node) ) + cout << "B= " << (int)(cur_beta-beta.begin()) << " += " << (*next_beta) << "(" + << next_beta-beta.begin() << ") alprob:" << (*alprob) << " lexprob:" << (*next_node) << endl;*/ + (*cur_beta)+=(*next_beta++)*(*alprob++)*(*next_node); + } + } + for(int i=0;i<I;i++) + alpha[i]=net.getAlphainit(i)*net.nodeProb(i,0); + double* cur_alpha=conv<double>(alpha.begin())+I; + cur_beta=conv<double>(beta.begin())+I; + for(int j=1;j<J;j++){ + Array2<double>&e=E[ (E.size()==1)?0:(j-1) ]; + if( (E.size()!=1) || j==1 ) + { + e.resize(I,I); + fill(e.begin(),e.end(),0.0); + } + + for(int ti=0;ti<I;++ti,++cur_alpha,++cur_beta) { + const double * prev_alpha=conv<double>(alpha.begin())+I*(j-1); + double *cur_e= &e(ti,0); + double this_node=net.nodeProb(ti,j); + const double* alprob= &net.outProb(j-1,0,ti); + for(int pi=0;pi<I;++pi,++prev_alpha,(alprob+=I)){ + massert(prev_alpha<cur_alpha&& &net.outProb(j-1,pi,ti)==alprob); + massert(&e(ti,pi)==cur_e); + const double alpha_increment= *prev_alpha*(*alprob)*this_node; + (*cur_alpha)+=alpha_increment; + (*cur_e++)+=alpha_increment*(*cur_beta); + } + } + } + g.resize(N); + transform(alpha.begin(),alpha.end(),beta.begin(),g.begin(),multiplies<double>()); + double bsum=0,esum=0,esum2; + for(int i=0;i<I;i++) + bsum+=beta[i]*net.nodeProb(i,0)*net.getAlphainit(i); + for(unsigned int j=0;j<(unsigned int)E.size();j++) + { + Array2<double>&e=E[j]; + const double *epe=e.end(); + for(const double*ep=e.begin();ep!=epe;++ep) + esum+=*ep; + } + if( J>1 ) + esum2=esum/(J-1); + else + esum2=0.0; + if(!(esum2==0.0||mfabs(esum2-bsum)/bsum<1e-3*I)) + cout << "ERROR2: " << esum2 <<" " <<bsum << " " << esum << net << endl; + double * sumptr=conv<double>(sum.begin()); + double* ge=conv<double>(g.end()); + for(double* gp=conv<double>(g.begin());gp!=ge;gp+=I) + { + *sumptr++=normalize_if_possible(gp,gp+I); + if(bsum && !(mfabs((*(sumptr-1)-bsum)/bsum)<1e-3*I)) + cout << "ERROR: " << *(sumptr-1) << " " << bsum << " " << mfabs((*(sumptr-1)-bsum)/bsum) << ' ' << I << ' ' << J << endl; + } + for(unsigned int j=0;j<(unsigned int)E.size();j++) + { + Array2<double>&e=E[j]; + double* epe=e.end(); + if( esum ) + for(double*ep=e.begin();ep!=epe;++ep) + *ep/=esum; + else + for(double*ep=e.begin();ep!=epe;++ep) + *ep/=1.0/(max(I*I,I*I*(J-1))); + } + if( sum.size() ) + return sum[0]; + else + return 1.0; +} +void HMMViterbi(const HMMNetwork&net,Array<int>&vit) { + const int I=net.size1(),J=net.size2(); + vit.resize(J); + Array<double>g; + Array<Array2<double> >e(1); + ForwardBackwardTraining(net,g,e); + for(int j=0;j<J;j++) { + double * begin=conv<double>(g.begin())+I*j; + vit[j]=max_element(begin,begin+I)-begin; + } +} +void HMMViterbi(const HMMNetwork&net,Array<double>&g,Array<int>&vit) { + const int I=net.size1(),J=net.size2(); + vit.resize(J); + for(int j=0;j<J;j++) { + double* begin=conv<double>(g.begin())+I*j; + vit[j]=max_element(begin,begin+I)-begin; + } +} + +double HMMRealViterbi(const HMMNetwork&net,Array<int>&vitar,int pegi,int pegj,bool verbose){ + const int I=net.size1(),J=net.size2(),N=I*J; + Array<double> alpha(N,-1); + Array<double*> bp(N,(double*)0); + vitar.resize(J); + if( J==0 ) + return 1.0; + for(int i=0;i<I;i++) + { + alpha[i]=net.getAlphainit(i)*net.nodeProb(i,0); + if( i>I/2 ) + alpha[i]=0; // only first empty word can be chosen + bp[i]=0; + } + double *cur_alpha=conv<double>(alpha.begin())+I; + double **cur_bp=conv<double*>(bp.begin())+I; + for(int j=1;j<J;j++) + { + if( pegj+1==j) + for(int ti=0;ti<I;ti++) + if( (pegi!=-1&&ti!=pegi)||(pegi==-1&&ti<I/2) ) + (cur_alpha-I)[ti]=0.0; + for(int ti=0;ti<I;++ti,++cur_alpha,++cur_bp) { + double* prev_alpha=conv<double>(alpha.begin())+I*(j-1); + double this_node=net.nodeProb(ti,j); + const double *alprob= &net.outProb(j-1,0,ti); + for(int pi=0;pi<I;++pi,++prev_alpha,(alprob+=I)){ + massert(prev_alpha<cur_alpha&& &net.outProb(j-1,pi,ti)==alprob); + const double alpha_increment= *prev_alpha*(*alprob)*this_node; + if( alpha_increment> *cur_alpha ) + { + (*cur_alpha)=alpha_increment; + (*cur_bp)=prev_alpha; + } + } + } + } + for(int i=0;i<I;i++) + alpha[N-I+i]*=net.getBetainit(i); + if( pegj==J-1) + for(int ti=0;ti<I;ti++) + if( (pegi!=-1&&ti!=pegi)||(pegi==-1&&ti<I/2) ) + (alpha)[N-I+ti]=0.0; + + int j=J-1; + cur_alpha=conv<double>(alpha.begin())+j*I; + vitar[J-1]=max_element(cur_alpha,cur_alpha+I)-cur_alpha; + double ret= *max_element(cur_alpha,cur_alpha+I); + while(bp[vitar[j]+j*I]) + { + cur_alpha-=I; + vitar[j-1]=bp[vitar[j]+j*I]-cur_alpha; + massert(vitar[j-1]<I&&vitar[j-1]>=0); + j--; + } + massert(j==0); + if( verbose ) + { + cout << "VERB:PEG: " << pegi << ' ' << pegj << endl; + for(int j=0;j<J;j++) + cout << "NP " << net.nodeProb(vitar[j],j) << ' ' << "AP " << ((j==0)?net.getAlphainit(vitar[j]):net.outProb(j-1,vitar[j-1],vitar[j])) << " j:" << j << " i:" << vitar[j] << "; "; + cout << endl; + } + return ret; +} + +double MaximumTraining(const HMMNetwork&net,Array<double>&g,Array<Array2<double> >&E){ + Array<int> vitar; + double ret=HMMRealViterbi(net,vitar); + const int I=net.size1(),J=net.size2(); + if( E.size()==1 ) + { + Array2<double>&e=E[0]; + e.resize(I,I); + g.resize(I*J); + fill(g.begin(),g.end(),0.0); + fill(e.begin(),e.end(),0.0); + for(int i=0;i<J;++i) + { + g[i*I+vitar[i]]=1.0; + if( i>0 ) + e(vitar[i],vitar[i-1])++; + } + } + else + { + g.resize(I*J); + fill(g.begin(),g.end(),0.0); + for(int i=0;i<J;++i) + { + g[i*I+vitar[i]]=1.0; + if( i>0 ) + { + Array2<double>&e=E[i-1]; + e.resize(I,I); + fill(e.begin(),e.end(),0.0); + e(vitar[i],vitar[i-1])++; + } + } + } + return ret; +} + +#endif + |