Expectation and Maximization functions from M2MFstAligner

EandMfunctions.cpp

void M2MFstAligner::expectation( ){
  for( int i=0; i<fsas.size(); i++ ){
    //Comput Forward and Backward probabilities
    ShortestDistance( fsas.at(i), &alpha );
    ShortestDistance( fsas.at(i), &beta, true );

    //Compute the normalized Gamma probabilities and 
    // update our running tally
    for( StateIterator<VectorFst<LogArc> > siter(fsas.at(i)); !siter.Done(); siter.Next() ){
      LogArc::StateId q = siter.Value();
      for( ArcIterator<VectorFst<LogArc> > aiter(fsas.at(i),q); !aiter.Done(); aiter.Next() ){
        const LogArc&      arc = aiter.Value();
        const LogWeight& gamma = Divide(Times(Times(alpha[q], arc.weight), beta[arc.nextstate]), beta[0]);
        //Check for any BadValue results, otherwise add to the tally.
        //We call this 'prev_alignment_model' which may seem misleading, but
        // this conventions leads to 'alignment_model' being the final version.
        if( gamma.Value()==gamma.Value() ){
          prev_alignment_model[arc.ilabel] = Plus(prev_alignment_model[arc.ilabel], gamma);
          total = Plus(total, gamma);
        }
      }
    }
    alpha.clear();
    beta.clear();
  }
}



float M2MFstAligner::maximization( bool lastiter ){
  //Maximization. Simple count normalization.  Probably get an improvement 
  // by using a more sophisticated regularization approach. 
  map<LogArc::Label,LogWeight>::iterator it;
  float change = abs(total.Value()-prevTotal.Value());
  //cout << "Total: " << total << " Change: " << abs(total.Value()-prevTotal.Value()) << endl;
  prevTotal = total;

  //Normalize and iterate to the next model.  We apply it dynamically 
  // during the expectation step.
  for( it=prev_alignment_model.begin(); it != prev_alignment_model.end(); it++ ){
    alignment_model[(*it).first] = Divide((*it).second,total);
    (*it).second = LogWeight::Zero();
  }

  for( int i=0; i<fsas.size(); i++ ){
    for( StateIterator<VectorFst<LogArc> > siter(fsas[i]); !siter.Done(); siter.Next() ){
      LogArc::StateId q = siter.Value();
      for( MutableArcIterator<VectorFst<LogArc> > aiter(&fsas[i], q); !aiter.Done(); aiter.Next() ){
        LogArc arc = aiter.Value();
        arc.weight = alignment_model[arc.ilabel];
        aiter.SetValue(arc);
      }
    }
  }

  total = LogWeight::Zero();
  return change;
}