mosesdecoder/moses/TranslationModel/PhraseDictionaryMultiModelCounts.cpp

/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.

This library 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
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
***********************************************************************/
#include "util/exception.hh"

#include "moses/TranslationModel/PhraseDictionaryMultiModelCounts.h"

#define LINE_MAX_LENGTH 100000
#include "phrase-extract/SafeGetline.h" // for SAFE_GETLINE()

using namespace std;

// from phrase-extract/tables-core.cpp
vector<string> tokenize( const char* input )
{
  vector< string > token;
  bool betweenWords = true;
  int start=0;
  int i=0;
  for(; input[i] != '\0'; i++) {
    bool isSpace = (input[i] == ' ' || input[i] == '\t');

    if (!isSpace && betweenWords) {
      start = i;
      betweenWords = false;
    } else if (isSpace && !betweenWords) {
      token.push_back( string( input+start, i-start ) );
      betweenWords = true;
    }
  }
  if (!betweenWords)
    token.push_back( string( input+start, i-start ) );
  return token;
}

namespace Moses
{

PhraseDictionaryMultiModelCounts::PhraseDictionaryMultiModelCounts(const std::string &line)
:PhraseDictionaryMultiModel("PhraseDictionaryMultiModel", line)
{
    m_mode = "instance_weighting"; //TODO: set this in config; use m_mode to switch between interpolation and instance weighting
    m_combineFunction = InstanceWeighting;
    //m_mode = "interpolate";
    //m_combineFunction = LinearInterpolationFromCounts;

    for (size_t i = 0; i < m_args.size(); ++i) {
      const vector<string> &args = m_args[i];
      if (args[0] == "mode") {
        m_mode =args[1];
        if (m_mode == "instance_weighting")
          m_combineFunction = InstanceWeighting;
        else if (m_mode == "interpolate")
          m_combineFunction = LinearInterpolationFromCounts;
        else {
          ostringstream msg;
          msg << "combination mode unknown: " << m_mode;
          throw runtime_error(msg.str());
        }

      }
    } // for

}

PhraseDictionaryMultiModelCounts::~PhraseDictionaryMultiModelCounts()
{
    RemoveAllInColl(m_lexTable_e2f);
    RemoveAllInColl(m_lexTable_f2e);
    RemoveAllInColl(m_pd);
    RemoveAllInColl(m_inverse_pd);
}

bool PhraseDictionaryMultiModelCounts::InitDictionary(const vector<FactorType> &input
                                  , const vector<FactorType> &output
                                  , const vector<string> &config
                                  , const vector<float> &weight
                                  , size_t tableLimit
                                  , size_t numInputScores
                                  , const LMList &languageModels
                                  , float weightWP)
{
  /*

  for(size_t i = 0; i < m_numModels; ++i){

      string impl, file, main_table, target_table, lex_e2f, lex_f2e;

      string delim = ":";
      size_t delim_pos = files[i].find(delim);
      UTIL_THROW_IF(delim_pos >= files[i].size(), util::Exception, "Phrase table must be specified in this format: Implementation:Path");

      impl = files[i].substr(0,delim_pos);
      file = files[i].substr(delim_pos+1,files[i].size());
      main_table = file + "/count-table";
      target_table = file + "/count-table-target";
      lex_e2f = file + "/lex.counts.e2f";
      lex_f2e = file + "/lex.counts.f2e";
      size_t componentTableLimit = 0; // using 0, because we can't trust implemented pruning algorithms with count tables.

      PhraseTableImplementation implementation = (PhraseTableImplementation) Scan<int>(impl);

      //how many actual scores there are in the phrase tables
      size_t numScoresCounts = 3;
      size_t numScoresTargetCounts = 1;
      if (implementation == Memory) {

            if (!FileExists(main_table) && FileExists(main_table + ".gz")) main_table += ".gz";
            if (!FileExists(target_table) && FileExists(target_table + ".gz")) target_table += ".gz";

            PhraseDictionaryMemory* pdm = new PhraseDictionaryMemory(m_numScoreComponent, m_feature_load);
            pdm->SetNumScoreComponentMultiModel(numScoresCounts); //instead of complaining about inequal number of scores, silently fill up the score vector with zeroes
            pdm->Load( input, output, main_table, m_weight, componentTableLimit, languageModels, m_weightWP);
            m_pd.push_back(pdm);

            PhraseDictionaryMemory* pdm_inverse = new PhraseDictionaryMemory(m_numScoreComponent, m_feature_load);
            pdm_inverse->SetNumScoreComponentMultiModel(numScoresTargetCounts);
            pdm_inverse->Load( input, output, target_table, m_weight, componentTableLimit, languageModels, m_weightWP);
            m_inverse_pd.push_back(pdm_inverse);
      } else if (implementation == Binary) {
            PhraseDictionaryTreeAdaptor* pdta = new PhraseDictionaryTreeAdaptor(m_numScoreComponent, numInputScores , m_feature_load);
            pdta->SetNumScoreComponentMultiModel(m_numScoreComponent); //for binary models, we need to pass number of log-linear components to correctly resize the score vector
            pdta->Load(input, output, main_table, m_weight, m_componentTableLimit, languageModels, m_weightWP);
            m_pd.push_back(pdta);

            PhraseDictionaryTreeAdaptor* pdta_inverse = new PhraseDictionaryTreeAdaptor(m_numScoreComponent, numInputScores , m_feature_load);
            pdta_inverse->SetNumScoreComponentMultiModel(m_numScoreComponent);
            pdta_inverse->Load(input, output, target_table, m_weight, m_componentTableLimit, languageModels, m_weightWP);
            m_inverse_pd.push_back(pdta_inverse);
      } else if (implementation == Compact) { 
#ifndef WIN32
            PhraseDictionaryCompact* pdc = new PhraseDictionaryCompact(m_numScoreComponent, implementation, m_feature_load);
            pdc->SetNumScoreComponentMultiModel(m_numScoreComponent); //for compact models, we need to pass number of log-linear components to correctly resize the score vector
            pdc->Load( input, output, main_table, m_weight, componentTableLimit, languageModels, m_weightWP);
            m_pd.push_back(pdc);

            PhraseDictionaryCompact* pdc_inverse = new PhraseDictionaryCompact(m_numScoreComponent, implementation, m_feature_load);
            pdc_inverse->SetNumScoreComponentMultiModel(m_numScoreComponent);
            pdc_inverse->Load( input, output, target_table, m_weight, componentTableLimit, languageModels, m_weightWP);
            m_inverse_pd.push_back(pdc_inverse);
#else
            UTIL_THROW(util::Exception, "Compact phrase table not supported in windows"); 
#endif
      }
      else {
        UTIL_THROW(util::Exception,"PhraseDictionaryMultiModel does not support phrase table type " << implementation);
      }

      lexicalTable* e2f = new lexicalTable;
      LoadLexicalTable(lex_e2f, e2f);
      lexicalTable* f2e = new lexicalTable;
      LoadLexicalTable(lex_f2e, f2e);

      m_lexTable_e2f.push_back(e2f);
      m_lexTable_f2e.push_back(f2e);

  }

*/

  return true;
}


const TargetPhraseCollection *PhraseDictionaryMultiModelCounts::GetTargetPhraseCollection(const Phrase& src) const
{

  vector<vector<float> > multimodelweights;
  bool normalize;
  normalize = (m_mode == "interpolate") ? true : false;
  multimodelweights = getWeights(4,normalize);

  //source phrase frequency is shared among all phrase pairs
  vector<float> fs(m_numModels);

  map<string,multiModelCountsStatistics*>* allStats = new(map<string,multiModelCountsStatistics*>);

  CollectSufficientStatistics(src, fs, allStats);

  TargetPhraseCollection *ret = CreateTargetPhraseCollectionCounts(src, fs, allStats, multimodelweights);

  ret->NthElement(m_tableLimit); // sort the phrases for pruning later
  const_cast<PhraseDictionaryMultiModelCounts*>(this)->CacheForCleanup(ret);
  return ret;
}


void PhraseDictionaryMultiModelCounts::CollectSufficientStatistics(const Phrase& src, vector<float> &fs, map<string,multiModelCountsStatistics*>* allStats) const
//fill fs and allStats with statistics from models
{
  for(size_t i = 0; i < m_numModels; ++i){

    TargetPhraseCollection *ret_raw = (TargetPhraseCollection*)  m_pd[i]->GetTargetPhraseCollection( src);
    if (ret_raw != NULL) {

      TargetPhraseCollection::iterator iterTargetPhrase;
      for (iterTargetPhrase = ret_raw->begin(); iterTargetPhrase != ret_raw->end();  ++iterTargetPhrase) {

        TargetPhrase * targetPhrase = *iterTargetPhrase;
        vector<float> raw_scores = targetPhrase->GetScoreBreakdown().GetScoresForProducer(this);

        string targetString = targetPhrase->GetStringRep(m_output);
        if (allStats->find(targetString) == allStats->end()) {

          multiModelCountsStatistics * statistics = new multiModelCountsStatistics;
          statistics->targetPhrase = new TargetPhrase(*targetPhrase); //make a copy so that we don't overwrite the original phrase table info

          statistics->fst.resize(m_numModels);
          statistics->ft.resize(m_numModels);
          Scores scoreVector(5);
          scoreVector[0] = -raw_scores[0];
          scoreVector[1] = -raw_scores[1];
          scoreVector[2] = -raw_scores[2];
          statistics->targetPhrase->GetScoreBreakdown().Assign(this, scoreVector); // set scores to 0

          (*allStats)[targetString] = statistics;

        }
        multiModelCountsStatistics * statistics = (*allStats)[targetString];

        statistics->fst[i] = UntransformScore(raw_scores[0]);
        statistics->ft[i] = UntransformScore(raw_scores[1]);
        fs[i] = UntransformScore(raw_scores[2]);
        (*allStats)[targetString] = statistics;
      }
    }
  }

  // get target phrase frequency for models which have not seen the phrase pair
  for ( map< string, multiModelCountsStatistics*>::const_iterator iter = allStats->begin(); iter != allStats->end(); ++iter ) {
    multiModelCountsStatistics * statistics = iter->second;

    for (size_t i = 0; i < m_numModels; ++i) {
        if (!statistics->ft[i]) {
            statistics->ft[i] = GetTargetCount(static_cast<const Phrase&>(*statistics->targetPhrase), i);
        }
    }
  }
}


TargetPhraseCollection* PhraseDictionaryMultiModelCounts::CreateTargetPhraseCollectionCounts(const Phrase &src, vector<float> &fs, map<string,multiModelCountsStatistics*>* allStats, vector<vector<float> > &multimodelweights) const
{
  TargetPhraseCollection *ret = new TargetPhraseCollection();
  for ( map< string, multiModelCountsStatistics*>::const_iterator iter = allStats->begin(); iter != allStats->end(); ++iter ) {

    multiModelCountsStatistics * statistics = iter->second;

    if (statistics->targetPhrase->GetAlignTerm().GetSize() == 0) {
        UTIL_THROW(util::Exception, " alignment information empty\ncount-tables need to include alignment information for computation of lexical weights.\nUse --phrase-word-alignment during training; for on-disk tables, also set -alignment-info when creating on-disk tables."); 
    }

    try {
        pair<vector< set<size_t> >, vector< set<size_t> > > alignment = GetAlignmentsForLexWeights(src, static_cast<const Phrase&>(*statistics->targetPhrase), statistics->targetPhrase->GetAlignTerm());
        vector< set<size_t> > alignedToT = alignment.first;
        vector< set<size_t> > alignedToS = alignment.second;
        double lexst = ComputeWeightedLexicalTranslation(static_cast<const Phrase&>(*statistics->targetPhrase), src, alignedToS, m_lexTable_e2f, multimodelweights[1], m_output, m_input );
        double lexts = ComputeWeightedLexicalTranslation(src, static_cast<const Phrase&>(*statistics->targetPhrase), alignedToT, m_lexTable_f2e, multimodelweights[3], m_input, m_output );

        Scores scoreVector(5);
        scoreVector[0] = FloorScore(TransformScore(m_combineFunction(statistics->fst, statistics->ft, multimodelweights[0])));
        scoreVector[1] = FloorScore(TransformScore(lexst));
        scoreVector[2] = FloorScore(TransformScore(m_combineFunction(statistics->fst, fs, multimodelweights[2])));
        scoreVector[3] = FloorScore(TransformScore(lexts));
        scoreVector[4] = FloorScore(TransformScore(2.718));

        statistics->targetPhrase->GetScoreBreakdown().Assign(this, scoreVector);
    }
    catch (AlignmentException& e) {
        continue;
    }

    ret->Add(new TargetPhrase(*statistics->targetPhrase));
  }

  RemoveAllInMap(*allStats);
  delete allStats;
  return ret;
}


float PhraseDictionaryMultiModelCounts::GetTargetCount(const Phrase &target, size_t modelIndex) const {

    TargetPhraseCollection *ret_raw = (TargetPhraseCollection*)  m_inverse_pd[modelIndex]->GetTargetPhraseCollection(target);

    // in inverse mode, we want the first score of the first phrase pair (note: if we were to work with truly symmetric models, it would be the third score)
    if (ret_raw != NULL) {
        TargetPhrase * targetPhrase = *(ret_raw->begin());
        return UntransformScore(targetPhrase->GetScoreBreakdown().GetScoresForProducer(this)[0]);
    }

    // target phrase unknown
    else return 0;
}


pair<PhraseDictionaryMultiModelCounts::AlignVector,PhraseDictionaryMultiModelCounts::AlignVector> PhraseDictionaryMultiModelCounts::GetAlignmentsForLexWeights(const Phrase &phraseS, const Phrase &phraseT, const AlignmentInfo &alignment) const {

    size_t tsize = phraseT.GetSize();
    size_t ssize = phraseS.GetSize();
    AlignVector alignedToT (tsize);
    AlignVector alignedToS (ssize);
    AlignmentInfo::const_iterator iter;

    for (iter = alignment.begin(); iter != alignment.end(); ++iter) {
    const pair<size_t,size_t> &alignPair = *iter;
        size_t s = alignPair.first;
        size_t t = alignPair.second;
        if (s >= ssize || t >= tsize) {
            cerr << "Error: inconsistent alignment for phrase pair: " << phraseS << " - " << phraseT << endl;
            cerr << "phrase pair will be discarded" << endl;
            throw AlignmentException();
        }
        alignedToT[t].insert( s );
        alignedToS[s].insert( t );
  }
  return make_pair(alignedToT,alignedToS);
}


double PhraseDictionaryMultiModelCounts::ComputeWeightedLexicalTranslation( const Phrase &phraseS, const Phrase &phraseT, AlignVector &alignment, const vector<lexicalTable*> &tables, vector<float> &multimodelweights, const vector<FactorType> &input_factors, const vector<FactorType> &output_factors ) const {
  // lexical translation probability

  double lexScore = 1.0;
  string null = "NULL";

  // all target words have to be explained
  for(size_t ti=0; ti<alignment.size(); ti++) {
    const set< size_t > & srcIndices = alignment[ ti ];
    Word t_word = phraseT.GetWord(ti);
    string ti_str = t_word.GetString(output_factors, false);
    if (srcIndices.empty()) {
      // explain unaligned word by NULL
      lexScore *= GetLexicalProbability( null, ti_str, tables, multimodelweights );
    } else {
      // go through all the aligned words to compute average
      double thisWordScore = 0;
      for (set< size_t >::const_iterator si(srcIndices.begin()); si != srcIndices.end(); ++si) {
        string s_str = phraseS.GetWord(*si).GetString(input_factors, false);
        thisWordScore += GetLexicalProbability( s_str, ti_str, tables, multimodelweights );
      }
      lexScore *= thisWordScore / srcIndices.size();
    }
  }
  return lexScore;
}


lexicalCache PhraseDictionaryMultiModelCounts::CacheLexicalStatistics( const Phrase &phraseS, const Phrase &phraseT, AlignVector &alignment, const vector<lexicalTable*> &tables, const vector<FactorType> &input_factors, const vector<FactorType> &output_factors ) {
//do all the necessary lexical table lookups and get counts, but don't apply weights yet

  string null = "NULL";
  lexicalCache ret;

  // all target words have to be explained
  for(size_t ti=0; ti<alignment.size(); ti++) {
    const set< size_t > & srcIndices = alignment[ ti ];
    Word t_word = phraseT.GetWord(ti);
    string ti_str = t_word.GetString(output_factors, false);

    vector<lexicalPair> ti_vector;
    if (srcIndices.empty()) {
      // explain unaligned word by NULL
      vector<float> joint_count (m_numModels);
      vector<float> marginals (m_numModels);

      FillLexicalCountsJoint(null, ti_str, joint_count, tables);
      FillLexicalCountsMarginal(null, marginals, tables);

      ti_vector.push_back(make_pair(joint_count, marginals));

    } else {
      for (set< size_t >::const_iterator si(srcIndices.begin()); si != srcIndices.end(); ++si) {
        string s_str = phraseS.GetWord(*si).GetString(input_factors, false);
        vector<float> joint_count (m_numModels);
        vector<float> marginals (m_numModels);

        FillLexicalCountsJoint(s_str, ti_str, joint_count, tables);
        FillLexicalCountsMarginal(s_str, marginals, tables);

        ti_vector.push_back(make_pair(joint_count, marginals));
      }
    }
    ret.push_back(ti_vector);
  }
  return ret;
}


double PhraseDictionaryMultiModelCounts::ComputeWeightedLexicalTranslationFromCache( lexicalCache &cache, vector<float> &weights ) const {
  // lexical translation probability

  double lexScore = 1.0;

  for (lexicalCache::const_iterator iter = cache.begin();  iter != cache.end(); ++iter) {
      vector<lexicalPair> t_vector = *iter;
      double thisWordScore = 0;
      for ( vector<lexicalPair>::const_iterator iter2 = t_vector.begin();  iter2 != t_vector.end(); ++iter2) {
          vector<float> joint_count = iter2->first;
          vector<float> marginal = iter2->second;
          thisWordScore += m_combineFunction(joint_count, marginal, weights);
      }
      lexScore *= thisWordScore / t_vector.size();
  }
  return lexScore;
}

// get lexical probability for single word alignment pair
double PhraseDictionaryMultiModelCounts::GetLexicalProbability( string &wordS, string &wordT, const vector<lexicalTable*> &tables, vector<float> &multimodelweights ) const {
    vector<float> joint_count (m_numModels);
    vector<float> marginals (m_numModels);

    FillLexicalCountsJoint(wordS, wordT, joint_count, tables);
    FillLexicalCountsMarginal(wordS, marginals, tables);

    double lexProb = m_combineFunction(joint_count, marginals, multimodelweights);

  return lexProb;
}


void PhraseDictionaryMultiModelCounts::FillLexicalCountsJoint(string &wordS, string &wordT, vector<float> &count, const vector<lexicalTable*> &tables) const {
    for (size_t i=0;i < m_numModels;i++) {
        lexicalMapJoint::iterator joint_s = tables[i]->joint.find( wordS );
        if (joint_s == tables[i]->joint.end()) count[i] = 0.0;
        else {
            lexicalMap::iterator joint_t = joint_s->second.find( wordT );
            if (joint_t == joint_s->second.end()) count[i] = 0.0;
            else count[i] = joint_t->second;
        }
    }
}

void PhraseDictionaryMultiModelCounts::FillLexicalCountsMarginal(string &wordS, vector<float> &count, const vector<lexicalTable*> &tables) const {
    for (size_t i=0;i < m_numModels;i++) {
        lexicalMap::iterator marginal_s = tables[i]->marginal.find( wordS );
        if (marginal_s == tables[i]->marginal.end()) count[i] = 0.0;
        else count[i] = marginal_s->second;
    }
}


void PhraseDictionaryMultiModelCounts::LoadLexicalTable( string &fileName, lexicalTable* ltable) {

  cerr << "Loading lexical translation table from " << fileName;
  ifstream inFile;
  inFile.open(fileName.c_str());
  if (inFile.fail()) {
    cerr << " - ERROR: could not open file\n";
    exit(1);
  }
  istream *inFileP = &inFile;

  char line[LINE_MAX_LENGTH];

  int i=0;
  while(true) {
    i++;
    if (i%100000 == 0) cerr << "." << flush;
    SAFE_GETLINE((*inFileP), line, LINE_MAX_LENGTH, '\n', __FILE__);
    if (inFileP->eof()) break;

    vector<string> token = tokenize( line );
    if (token.size() != 4) {
      cerr << "line " << i << " in " << fileName
           << " has wrong number of tokens, skipping:\n"
           << token.size() << " " << token[0] << " " << line << endl;
      continue;
    }

    double joint = atof( token[2].c_str() );
    double marginal = atof( token[3].c_str() );
    string wordT = token[0];
    string wordS = token[1];
    ltable->joint[ wordS ][ wordT ] = joint;
    ltable->marginal[ wordS ] = marginal;
  }
  cerr << endl;

}


#ifdef WITH_DLIB
vector<float> PhraseDictionaryMultiModelCounts::MinimizePerplexity(vector<pair<string, string> > &phrase_pair_vector) {

    const StaticData &staticData = StaticData::Instance();
    const string& factorDelimiter = staticData.GetFactorDelimiter();

    map<pair<string, string>, size_t> phrase_pair_map;

    for ( vector<pair<string, string> >::const_iterator iter = phrase_pair_vector.begin(); iter != phrase_pair_vector.end(); ++iter ) {
        phrase_pair_map[*iter] += 1;
    }

    vector<multiModelCountsStatisticsOptimization*> optimizerStats;

    for ( map<pair<string, string>, size_t>::iterator iter = phrase_pair_map.begin(); iter != phrase_pair_map.end(); ++iter ) {

        pair<string, string> phrase_pair = iter->first;
        string source_string = phrase_pair.first;
        string target_string = phrase_pair.second;

        vector<float> fs(m_numModels);
        map<string,multiModelCountsStatistics*>* allStats = new(map<string,multiModelCountsStatistics*>);

        Phrase sourcePhrase(0);
        sourcePhrase.CreateFromString(m_input, source_string, factorDelimiter);

        CollectSufficientStatistics(sourcePhrase, fs, allStats); //optimization potential: only call this once per source phrase

        //phrase pair not found; leave cache empty
        if (allStats->find(target_string) == allStats->end()) {
            RemoveAllInMap(*allStats);
            delete allStats;
            continue;
        }

        multiModelCountsStatisticsOptimization * targetStatistics = new multiModelCountsStatisticsOptimization();
        targetStatistics->targetPhrase = new TargetPhrase(*(*allStats)[target_string]->targetPhrase);
        targetStatistics->fs = fs;
        targetStatistics->fst = (*allStats)[target_string]->fst;
        targetStatistics->ft = (*allStats)[target_string]->ft;
        targetStatistics->f = iter->second;

        try {
            pair<vector< set<size_t> >, vector< set<size_t> > > alignment = GetAlignmentsForLexWeights(sourcePhrase, static_cast<const Phrase&>(*targetStatistics->targetPhrase), targetStatistics->targetPhrase->GetAlignTerm());
            targetStatistics->lexCachee2f = CacheLexicalStatistics(static_cast<const Phrase&>(*targetStatistics->targetPhrase), sourcePhrase, alignment.second, m_lexTable_e2f, m_output, m_input );
            targetStatistics->lexCachef2e = CacheLexicalStatistics(sourcePhrase, static_cast<const Phrase&>(*targetStatistics->targetPhrase), alignment.first, m_lexTable_f2e, m_input, m_output );

            optimizerStats.push_back(targetStatistics);
        }
        catch (AlignmentException& e) {}

        RemoveAllInMap(*allStats);
        delete allStats;
    }

    Sentence sentence;
    CleanUp(sentence); // free memory used by compact phrase tables

    vector<float> ret (m_numModels*4);
    for (size_t iFeature=0; iFeature < 4; iFeature++) {

        CrossEntropyCounts * ObjectiveFunction = new CrossEntropyCounts(optimizerStats, this, iFeature);

        vector<float> weight_vector = Optimize(ObjectiveFunction, m_numModels);

        if (m_mode == "interpolate") {
            weight_vector = normalizeWeights(weight_vector);
        }
        else if (m_mode == "instance_weighting") {
            float first_value = weight_vector[0];
            for (size_t i=0; i < m_numModels; i++) {
                weight_vector[i] = weight_vector[i]/first_value;
            }
        }
        cerr << "Weight vector for feature " << iFeature << ": ";
        for (size_t i=0; i < m_numModels; i++) {
            ret[(iFeature*m_numModels)+i] = weight_vector[i];
            cerr << weight_vector[i] << " ";
        }
        cerr << endl;
        delete ObjectiveFunction;
    }

    RemoveAllInColl(optimizerStats);
    return ret;

}

double CrossEntropyCounts::operator() ( const dlib::matrix<double,0,1>& arg) const
{
    double total = 0.0;
    double n = 0.0;
    std::vector<float> weight_vector (m_model->m_numModels);

    for (int i=0; i < arg.nr(); i++) {
        weight_vector[i] = arg(i);
    }
    if (m_model->m_mode == "interpolate") {
        weight_vector = m_model->normalizeWeights(weight_vector);
    }

    for ( std::vector<multiModelCountsStatisticsOptimization*>::const_iterator iter = m_optimizerStats.begin(); iter != m_optimizerStats.end(); ++iter ) {
        multiModelCountsStatisticsOptimization* statistics = *iter;
        size_t f = statistics->f;

        double score;
        if (m_iFeature == 0) {
            score = m_model->m_combineFunction(statistics->fst, statistics->ft, weight_vector);
        }
        else if (m_iFeature == 1) {
            score = m_model->ComputeWeightedLexicalTranslationFromCache(statistics->lexCachee2f, weight_vector);
        }
        else if (m_iFeature == 2) {
            score = m_model->m_combineFunction(statistics->fst, statistics->fs, weight_vector);
        }
        else if (m_iFeature == 3) {
            score = m_model->ComputeWeightedLexicalTranslationFromCache(statistics->lexCachef2e, weight_vector);
        }
        else {
            score = 0;
            UTIL_THROW(util::Exception, "Trying to optimize feature that I don't know. Aborting");
        }
        total -= (FloorScore(TransformScore(score))/TransformScore(2))*f;
        n += f;
    }
    return total/n;
}

#endif

// calculate weighted probability based on instance weighting of joint counts and marginal counts
double InstanceWeighting(vector<float> &joint_counts, vector<float> &marginals, vector<float> &multimodelweights) {

    double joint_counts_weighted =  inner_product(joint_counts.begin(), joint_counts.end(), multimodelweights.begin(), 0.0);
    double marginals_weighted = inner_product(marginals.begin(), marginals.end(), multimodelweights.begin(), 0.0);

    if (marginals_weighted == 0) {
        return 0;
    }
    else {
        return joint_counts_weighted/marginals_weighted;
    }
}


// calculate linear interpolation of relative frequency estimates based on joint count and marginal counts
//unused for now; enable in config?
double LinearInterpolationFromCounts(vector<float> &joint_counts, vector<float> &marginals, vector<float> &multimodelweights) {

    vector<float> p(marginals.size());

    for (size_t i=0;i < marginals.size();i++) {
        if (marginals[i] != 0) {
            p[i] = joint_counts[i]/marginals[i];
        }
    }

    double p_weighted = inner_product(p.begin(), p.end(), multimodelweights.begin(), 0.0);

    return p_weighted;
}


} //namespace