port PhraseDictionaryMultiModel to new format

2 files changed, 0 insertions, 625 deletions

diff --git a/defer/PhraseDictionaryMultiModel.cpp b/defer/PhraseDictionaryMultiModel.cpp
deleted file mode 100644
index 5ddb6e2b2..000000000
--- a/defer/PhraseDictionaryMultiModel.cpp
+++ /dev/null
@@ -1,475 +0,0 @@
-/***********************************************************************
-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/PhraseDictionaryMultiModel.h"
-
-using namespace std;
-
-namespace Moses
-
-{
-PhraseDictionaryMultiModel::PhraseDictionaryMultiModel(size_t numScoreComponent,
-    PhraseDictionaryFeature* feature): PhraseDictionary(numScoreComponent, feature)
-{
-    m_feature_load = feature;
-}
-
-PhraseDictionaryMultiModel::~PhraseDictionaryMultiModel()
-{
-    RemoveAllInColl(m_pd);
-}
-
-bool PhraseDictionaryMultiModel::Load(const std::vector<FactorType> &input
-                                  , const std::vector<FactorType> &output
-                                  , const std::vector<std::string> &config
-                                  , const vector<float> &weight
-                                  , size_t tableLimit
-                                  , size_t numInputScores
-                                  , const LMList &languageModels
-                                  , float weightWP)
-{
-  m_languageModels = &languageModels;
-  m_weight = weight;
-  m_weightWP = weightWP;
-  m_input = input;
-  m_output = output;
-  m_tableLimit = tableLimit;
-
-  m_mode = config[4];
-  std::vector<std::string> files(config.begin()+5,config.end());
-
-  m_numModels = files.size();
-
-  // since the top X target phrases of the final model are not the same as the top X phrases of each component model,
-  // one could choose a higher value than tableLimit (or 0) here for maximal precision, at a cost of speed.
-  m_componentTableLimit = tableLimit;
-
-  //how many actual scores there are in the phrase tables
-  //so far, equal to number of log-linear scores, but it is allowed to be smaller (for other combination types)
-  size_t numPtScores = m_numScoreComponent;
-
-  if (m_mode != "interpolate") {
-    ostringstream msg;
-    msg << "combination mode unknown: " << m_mode;
-    throw runtime_error(msg.str());
-  }
-
-  for(size_t i = 0; i < m_numModels; ++i){
-
-      std::string impl, file, main_table;
-
-      std::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());
-
-      PhraseTableImplementation implementation = (PhraseTableImplementation) Scan<int>(impl);
-
-      if (implementation == Memory) {
-
-            if (!FileExists(file) && FileExists(file + ".gz")) file += ".gz";
-
-            PhraseDictionaryMemory* pdm = new PhraseDictionaryMemory(m_numScoreComponent, m_feature_load);
-            pdm->SetNumScoreComponentMultiModel(numPtScores); //instead of complaining about inequal number of scores, silently fill up the score vector with zeroes
-            pdm->Load( input, output, file, m_weight, m_componentTableLimit, languageModels, m_weightWP);
-            m_pd.push_back(pdm);
-      } else if (implementation == Binary) {
-            PhraseDictionaryTreeAdaptor* pdta = new PhraseDictionaryTreeAdaptor(m_numScoreComponent, numInputScores , m_feature_load);
-            pdta->Load(input, output, file, m_weight, m_componentTableLimit, languageModels, m_weightWP);
-            m_pd.push_back(pdta);
-      } 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, file, m_weight, m_componentTableLimit, languageModels, m_weightWP);
-            m_pd.push_back(pdc);
-#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);
-      }
-  }
-
-  return true;
-}
-
-
-const TargetPhraseCollection *PhraseDictionaryMultiModel::GetTargetPhraseCollection(const Phrase& src) const
-{
-
-  std::vector<std::vector<float> > multimodelweights;
-
-  if (m_mode == "interpolate") {
-    //interpolation of phrase penalty is skipped, and fixed-value (2.718) is used instead. results will be screwed up if phrase penalty is not last feature
-    size_t numWeights = m_numScoreComponent-1;
-    multimodelweights = getWeights(numWeights, true);
-  }
-
-  std::map<std::string,multiModelStatistics*>* allStats = new(std::map<std::string,multiModelStatistics*>);
-
-  CollectSufficientStatistics(src, allStats);
-
-  TargetPhraseCollection *ret = NULL;
-  if (m_mode == "interpolate") {
-    ret = CreateTargetPhraseCollectionLinearInterpolation(allStats, multimodelweights);
-  }
-
-  ret->NthElement(m_tableLimit); // sort the phrases for pruning later
-  const_cast<PhraseDictionaryMultiModel*>(this)->CacheForCleanup(ret);
-  RemoveAllInMap(*allStats);
-  delete allStats;
-
-  return ret;
-}
-
-
-void PhraseDictionaryMultiModel::CollectSufficientStatistics(const Phrase& src, std::map<std::string,multiModelStatistics*>* allStats) const
-{
-  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, iterLast;
-      if (m_componentTableLimit != 0 && ret_raw->GetSize() > m_componentTableLimit) {
-          iterLast = ret_raw->begin() + m_componentTableLimit;
-      }
-      else {
-          iterLast = ret_raw->end();
-      }
-
-      for (iterTargetPhrase = ret_raw->begin(); iterTargetPhrase != iterLast;  ++iterTargetPhrase) {
-        TargetPhrase * targetPhrase = *iterTargetPhrase;
-        std::vector<float> raw_scores = targetPhrase->GetScoreBreakdown().GetScoresForProducer(m_feature);
-
-        std::string targetString = targetPhrase->GetStringRep(m_output);
-        if (allStats->find(targetString) == allStats->end()) {
-
-          multiModelStatistics * statistics = new multiModelStatistics;
-          statistics->targetPhrase = new TargetPhrase(*targetPhrase); //make a copy so that we don't overwrite the original phrase table info
-
-          Scores scoreVector(m_numScoreComponent);
-          statistics->p.resize(m_numScoreComponent);
-          for(size_t j = 0; j < m_numScoreComponent; ++j){
-              statistics->p[j].resize(m_numModels);
-              scoreVector[j] = -raw_scores[j];
-          }
-
-          statistics->targetPhrase->SetScore(m_feature, scoreVector, ScoreComponentCollection(), m_weight, m_weightWP, *m_languageModels); // set scores to 0
-
-          (*allStats)[targetString] = statistics;
-
-        }
-        multiModelStatistics * statistics = (*allStats)[targetString];
-
-        for(size_t j = 0; j < m_numScoreComponent; ++j){
-            statistics->p[j][i] = UntransformScore(raw_scores[j]);
-        }
-
-        (*allStats)[targetString] = statistics;
-      }
-    }
-  }
-}
-
-
-TargetPhraseCollection* PhraseDictionaryMultiModel::CreateTargetPhraseCollectionLinearInterpolation(std::map<std::string,multiModelStatistics*>* allStats, std::vector<std::vector<float> > &multimodelweights) const
-{
-    TargetPhraseCollection *ret = new TargetPhraseCollection();
-    for ( std::map< std::string, multiModelStatistics*>::const_iterator iter = allStats->begin(); iter != allStats->end(); ++iter ) {
-
-        multiModelStatistics * statistics = iter->second;
-
-        Scores scoreVector(m_numScoreComponent);
-
-        for(size_t i = 0; i < m_numScoreComponent-1; ++i){
-            scoreVector[i] = TransformScore(std::inner_product(statistics->p[i].begin(), statistics->p[i].end(), multimodelweights[i].begin(), 0.0));
-        }
-
-        //assuming that last value is phrase penalty
-        scoreVector[m_numScoreComponent-1] = 1.0;
-
-        statistics->targetPhrase->SetScore(m_feature, scoreVector, ScoreComponentCollection(), m_weight, m_weightWP, *m_languageModels);
-        ret->Add(new TargetPhrase(*statistics->targetPhrase));
-    }
-    return ret;
-}
-
-
-//TODO: is it worth caching the results as long as weights don't change?
-std::vector<std::vector<float> > PhraseDictionaryMultiModel::getWeights(size_t numWeights, bool normalize) const
-{
-  const std::vector<float>* weights_ptr;
-  std::vector<float> raw_weights;
-  const StaticData &staticData = StaticData::Instance();
-
-  weights_ptr = staticData.GetTemporaryMultiModelWeightsVector();
-
-  //checking weights passed to mosesserver; only valid for this sentence; *don't* raise exception if client weights are malformed
-  if (weights_ptr == NULL || weights_ptr->size() == 0) {
-    weights_ptr = staticData.GetMultiModelWeightsVector(); //fall back to weights defined in config
-  }
-  else if(weights_ptr->size() != m_numModels && weights_ptr->size() != m_numModels * numWeights) {
-    //TODO: can we pass error message to client if weights are malformed?
-    std::stringstream strme;
-    strme << "Must have either one multimodel weight per model (" << m_numModels << "), or one per weighted feature and model (" << numWeights << "*" << m_numModels << "). You have " << weights_ptr->size() << ". Reverting to weights in config";
-    UserMessage::Add(strme.str());
-    weights_ptr = staticData.GetMultiModelWeightsVector(); //fall back to weights defined in config
-  }
-
-  //checking weights defined in config; only valid for this sentence; raise exception if config weights are malformed
-  if (weights_ptr == NULL || weights_ptr->size() == 0) {
-    for (size_t i=0;i < m_numModels;i++) {
-      raw_weights.push_back(1.0/m_numModels); //uniform weights created online
-    }
-  }
-  else if(weights_ptr->size() != m_numModels && weights_ptr->size() != m_numModels * numWeights) {
-    std::stringstream strme;
-    strme << "Must have either one multimodel weight per model (" << m_numModels << "), or one per weighted feature and model (" << numWeights << "*" << m_numModels << "). You have " << weights_ptr->size() << ".";
-    UTIL_THROW(util::Exception, strme.str());
-  }
-  else {
-      raw_weights = *weights_ptr;
-  }
-
-  std::vector<std::vector<float> > multimodelweights (numWeights);
-
-  for (size_t i=0;i < numWeights;i++) {
-    std::vector<float> weights_onefeature (m_numModels);
-    if(raw_weights.size() == m_numModels) {
-        weights_onefeature = raw_weights;
-    }
-    else {
-        copy ( raw_weights.begin()+i*m_numModels, raw_weights.begin()+(i+1)*m_numModels, weights_onefeature.begin() );
-    }
-    if(normalize) {
-         multimodelweights[i] = normalizeWeights(weights_onefeature);
-    }
-    else {
-        multimodelweights[i] = weights_onefeature;
-    }
-  }
-
-  return multimodelweights;
-}
-
-std::vector<float> PhraseDictionaryMultiModel::normalizeWeights(std::vector<float> &weights) const
-{
-    std::vector<float> ret (m_numModels);
-    float total = std::accumulate(weights.begin(),weights.end(),0.0);
-    for (size_t i=0;i < weights.size();i++) {
-        ret[i] = weights[i]/total;
-    }
-    return ret;
-}
-
-
-ChartRuleLookupManager *PhraseDictionaryMultiModel::CreateRuleLookupManager(const InputType&, const ChartCellCollectionBase&)
-{
-  UTIL_THROW(util::Exception, "Phrase table used in chart decoder");
-}
-
-
-//copied from PhraseDictionaryCompact; free memory allocated to TargetPhraseCollection (and each TargetPhrase) at end of sentence
-void PhraseDictionaryMultiModel::CacheForCleanup(TargetPhraseCollection* tpc) {
-#ifdef WITH_THREADS
-  boost::mutex::scoped_lock lock(m_sentenceMutex);
-  PhraseCache &ref = m_sentenceCache[boost::this_thread::get_id()];
-#else
-  PhraseCache &ref = m_sentenceCache;
-#endif
-  ref.push_back(tpc);
-}
-
-
-void PhraseDictionaryMultiModel::CleanUp(const InputType &source) {
-#ifdef WITH_THREADS
-  boost::mutex::scoped_lock lock(m_sentenceMutex);
-  PhraseCache &ref = m_sentenceCache[boost::this_thread::get_id()];
-#else
-  PhraseCache &ref = m_sentenceCache;
-#endif
-  for(PhraseCache::iterator it = ref.begin(); it != ref.end(); it++) {
-      delete *it;
-  }
-
-  PhraseCache temp;
-  temp.swap(ref);
-
-  CleanUpComponentModels(source);
-
-  const StaticData &staticData = StaticData::Instance();
-  std::vector<float> empty_vector;
-  (const_cast<StaticData&>(staticData)).SetTemporaryMultiModelWeightsVector(empty_vector);
-}
-
-
-void  PhraseDictionaryMultiModel::CleanUpComponentModels(const InputType &source) {
-  for(size_t i = 0; i < m_numModels; ++i){
-    m_pd[i]->CleanUp(source);
-  }
-}
-
-
-#ifdef WITH_DLIB
-vector<float> PhraseDictionaryMultiModel::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<multiModelStatisticsOptimization*> 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,multiModelStatistics*>* allStats = new(map<string,multiModelStatistics*>);
-
-        Phrase sourcePhrase(0);
-        sourcePhrase.CreateFromString(m_input, source_string, factorDelimiter);
-
-        CollectSufficientStatistics(sourcePhrase, 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;
-        }
-
-        multiModelStatisticsOptimization* targetStatistics = new multiModelStatisticsOptimization();
-        targetStatistics->targetPhrase = new TargetPhrase(*(*allStats)[target_string]->targetPhrase);
-        targetStatistics->p = (*allStats)[target_string]->p;
-        targetStatistics->f = iter->second;
-        optimizerStats.push_back(targetStatistics);
-
-        RemoveAllInMap(*allStats);
-        delete allStats;
-        }
-
-    Sentence sentence;
-    CleanUp(sentence); // free memory used by compact phrase tables
-
-    size_t numWeights = m_numScoreComponent;
-    if (m_mode == "interpolate") {
-        //interpolation of phrase penalty is skipped, and fixed-value (2.718) is used instead. results will be screwed up if phrase penalty is not last feature
-        numWeights = m_numScoreComponent-1;
-    }
-
-    vector<float> ret (m_numModels*numWeights);
-    for (size_t iFeature=0; iFeature < numWeights; iFeature++) {
-
-        CrossEntropy * ObjectiveFunction = new CrossEntropy(optimizerStats, this, iFeature);
-
-        vector<float> weight_vector = Optimize(ObjectiveFunction, m_numModels);
-
-        if (m_mode == "interpolate") {
-            weight_vector = normalizeWeights(weight_vector);
-        }
-
-        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;
-
-}
-
-vector<float> PhraseDictionaryMultiModel::Optimize(OptimizationObjective *ObjectiveFunction, size_t numModels) {
-
-        dlib::matrix<double,0,1> starting_point;
-        starting_point.set_size(numModels);
-        starting_point = 1.0;
-
-        try {
-            dlib::find_min_bobyqa(*ObjectiveFunction,
-                            starting_point,
-                            2*numModels+1,    // number of interpolation points
-                            dlib::uniform_matrix<double>(numModels,1, 1e-09),  // lower bound constraint
-                            dlib::uniform_matrix<double>(numModels,1, 1e100),   // upper bound constraint
-                            1.0,    // initial trust region radius
-                            1e-5,  // stopping trust region radius
-                            10000    // max number of objective function evaluations
-            );
-        }
-        catch (dlib::bobyqa_failure& e)
-        {
-            cerr << e.what() << endl;
-        }
-
-        vector<float> weight_vector (numModels);
-
-        for (int i=0; i < starting_point.nr(); i++) {
-            weight_vector[i] = starting_point(i);
-        }
-
-        cerr << "Cross-entropy: " << (*ObjectiveFunction)(starting_point) << endl;
-        return weight_vector;
-}
-
-
-double CrossEntropy::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<multiModelStatisticsOptimization*>::const_iterator iter = m_optimizerStats.begin(); iter != m_optimizerStats.end(); ++iter ) {
-        multiModelStatisticsOptimization* statistics = *iter;
-        size_t f = statistics->f;
-
-        double score;
-        score = std::inner_product(statistics->p[m_iFeature].begin(), statistics->p[m_iFeature].end(), weight_vector.begin(), 0.0);
-
-        total -= (FloorScore(TransformScore(score))/TransformScore(2))*f;
-        n += f;
-    }
-    return total/n;
-}
-
-#endif
-
-} //namespace
diff --git a/defer/PhraseDictionaryMultiModel.h b/defer/PhraseDictionaryMultiModel.h
deleted file mode 100644
index 3b7cba959..000000000
--- a/defer/PhraseDictionaryMultiModel.h
+++ /dev/null
@@ -1,150 +0,0 @@
-/***********************************************************************
-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
-***********************************************************************/
-
-#ifndef moses_PhraseDictionaryMultiModel_h
-#define moses_PhraseDictionaryMultiModel_h
-
-#include "moses/TranslationModel/PhraseDictionary.h"
-#include "moses/TranslationModel/PhraseDictionaryMemory.h"
-#include "moses/TranslationModel/PhraseDictionaryTreeAdaptor.h"
-#ifndef WIN32
-#include "moses/TranslationModel/CompactPT/PhraseDictionaryCompact.h"
-#endif
-
-
-#include <boost/unordered_map.hpp>
-#include "moses/StaticData.h"
-#include "moses/TargetPhrase.h"
-#include "moses/Util.h"
-#include "moses/UserMessage.h"
-
-#ifdef WITH_DLIB
-#include <dlib/optimization.h>
-#endif
-
-namespace Moses
-{
-
-  struct multiModelStatistics {
-    TargetPhrase *targetPhrase;
-    std::vector<std::vector<float> > p;
-    ~multiModelStatistics() {delete targetPhrase;};
-  };
-
-  struct multiModelStatisticsOptimization: multiModelStatistics {
-    size_t f;
-  };
-
-class OptimizationObjective;
-
-/** Implementation of a virtual phrase table constructed from multiple component phrase tables.
- */
-class PhraseDictionaryMultiModel: public PhraseDictionary
-{
-#ifdef WITH_DLIB
-friend class CrossEntropy;
-#endif
-
-public:
-  PhraseDictionaryMultiModel(size_t m_numScoreComponent, PhraseDictionaryFeature* feature);
-  ~PhraseDictionaryMultiModel();
-  bool Load(const std::vector<FactorType> &input
-            , const std::vector<FactorType> &output
-            , const std::vector<std::string> &files
-            , const std::vector<float> &weight
-            , size_t tableLimit
-            , size_t numInputScores
-            , const LMList &languageModels
-            , float weightWP);
-  virtual void CollectSufficientStatistics(const Phrase& src, std::map<std::string,multiModelStatistics*>* allStats) const;
-  virtual TargetPhraseCollection* CreateTargetPhraseCollectionLinearInterpolation(std::map<std::string,multiModelStatistics*>* allStats, std::vector<std::vector<float> > &multimodelweights) const;
-  std::vector<std::vector<float> > getWeights(size_t numWeights, bool normalize) const;
-  std::vector<float> normalizeWeights(std::vector<float> &weights) const;
-  void CacheForCleanup(TargetPhraseCollection* tpc);
-  void CleanUp(const InputType &source);
-  virtual void CleanUpComponentModels(const InputType &source);
-#ifdef WITH_DLIB
-  virtual std::vector<float> MinimizePerplexity(std::vector<std::pair<std::string, std::string> > &phrase_pair_vector);
-  std::vector<float> Optimize(OptimizationObjective * ObjectiveFunction, size_t numModels);
-#endif
-  // functions below required by base class
-  virtual const TargetPhraseCollection* GetTargetPhraseCollection(const Phrase& src) const;
-  virtual void InitializeForInput(InputType const&) {
-    /* Don't do anything source specific here as this object is shared between threads.*/
-  }
-  ChartRuleLookupManager *CreateRuleLookupManager(const InputType&, const ChartCellCollectionBase&);
-
-protected:
-  std::string m_mode;
-  std::vector<PhraseDictionary*> m_pd;
-  std::vector<float> m_weight;
-  const LMList *m_languageModels;
-  float m_weightWP;
-  std::vector<FactorType> m_input;
-  std::vector<FactorType> m_output;
-  size_t m_numModels;
-  size_t m_componentTableLimit;
-  PhraseDictionaryFeature* m_feature_load;
-
-  typedef std::vector<TargetPhraseCollection*> PhraseCache;
-#ifdef WITH_THREADS
-  boost::mutex m_sentenceMutex;
-  typedef std::map<boost::thread::id, PhraseCache> SentenceCache;
-#else
-  typedef PhraseCache SentenceCache;
-#endif
-  SentenceCache m_sentenceCache;
-
-};
-
-#ifdef WITH_DLIB
-class OptimizationObjective 
-{
-public:
-
-    virtual double operator() ( const dlib::matrix<double,0,1>& arg) const = 0;
-};
-
-class CrossEntropy: public OptimizationObjective
-{
-public:
-
-    CrossEntropy (
-        std::vector<multiModelStatisticsOptimization*> &optimizerStats,
-        PhraseDictionaryMultiModel * model,
-        size_t iFeature
-    )
-    {
-        m_optimizerStats = optimizerStats;
-        m_model = model;
-        m_iFeature = iFeature;
-    }
-
-    double operator() ( const dlib::matrix<double,0,1>& arg) const;
-
-protected:
-    std::vector<multiModelStatisticsOptimization*> m_optimizerStats;
-    PhraseDictionaryMultiModel * m_model;
-    size_t m_iFeature;
-};
-#endif
-
-} // end namespace
-
-#endif