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// $Id$
/***********************************************************************
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_LanguageModelKen_h
#define moses_LanguageModelKen_h
#include <string>
#include <boost/shared_ptr.hpp>
#include "lm/word_index.hh"
#include "util/mmap.hh"
#include "moses/LM/Base.h"
#include "moses/Hypothesis.h"
#include "moses/TypeDef.h"
#include "moses/Word.h"
namespace Moses
{
//class LanguageModel;
class FFState;
class InMemoryPerSentenceOnDemandLM;
LanguageModel *ConstructKenLM(const std::string &line);
//! This will also load. Returns a templated KenLM class
LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method);
/*
* An implementation of single factor LM using Kenneth's code.
*/
template <class Model> class LanguageModelKen : public LanguageModel
{
public:
LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method);
virtual const FFState *EmptyHypothesisState(const InputType &/*input*/) const;
virtual void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const;
virtual FFState *EvaluateWhenApplied(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const;
virtual FFState *EvaluateWhenApplied(const ChartHypothesis& cur_hypo, int featureID, ScoreComponentCollection *accumulator) const;
virtual FFState *EvaluateWhenApplied(const Syntax::SHyperedge& hyperedge, int featureID, ScoreComponentCollection *accumulator) const;
virtual void IncrementalCallback(Incremental::Manager &manager) const;
virtual void ReportHistoryOrder(std::ostream &out,const Phrase &phrase) const;
virtual bool IsUseable(const FactorMask &mask) const;
friend class InMemoryPerSentenceOnDemandLM;
protected:
boost::shared_ptr<Model> m_ngram;
const Factor *m_beginSentenceFactor;
FactorType m_factorType;
void LoadModel(const std::string &file, util::LoadMethod load_method);
lm::WordIndex TranslateID(const Word &word) const {
std::size_t factor = word.GetFactor(m_factorType)->GetId();
return (factor >= m_lmIdLookup.size() ? 0 : m_lmIdLookup[factor]);
}
std::vector<lm::WordIndex> m_lmIdLookup;
private:
LanguageModelKen();
LanguageModelKen(const LanguageModelKen<Model> ©_from);
// Convert last words of hypothesis into vocab ids, returning an end pointer.
lm::WordIndex *LastIDs(const Hypothesis &hypo, lm::WordIndex *indices) const {
lm::WordIndex *index = indices;
lm::WordIndex *end = indices + m_ngram->Order() - 1;
int position = hypo.GetCurrTargetWordsRange().GetEndPos();
for (; ; ++index, --position) {
if (index == end) return index;
if (position == -1) {
*index = m_ngram->GetVocabulary().BeginSentence();
return index + 1;
}
*index = TranslateID(hypo.GetWord(position));
}
}
protected:
//bool m_oovFeatureEnabled; /// originally from LanguageModel, copied here to separate the interfaces. Called m_enableOOVFeature there
};
} // namespace Moses
#endif
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