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/***********************************************************************
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/check.hh"
#include <limits>
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include "Factor.h"
#include "Util.h"
#include "LM/SingleFactor.h"
#include "RandLM.h"
#include "LM/Rand.h"
#include "FactorCollection.h"
#include "Phrase.h"
#include "InputFileStream.h"
#include "StaticData.h"
namespace Moses
{
namespace
{
using namespace std;
class LanguageModelRandLM : public LanguageModelPointerState
{
public:
LanguageModelRandLM()
: m_lm(0) {}
bool Load(const std::string &filePath, FactorType factorType, size_t nGramOrder);
virtual LMResult GetValue(const std::vector<const Word*> &contextFactor, State* finalState = NULL) const;
~LanguageModelRandLM() {
delete m_lm;
}
void CleanUpAfterSentenceProcessing() {
m_lm->clearCaches(); // clear caches
}
void InitializeBeforeSentenceProcessing() {
m_lm->initThreadSpecificData(); // Creates thread specific data iff
// compiled with multithreading.
}
protected:
std::vector<randlm::WordID> m_randlm_ids_vec;
randlm::RandLM* m_lm;
randlm::WordID m_oov_id;
void CreateFactors(FactorCollection &factorCollection);
randlm::WordID GetLmID( const std::string &str ) const;
randlm::WordID GetLmID( const Factor *factor ) const {
size_t factorId = factor->GetId();
return ( factorId >= m_randlm_ids_vec.size()) ? m_oov_id : m_randlm_ids_vec[factorId];
};
};
bool LanguageModelRandLM::Load(const std::string &filePath, FactorType factorType,
size_t nGramOrder)
{
cerr << "Loading LanguageModelRandLM..." << endl;
FactorCollection &factorCollection = FactorCollection::Instance();
m_filePath = filePath;
m_factorType = factorType;
m_nGramOrder = nGramOrder;
int cache_MB = 50; // increase cache size
m_lm = randlm::RandLM::initRandLM(filePath, nGramOrder, cache_MB);
CHECK(m_lm != NULL);
// get special word ids
m_oov_id = m_lm->getWordID(m_lm->getOOV());
CreateFactors(factorCollection);
m_lm->initThreadSpecificData();
return true;
}
void LanguageModelRandLM::CreateFactors(FactorCollection &factorCollection) // add factors which have randlm id
{
// code copied & paste from SRI LM class. should do template function
// first get all bf vocab in map
std::map<size_t, randlm::WordID> randlm_ids_map; // map from factor id -> randlm id
size_t maxFactorId = 0; // to create lookup vector later on
for(std::map<randlm::Word, randlm::WordID>::const_iterator vIter = m_lm->vocabStart();
vIter != m_lm->vocabEnd(); vIter++) {
// get word from randlm vocab and associate with (new) factor id
size_t factorId=factorCollection.AddFactor(Output,m_factorType,vIter->first)->GetId();
randlm_ids_map[factorId] = vIter->second;
maxFactorId = (factorId > maxFactorId) ? factorId : maxFactorId;
}
// add factors for BOS and EOS and store bf word ids
size_t factorId;
m_sentenceStart = factorCollection.AddFactor(Output, m_factorType, m_lm->getBOS());
factorId = m_sentenceStart->GetId();
maxFactorId = (factorId > maxFactorId) ? factorId : maxFactorId;
m_sentenceStartArray[m_factorType] = m_sentenceStart;
m_sentenceEnd = factorCollection.AddFactor(Output, m_factorType, m_lm->getEOS());
factorId = m_sentenceEnd->GetId();
maxFactorId = (factorId > maxFactorId) ? factorId : maxFactorId;
m_sentenceEndArray[m_factorType] = m_sentenceEnd;
// add to lookup vector in object
m_randlm_ids_vec.resize(maxFactorId+1);
// fill with OOV code
fill(m_randlm_ids_vec.begin(), m_randlm_ids_vec.end(), m_oov_id);
for (map<size_t, randlm::WordID>::const_iterator iter = randlm_ids_map.begin();
iter != randlm_ids_map.end() ; ++iter)
m_randlm_ids_vec[iter->first] = iter->second;
}
randlm::WordID LanguageModelRandLM::GetLmID( const std::string &str ) const
{
return m_lm->getWordID(str);
}
LMResult LanguageModelRandLM::GetValue(const vector<const Word*> &contextFactor,
State* finalState) const
{
FactorType factorType = GetFactorType();
// set up context
randlm::WordID ngram[MAX_NGRAM_SIZE];
int count = contextFactor.size();
for (int i = 0 ; i < count ; i++) {
ngram[i] = GetLmID((*contextFactor[i])[factorType]);
//std::cerr << m_lm->getWord(ngram[i]) << " ";
}
int found = 0;
LMResult ret;
ret.score = FloorScore(TransformLMScore(m_lm->getProb(&ngram[0], count, &found, finalState)));
ret.unknown = count && (ngram[count - 1] == m_oov_id);
//if (finalState)
// std::cerr << " = " << logprob << "(" << *finalState << ", " <<")"<< std::endl;
//else
// std::cerr << " = " << logprob << std::endl;
return ret;
}
}
LanguageModelPointerState *NewRandLM() {
return new LanguageModelRandLM();
}
}
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