ChartRuleLookupManagerOnDisk::~ChartRuleLookupManagerOnDisk()
{
std::map<UINT64, const TargetPhraseCollection*>::const_iterator iterCache;
for (iterCache = m_cache.begin(); iterCache != m_cache.end(); ++iterCache) {
delete iterCache->second;
}
m_cache.clear();
RemoveAllInColl(m_expandableDottedRuleListVec);
RemoveAllInColl(m_sourcePhraseNode);
}
MockHypothesisGuard::~MockHypothesisGuard()
{
RemoveAllInColl(m_toptions);
while (m_hypothesis) {
Hypothesis* prevHypo = const_cast<Hypothesis*>(m_hypothesis->GetPrevHypo());
delete m_hypothesis;
m_hypothesis = prevHypo;
}
}
void PhraseDictionaryNewFormat::CleanUp()
{
//RemoveAllInColl(m_chartTargetPhraseColl);
std::vector<ChartRuleCollection*>::iterator iter;
for (iter = m_chartTargetPhraseColl.begin(); iter != m_chartTargetPhraseColl.end(); ++iter)
{
ChartRuleCollection *item = *iter;
ChartRuleCollection::Delete(item);
}
m_chartTargetPhraseColl.clear();
RemoveAllInColl(m_runningNodesVec);
}
ChartParser::~ChartParser()
{
RemoveAllInColl(m_ruleLookupManagers);
StaticData::Instance().CleanUpAfterSentenceProcessing(m_source);
InputPathMatrix::const_iterator iterOuter;
for (iterOuter = m_inputPathMatrix.begin(); iterOuter != m_inputPathMatrix.end(); ++iterOuter) {
const std::vector<InputPath*> &outer = *iterOuter;
std::vector<InputPath*>::const_iterator iterInner;
for (iterInner = outer.begin(); iterInner != outer.end(); ++iterInner) {
InputPath *path = *iterInner;
delete path;
}
}
}
void FeatureFunction::Destroy()
{
RemoveAllInColl(s_staticColl);
}
Sentence::~Sentence()
{
RemoveAllInColl(m_xmlOptions);
}
TranslationOptionList::~TranslationOptionList()
{
RemoveAllInColl(m_coll);
}
ChartRuleLookupManagerMemoryPerSentence::~ChartRuleLookupManagerMemoryPerSentence()
{
RemoveAllInColl(m_dottedRuleColls);
}
DecodeGraph::~DecodeGraph()
{
RemoveAllInColl(m_steps);
}
void LMList::CleanUp()
{
RemoveAllInColl(m_coll);
}
ChartCellCollectionBase::~ChartCellCollectionBase()
{
m_source.clear();
for (std::vector<std::vector<ChartCellBase*> >::iterator i = m_cells.begin(); i != m_cells.end(); ++i)
RemoveAllInColl(*i);
}
ChartTrellisNode::~ChartTrellisNode()
{
RemoveAllInColl(m_children);
}
vector<float> PhraseDictionaryMultiModel::MinimizePerplexity(vector<pair<string, string> > &phrase_pair_vector)
{
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(Input, m_input, source_string, NULL);
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;
CleanUpAfterSentenceProcessing(sentence); // free memory used by compact phrase tables
size_t numWeights = m_numScoreComponents;
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> 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(Input, m_input, source_string, factorDelimiter, NULL);
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, false );
targetStatistics->lexCachef2e = CacheLexicalStatistics(sourcePhrase, static_cast<const Phrase&>(*targetStatistics->targetPhrase), alignment.first, m_lexTable_f2e, true );
optimizerStats.push_back(targetStatistics);
} catch (AlignmentException& e) {}
RemoveAllInMap(*allStats);
delete allStats;
}
Sentence sentence;
CleanUpAfterSentenceProcessing(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;
}
ChartTranslationOptionList::~ChartTranslationOptionList()
{
RemoveAllInColl(m_collection);
}
PhraseDictionaryMultiModelCounts::~PhraseDictionaryMultiModelCounts()
{
RemoveAllInColl(m_lexTable_e2f);
RemoveAllInColl(m_lexTable_f2e);
}
ChartRuleLookupManagerMemory::~ChartRuleLookupManagerMemory()
{
RemoveAllInColl(m_processedRuleColls);
}
void SkeletonPT::CleanUpAfterSentenceProcessing(const InputType& source)
{
RemoveAllInColl(m_allTPColl);
}
RuleCube::~RuleCube()
{
RemoveAllInColl(m_covered);
}
AlignmentPhrase::~AlignmentPhrase()
{
RemoveAllInColl(m_collection);
}
ChartParserUnknown::~ChartParserUnknown()
{
RemoveAllInColl(m_unksrcs);
RemoveAllInColl(m_cacheTargetPhraseCollection);
}
ProcessedRuleStack::~ProcessedRuleStack()
{
RemoveAllInColl(m_coll);
RemoveAllInColl(m_savedNode);
}
ChartRuleLookupManagerMemory::~ChartRuleLookupManagerMemory()
{
RemoveAllInColl(m_dottedRuleColls);
}
ChartRuleCollection::~ChartRuleCollection()
{
RemoveAllInColl(m_collection);
}