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5bd44896f9
mosesdecoder/moses/StaticData.cpp
Hieu Hoang 5bd44896f9 refactor PhraseDictionaryFeature
2013-02-12 17:16:34 +00:00

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// $Id$
// vim:tabstop=2
/***********************************************************************
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 <string>
#include "util/check.hh"
#include "moses/TranslationModel/PhraseDictionaryMemory.h"
#include "DecodeStepTranslation.h"
#include "DecodeStepGeneration.h"
#include "GenerationDictionary.h"
#include "DummyScoreProducers.h"
#include "StaticData.h"
#include "Util.h"
#include "FactorCollection.h"
#include "Timer.h"
#include "LexicalReordering.h"
#include "GlobalLexicalModel.h"
#include "GlobalLexicalModelUnlimited.h"
#include "SentenceStats.h"
#include "PhraseBoundaryFeature.h"
#include "moses/TranslationModel/PhraseDictionary.h"
#include "SparsePhraseDictionaryFeature.h"
#include "PhrasePairFeature.h"
#include "PhraseLengthFeature.h"
#include "TargetWordInsertionFeature.h"
#include "SourceWordDeletionFeature.h"
#include "WordTranslationFeature.h"
#include "UserMessage.h"
#include "TranslationOption.h"
#include "TargetBigramFeature.h"
#include "TargetNgramFeature.h"
#include "DecodeGraph.h"
#include "InputFileStream.h"
#include "BleuScoreFeature.h"
#include "ScoreComponentCollection.h"
#include "LM/Ken.h"
#include "LM/IRST.h"
#ifdef HAVE_SYNLM
#include "SyntacticLanguageModel.h"
#endif
#ifdef WITH_THREADS
#include <boost/thread.hpp>
#endif
using namespace std;
namespace Moses
{
static size_t CalcMax(size_t x, const vector<size_t>& y)
{
size_t max = x;
for (vector<size_t>::const_iterator i=y.begin(); i != y.end(); ++i)
if (*i > max) max = *i;
return max;
}
static size_t CalcMax(size_t x, const vector<size_t>& y, const vector<size_t>& z)
{
size_t max = x;
for (vector<size_t>::const_iterator i=y.begin(); i != y.end(); ++i)
if (*i > max) max = *i;
for (vector<size_t>::const_iterator i=z.begin(); i != z.end(); ++i)
if (*i > max) max = *i;
return max;
}
int GetFeatureIndex(std::map<string, int> &map, const string &featureName)
{
std::map<string, int>::iterator iter;
iter = map.find(featureName);
if (iter == map.end()) {
map[featureName] = 0;
return 0;
}
else {
int &index = iter->second;
++index;
return index;
}
}
StaticData StaticData::s_instance;
StaticData::StaticData()
:m_sourceStartPosMattersForRecombination(false)
,m_inputType(SentenceInput)
,m_detailedTranslationReportingFilePath()
,m_onlyDistinctNBest(false)
,m_factorDelimiter("|") // default delimiter between factors
,m_lmEnableOOVFeature(false)
,m_isAlwaysCreateDirectTranslationOption(false)
,m_needAlignmentInfo(false)
{
m_xmlBrackets.first="<";
m_xmlBrackets.second=">";
// memory pools
Phrase::InitializeMemPool();
}
bool StaticData::LoadDataStatic(Parameter *parameter, const std::string &execPath) {
s_instance.SetExecPath(execPath);
return s_instance.LoadData(parameter);
}
bool StaticData::LoadData(Parameter *parameter)
{
ResetUserTime();
m_parameter = parameter;
// verbose level
m_verboseLevel = 1;
if (m_parameter->GetParam("verbose").size() == 1) {
m_verboseLevel = Scan<size_t>( m_parameter->GetParam("verbose")[0]);
}
m_parsingAlgorithm = (m_parameter->GetParam("parsing-algorithm").size() > 0) ?
(ParsingAlgorithm) Scan<size_t>(m_parameter->GetParam("parsing-algorithm")[0]) : ParseCYKPlus;
// to cube or not to cube
m_searchAlgorithm = (m_parameter->GetParam("search-algorithm").size() > 0) ?
(SearchAlgorithm) Scan<size_t>(m_parameter->GetParam("search-algorithm")[0]) : Normal;
if (IsChart())
LoadChartDecodingParameters();
// input type has to be specified BEFORE loading the phrase tables!
if(m_parameter->GetParam("inputtype").size())
m_inputType= (InputTypeEnum) Scan<int>(m_parameter->GetParam("inputtype")[0]);
std::string s_it = "text input";
if (m_inputType == 1) {
s_it = "confusion net";
}
if (m_inputType == 2) {
s_it = "word lattice";
}
VERBOSE(2,"input type is: "<<s_it<<"\n");
if(m_parameter->GetParam("recover-input-path").size()) {
m_recoverPath = Scan<bool>(m_parameter->GetParam("recover-input-path")[0]);
if (m_recoverPath && m_inputType == SentenceInput) {
TRACE_ERR("--recover-input-path should only be used with confusion net or word lattice input!\n");
m_recoverPath = false;
}
}
if(m_parameter->GetParam("sort-word-alignment").size()) {
m_wordAlignmentSort = (WordAlignmentSort) Scan<size_t>(m_parameter->GetParam("sort-word-alignment")[0]);
}
// factor delimiter
if (m_parameter->GetParam("factor-delimiter").size() > 0) {
m_factorDelimiter = m_parameter->GetParam("factor-delimiter")[0];
}
SetBooleanParameter( &m_continuePartialTranslation, "continue-partial-translation", false );
SetBooleanParameter( &m_outputHypoScore, "output-hypo-score", false );
//word-to-word alignment
SetBooleanParameter( &m_PrintAlignmentInfoNbest, "print-alignment-info-in-n-best", false );
if (m_PrintAlignmentInfoNbest) {
m_needAlignmentInfo = true;
}
if (m_parameter->GetParam("alignment-output-file").size() > 0) {
m_alignmentOutputFile = Scan<std::string>(m_parameter->GetParam("alignment-output-file")[0]);
m_needAlignmentInfo = true;
}
// n-best
if (m_parameter->GetParam("n-best-list").size() >= 2) {
m_nBestFilePath = m_parameter->GetParam("n-best-list")[0];
m_nBestSize = Scan<size_t>( m_parameter->GetParam("n-best-list")[1] );
m_onlyDistinctNBest=(m_parameter->GetParam("n-best-list").size()>2 && m_parameter->GetParam("n-best-list")[2]=="distinct");
} else if (m_parameter->GetParam("n-best-list").size() == 1) {
UserMessage::Add(string("wrong format for switch -n-best-list file size"));
return false;
} else {
m_nBestSize = 0;
}
if (m_parameter->GetParam("n-best-factor").size() > 0) {
m_nBestFactor = Scan<size_t>( m_parameter->GetParam("n-best-factor")[0]);
} else {
m_nBestFactor = 20;
}
// explicit setting of distinct nbest
SetBooleanParameter( &m_onlyDistinctNBest, "distinct-nbest", false);
//lattice samples
if (m_parameter->GetParam("lattice-samples").size() ==2 ) {
m_latticeSamplesFilePath = m_parameter->GetParam("lattice-samples")[0];
m_latticeSamplesSize = Scan<size_t>(m_parameter->GetParam("lattice-samples")[1]);
} else if (m_parameter->GetParam("lattice-samples").size() != 0 ) {
UserMessage::Add(string("wrong format for switch -lattice-samples file size"));
return false;
} else {
m_latticeSamplesSize = 0;
}
// word graph
if (m_parameter->GetParam("output-word-graph").size() == 2)
m_outputWordGraph = true;
else
m_outputWordGraph = false;
// search graph
if (m_parameter->GetParam("output-search-graph").size() > 0) {
if (m_parameter->GetParam("output-search-graph").size() != 1) {
UserMessage::Add(string("ERROR: wrong format for switch -output-search-graph file"));
return false;
}
m_outputSearchGraph = true;
}
// ... in extended format
else if (m_parameter->GetParam("output-search-graph-extended").size() > 0) {
if (m_parameter->GetParam("output-search-graph-extended").size() != 1) {
UserMessage::Add(string("ERROR: wrong format for switch -output-search-graph-extended file"));
return false;
}
m_outputSearchGraph = true;
m_outputSearchGraphExtended = true;
} else
m_outputSearchGraph = false;
#ifdef HAVE_PROTOBUF
if (m_parameter->GetParam("output-search-graph-pb").size() > 0) {
if (m_parameter->GetParam("output-search-graph-pb").size() != 1) {
UserMessage::Add(string("ERROR: wrong format for switch -output-search-graph-pb path"));
return false;
}
m_outputSearchGraphPB = true;
} else
m_outputSearchGraphPB = false;
#endif
SetBooleanParameter( &m_unprunedSearchGraph, "unpruned-search-graph", false );
SetBooleanParameter( &m_includeLHSInSearchGraph, "include-lhs-in-search-graph", false );
if (m_parameter->isParamSpecified("output-unknowns")) {
if (m_parameter->GetParam("output-unknowns").size() == 1) {
m_outputUnknownsFile =Scan<string>(m_parameter->GetParam("output-unknowns")[0]);
} else {
UserMessage::Add(string("need to specify exactly one file name for unknowns"));
return false;
}
}
// include feature names in the n-best list
SetBooleanParameter( &m_labeledNBestList, "labeled-n-best-list", true );
// include word alignment in the n-best list
SetBooleanParameter( &m_nBestIncludesSegmentation, "include-segmentation-in-n-best", false );
// printing source phrase spans
SetBooleanParameter( &m_reportSegmentation, "report-segmentation", false );
// print all factors of output translations
SetBooleanParameter( &m_reportAllFactors, "report-all-factors", false );
// print all factors of output translations
SetBooleanParameter( &m_reportAllFactorsNBest, "report-all-factors-in-n-best", false );
// caching of translation options
if (m_inputType == SentenceInput) {
SetBooleanParameter( &m_useTransOptCache, "use-persistent-cache", true );
m_transOptCacheMaxSize = (m_parameter->GetParam("persistent-cache-size").size() > 0)
? Scan<size_t>(m_parameter->GetParam("persistent-cache-size")[0]) : DEFAULT_MAX_TRANS_OPT_CACHE_SIZE;
} else {
m_useTransOptCache = false;
}
//input factors
const vector<string> &inputFactorVector = m_parameter->GetParam("input-factors");
for(size_t i=0; i<inputFactorVector.size(); i++) {
m_inputFactorOrder.push_back(Scan<FactorType>(inputFactorVector[i]));
}
if(m_inputFactorOrder.empty()) {
UserMessage::Add(string("no input factor specified in config file"));
return false;
}
//output factors
const vector<string> &outputFactorVector = m_parameter->GetParam("output-factors");
for(size_t i=0; i<outputFactorVector.size(); i++) {
m_outputFactorOrder.push_back(Scan<FactorType>(outputFactorVector[i]));
}
if(m_outputFactorOrder.empty()) {
// default. output factor 0
m_outputFactorOrder.push_back(0);
}
//source word deletion
SetBooleanParameter( &m_wordDeletionEnabled, "phrase-drop-allowed", false );
//Disable discarding
SetBooleanParameter(&m_disableDiscarding, "disable-discarding", false);
//Print All Derivations
SetBooleanParameter( &m_printAllDerivations , "print-all-derivations", false );
// additional output
if (m_parameter->isParamSpecified("translation-details")) {
const vector<string> &args = m_parameter->GetParam("translation-details");
if (args.size() == 1) {
m_detailedTranslationReportingFilePath = args[0];
} else {
UserMessage::Add(string("the translation-details option requires exactly one filename argument"));
return false;
}
}
// reordering constraints
m_maxDistortion = (m_parameter->GetParam("distortion-limit").size() > 0) ?
Scan<int>(m_parameter->GetParam("distortion-limit")[0])
: -1;
SetBooleanParameter( &m_reorderingConstraint, "monotone-at-punctuation", false );
// settings for pruning
m_maxHypoStackSize = (m_parameter->GetParam("stack").size() > 0)
? Scan<size_t>(m_parameter->GetParam("stack")[0]) : DEFAULT_MAX_HYPOSTACK_SIZE;
m_minHypoStackDiversity = 0;
if (m_parameter->GetParam("stack-diversity").size() > 0) {
if (m_maxDistortion > 15) {
UserMessage::Add("stack diversity > 0 is not allowed for distortion limits larger than 15");
return false;
}
if (m_inputType == WordLatticeInput) {
UserMessage::Add("stack diversity > 0 is not allowed for lattice input");
return false;
}
m_minHypoStackDiversity = Scan<size_t>(m_parameter->GetParam("stack-diversity")[0]);
}
m_beamWidth = (m_parameter->GetParam("beam-threshold").size() > 0) ?
TransformScore(Scan<float>(m_parameter->GetParam("beam-threshold")[0]))
: TransformScore(DEFAULT_BEAM_WIDTH);
m_earlyDiscardingThreshold = (m_parameter->GetParam("early-discarding-threshold").size() > 0) ?
TransformScore(Scan<float>(m_parameter->GetParam("early-discarding-threshold")[0]))
: TransformScore(DEFAULT_EARLY_DISCARDING_THRESHOLD);
m_translationOptionThreshold = (m_parameter->GetParam("translation-option-threshold").size() > 0) ?
TransformScore(Scan<float>(m_parameter->GetParam("translation-option-threshold")[0]))
: TransformScore(DEFAULT_TRANSLATION_OPTION_THRESHOLD);
m_maxNoTransOptPerCoverage = (m_parameter->GetParam("max-trans-opt-per-coverage").size() > 0)
? Scan<size_t>(m_parameter->GetParam("max-trans-opt-per-coverage")[0]) : DEFAULT_MAX_TRANS_OPT_SIZE;
m_maxNoPartTransOpt = (m_parameter->GetParam("max-partial-trans-opt").size() > 0)
? Scan<size_t>(m_parameter->GetParam("max-partial-trans-opt")[0]) : DEFAULT_MAX_PART_TRANS_OPT_SIZE;
m_maxPhraseLength = (m_parameter->GetParam("max-phrase-length").size() > 0)
? Scan<size_t>(m_parameter->GetParam("max-phrase-length")[0]) : DEFAULT_MAX_PHRASE_LENGTH;
m_cubePruningPopLimit = (m_parameter->GetParam("cube-pruning-pop-limit").size() > 0)
? Scan<size_t>(m_parameter->GetParam("cube-pruning-pop-limit")[0]) : DEFAULT_CUBE_PRUNING_POP_LIMIT;
m_cubePruningDiversity = (m_parameter->GetParam("cube-pruning-diversity").size() > 0)
? Scan<size_t>(m_parameter->GetParam("cube-pruning-diversity")[0]) : DEFAULT_CUBE_PRUNING_DIVERSITY;
SetBooleanParameter(&m_cubePruningLazyScoring, "cube-pruning-lazy-scoring", false);
// early distortion cost
SetBooleanParameter( &m_useEarlyDistortionCost, "early-distortion-cost", false );
// unknown word processing
SetBooleanParameter( &m_dropUnknown, "drop-unknown", false );
SetBooleanParameter( &m_lmEnableOOVFeature, "lmodel-oov-feature", false);
// minimum Bayes risk decoding
SetBooleanParameter( &m_mbr, "minimum-bayes-risk", false );
m_mbrSize = (m_parameter->GetParam("mbr-size").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("mbr-size")[0]) : 200;
m_mbrScale = (m_parameter->GetParam("mbr-scale").size() > 0) ?
Scan<float>(m_parameter->GetParam("mbr-scale")[0]) : 1.0f;
//lattice mbr
SetBooleanParameter( &m_useLatticeMBR, "lminimum-bayes-risk", false );
if (m_useLatticeMBR && m_mbr) {
cerr << "Errror: Cannot use both n-best mbr and lattice mbr together" << endl;
exit(1);
}
//mira training
SetBooleanParameter( &m_mira, "mira", false );
if (m_useLatticeMBR) m_mbr = true;
m_lmbrPruning = (m_parameter->GetParam("lmbr-pruning-factor").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("lmbr-pruning-factor")[0]) : 30;
m_lmbrThetas = Scan<float>(m_parameter->GetParam("lmbr-thetas"));
SetBooleanParameter( &m_useLatticeHypSetForLatticeMBR, "lattice-hypo-set", false );
m_lmbrPrecision = (m_parameter->GetParam("lmbr-p").size() > 0) ?
Scan<float>(m_parameter->GetParam("lmbr-p")[0]) : 0.8f;
m_lmbrPRatio = (m_parameter->GetParam("lmbr-r").size() > 0) ?
Scan<float>(m_parameter->GetParam("lmbr-r")[0]) : 0.6f;
m_lmbrMapWeight = (m_parameter->GetParam("lmbr-map-weight").size() >0) ?
Scan<float>(m_parameter->GetParam("lmbr-map-weight")[0]) : 0.0f;
//consensus decoding
SetBooleanParameter( &m_useConsensusDecoding, "consensus-decoding", false );
if (m_useConsensusDecoding && m_mbr) {
cerr<< "Error: Cannot use consensus decoding together with mbr" << endl;
exit(1);
}
if (m_useConsensusDecoding) m_mbr=true;
// Compact phrase table and reordering model
SetBooleanParameter( &m_minphrMemory, "minphr-memory", false );
SetBooleanParameter( &m_minlexrMemory, "minlexr-memory", false );
m_timeout_threshold = (m_parameter->GetParam("time-out").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("time-out")[0]) : -1;
m_timeout = (GetTimeoutThreshold() == (size_t)-1) ? false : true;
m_lmcache_cleanup_threshold = (m_parameter->GetParam("clean-lm-cache").size() > 0) ?
Scan<size_t>(m_parameter->GetParam("clean-lm-cache")[0]) : 1;
m_threadCount = 1;
const std::vector<std::string> &threadInfo = m_parameter->GetParam("threads");
if (!threadInfo.empty()) {
if (threadInfo[0] == "all") {
#ifdef WITH_THREADS
m_threadCount = boost::thread::hardware_concurrency();
if (!m_threadCount) {
UserMessage::Add("-threads all specified but Boost doesn't know how many cores there are");
return false;
}
#else
UserMessage::Add("-threads all specified but moses not built with thread support");
return false;
#endif
} else {
m_threadCount = Scan<int>(threadInfo[0]);
if (m_threadCount < 1) {
UserMessage::Add("Specify at least one thread.");
return false;
}
#ifndef WITH_THREADS
if (m_threadCount > 1) {
UserMessage::Add(std::string("Error: Thread count of ") + threadInfo[0] + " but moses not built with thread support");
return false;
}
#endif
}
}
m_startTranslationId = (m_parameter->GetParam("start-translation-id").size() > 0) ?
Scan<long>(m_parameter->GetParam("start-translation-id")[0]) : 0;
// Read in constraint decoding file, if provided
if(m_parameter->GetParam("constraint").size()) {
if (m_parameter->GetParam("search-algorithm").size() > 0
&& Scan<size_t>(m_parameter->GetParam("search-algorithm")[0]) != 0) {
cerr << "Can use -constraint only with stack-based search (-search-algorithm 0)" << endl;
exit(1);
}
m_constraintFileName = m_parameter->GetParam("constraint")[0];
InputFileStream constraintFile(m_constraintFileName);
std::string line;
long sentenceID = GetStartTranslationId() - 1;
while (getline(constraintFile, line)) {
vector<string> vecStr = Tokenize(line, "\t");
if (vecStr.size() == 1) {
sentenceID++;
Phrase phrase(0);
phrase.CreateFromString(GetOutputFactorOrder(), vecStr[0], GetFactorDelimiter());
m_constraints.insert(make_pair(sentenceID,phrase));
} else if (vecStr.size() == 2) {
sentenceID = Scan<long>(vecStr[0]);
Phrase phrase(0);
phrase.CreateFromString(GetOutputFactorOrder(), vecStr[1], GetFactorDelimiter());
m_constraints.insert(make_pair(sentenceID,phrase));
} else {
CHECK(false);
}
}
}
// use of xml in input
if (m_parameter->GetParam("xml-input").size() == 0) m_xmlInputType = XmlPassThrough;
else if (m_parameter->GetParam("xml-input")[0]=="exclusive") m_xmlInputType = XmlExclusive;
else if (m_parameter->GetParam("xml-input")[0]=="inclusive") m_xmlInputType = XmlInclusive;
else if (m_parameter->GetParam("xml-input")[0]=="ignore") m_xmlInputType = XmlIgnore;
else if (m_parameter->GetParam("xml-input")[0]=="pass-through") m_xmlInputType = XmlPassThrough;
else {
UserMessage::Add("invalid xml-input value, must be pass-through, exclusive, inclusive, or ignore");
return false;
}
// specify XML tags opening and closing brackets for XML option
if (m_parameter->GetParam("xml-brackets").size() > 0) {
std::vector<std::string> brackets = Tokenize(m_parameter->GetParam("xml-brackets")[0]);
if(brackets.size()!=2) {
cerr << "invalid xml-brackets value, must specify exactly 2 blank-delimited strings for XML tags opening and closing brackets" << endl;
exit(1);
}
m_xmlBrackets.first= brackets[0];
m_xmlBrackets.second=brackets[1];
cerr << "XML tags opening and closing brackets for XML input are: " << m_xmlBrackets.first << " and " << m_xmlBrackets.second << endl;
}
// all features
map<string, int> featureIndexMap;
const vector<string> &features = m_parameter->GetParam("feature");
for (size_t i = 0; i < features.size(); ++i) {
const string &line = Trim(features[i]);
cerr << "line=" << line << endl;
if (line.empty())
continue;
vector<string> toks = Tokenize(line);
const string &feature = toks[0];
int featureIndex = GetFeatureIndex(featureIndexMap, feature);
if (feature == "GlobalLexicalModel") {
GlobalLexicalModel *model = new GlobalLexicalModel(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "glm") {
GlobalLexicalModelUnlimited *model = NULL; //new GlobalLexicalModelUnlimited(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "swd") {
SourceWordDeletionFeature *model = new SourceWordDeletionFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "twi") {
TargetWordInsertionFeature *model = new TargetWordInsertionFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "PhraseBoundaryFeature") {
PhraseBoundaryFeature *model = new PhraseBoundaryFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "pl") {
PhraseLengthFeature *model = new PhraseLengthFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "WordTranslationFeature") {
WordTranslationFeature *model = new WordTranslationFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "TargetBigramFeature") {
TargetBigramFeature *model = new TargetBigramFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "TargetNgramFeature") {
TargetNgramFeature *model = new TargetNgramFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "PhrasePairFeature") {
PhrasePairFeature *model = new PhrasePairFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
}
else if (feature == "LexicalReordering") {
LexicalReordering *model = new LexicalReordering(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "KENLM") {
LanguageModel *model = ConstructKenLM(feature, line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "IRSTLM") {
LanguageModelIRST *irstlm = new LanguageModelIRST(line);
LanguageModel *model = new LMRefCount(irstlm, feature, line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "Generation") {
GenerationDictionary *model = new GenerationDictionary(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "BleuScoreFeature") {
BleuScoreFeature *model = new BleuScoreFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
else if (feature == "SparsePhraseDictionaryFeature") {
SparsePhraseDictionaryFeature *model = new SparsePhraseDictionaryFeature(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
//SetWeights(model, weights);
m_sparsePhraseDictionary.push_back(model);
}
else if (feature == "Distortion") {
DistortionScoreProducer *model = new DistortionScoreProducer(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
m_distortionScoreProducer = model;
}
else if (feature == "WordPenalty") {
WordPenaltyProducer *model = new WordPenaltyProducer(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
m_wpProducer = model;
}
else if (feature == "UnknownWordPenalty") {
UnknownWordPenaltyProducer *model = new UnknownWordPenaltyProducer(line);
vector<float> weights = m_parameter->GetWeights(feature, featureIndex);
if (weights.size() == 0)
weights.push_back(1.0f);
SetWeights(model, weights);
m_unknownWordPenaltyProducer = model;
}
else if (feature == "PhraseModel") {
PhraseDictionaryFeature* model = new PhraseDictionaryFeature(line);
vector<float> weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
m_phraseDictionary.push_back(model);
}
#ifdef HAVE_SYNLM
else if (feature == "SyntacticLanguageModel") {
SyntacticLanguageModel *model = new SyntacticLanguageModel(line);
const vector<float> &weights = m_parameter->GetWeights(feature, featureIndex);
SetWeights(model, weights);
}
#endif
else {
UserMessage::Add("Unknown feature function:" + feature);
return false;
}
}
CollectFeatureFunctions();
if (!LoadPhraseTables()) return false;
if (!LoadDecodeGraphs()) return false;
if (!CheckWeights()) {
return false;
}
// report individual sparse features in n-best list
if (m_parameter->GetParam("report-sparse-features").size() > 0) {
for(size_t i=0; i<m_parameter->GetParam("report-sparse-features").size(); i++) {
const std::string &name = m_parameter->GetParam("report-sparse-features")[i];
for (size_t j = 0; j < m_sparsePhraseDictionary.size(); ++j) {
if (m_sparsePhraseDictionary[j] && name.compare(m_sparsePhraseDictionary[j]->GetScoreProducerDescription()) == 0) {
m_sparsePhraseDictionary[j]->SetSparseFeatureReporting();
}
}
} // for(size_t i=0; i<m_parameter->GetParam("report-sparse-features").
}
//Instigate dictionary loading
ConfigDictionaries();
for (int i = 0; i < m_phraseDictionary.size(); i++)
cerr << m_phraseDictionary[i] << " ";
cerr << endl;
for (int i = 0; i < m_generationDictionary.size(); i++)
cerr << m_generationDictionary[i] << " ";
cerr << endl;
//Add any other features here.
//Load extra feature weights
//NB: These are common to all translation systems (at the moment!)
vector<string> extraWeightConfig = m_parameter->GetParam("weight-file");
if (extraWeightConfig.size()) {
if (extraWeightConfig.size() != 1) {
UserMessage::Add("One argument should be supplied for weight-file");
return false;
}
ScoreComponentCollection extraWeights;
if (!extraWeights.Load(extraWeightConfig[0])) {
UserMessage::Add("Unable to load weights from " + extraWeightConfig[0]);
return false;
}
m_allWeights.PlusEquals(extraWeights);
}
cerr << endl << "m_allWeights=" << m_allWeights << endl;
return true;
}
void StaticData::SetBooleanParameter( bool *parameter, string parameterName, bool defaultValue )
{
// default value if nothing is specified
*parameter = defaultValue;
if (! m_parameter->isParamSpecified( parameterName ) ) {
return;
}
// if parameter is just specified as, e.g. "-parameter" set it true
if (m_parameter->GetParam( parameterName ).size() == 0) {
*parameter = true;
}
// if paramter is specified "-parameter true" or "-parameter false"
else if (m_parameter->GetParam( parameterName ).size() == 1) {
*parameter = Scan<bool>( m_parameter->GetParam( parameterName )[0]);
}
}
void StaticData::SetWeight(const ScoreProducer* sp, float weight)
{
m_allWeights.Resize();
m_allWeights.Assign(sp,weight);
}
void StaticData::SetWeights(const ScoreProducer* sp, const std::vector<float>& weights)
{
m_allWeights.Resize();
m_allWeights.Assign(sp,weights);
}
StaticData::~StaticData()
{
/*
const std::vector<ScoreProducer*> &producers = FeatureFunction::GetFeatureFunctions();
for(size_t i=0;i<producers.size();++i) {
ScoreProducer *ff = producers[i];
cerr << endl << "Destroying" << ff << endl;
delete ff;
}
*/
// memory pools
Phrase::FinalizeMemPool();
}
/* Doesn't load phrase tables any more. Just creates the features. */
bool StaticData::LoadPhraseTables()
{
//VERBOSE(2,"Creating phrase table features" << endl);
if(m_inputType == ConfusionNetworkInput || m_inputType == WordLatticeInput) {
if (m_parameter->GetParam("input-scores").size()) {
m_numInputScores = Scan<size_t>(m_parameter->GetParam("input-scores")[0]);
}
else {
m_numInputScores = 1;
}
if (m_parameter->GetParam("input-scores").size() > 1) {
m_numRealWordsInInput = Scan<size_t>(m_parameter->GetParam("input-scores")[1]);
}
else {
m_numRealWordsInInput = 0;
}
}
else { // not confusion network or lattice input
m_numInputScores = 0;
m_numRealWordsInInput = 0;
}
/*
// load phrase translation tables
if (m_parameter->GetParam("ttable-file").size() > 0) {
// weights
const vector<string> &translationVector = m_parameter->GetParam("ttable-file");
vector<size_t> maxTargetPhrase = Scan<size_t>(m_parameter->GetParam("ttable-limit"));
if(maxTargetPhrase.size() == 1 && translationVector.size() > 1) {
VERBOSE(1, "Using uniform ttable-limit of " << maxTargetPhrase[0] << " for all translation tables." << endl);
for(size_t i = 1; i < translationVector.size(); i++)
maxTargetPhrase.push_back(maxTargetPhrase[0]);
} else if(maxTargetPhrase.size() != 1 && maxTargetPhrase.size() < translationVector.size()) {
stringstream strme;
strme << "You specified " << translationVector.size() << " translation tables, but only " << maxTargetPhrase.size() << " ttable-limits.";
UserMessage::Add(strme.str());
return false;
}
// MAIN LOOP
for(size_t currDict = 0 ; currDict < translationVector.size(); currDict++) {
stringstream ptLine;
ptLine << "PhraseModel ";
vector<string> token = Tokenize(translationVector[currDict]);
if(currDict == 0 && token.size() == 4) {
UserMessage::Add("Phrase table specification in old 4-field format. No longer supported");
return false;
}
CHECK(token.size() >= 5);
PhraseTableImplementation implementation = (PhraseTableImplementation) Scan<int>(token[0]);
ptLine << "implementation=" << implementation << " ";
ptLine << "input-factor=" << token[1] << " ";
ptLine << "output-factor=" << token[2] << " ";
ptLine << "path=" << token[4] << " ";
//characteristics of the phrase table
vector<FactorType> input = Tokenize<FactorType>(token[1], ",")
,output = Tokenize<FactorType>(token[2], ",");
size_t numScoreComponent = Scan<size_t>(token[3]);
string filePath= token[4];
if(currDict==0 && (m_inputType == ConfusionNetworkInput || m_inputType == WordLatticeInput)) {
// only the 1st pt. THis is shit
// TODO. find what the assumptions made by confusion network about phrase table output which makes
// it only work with binary file. This is a hack
CHECK(implementation == Binary);
numScoreComponent += m_numInputScores + m_numRealWordsInInput;
}
ptLine << "num-features=" << numScoreComponent << " ";
ptLine << "num-input-features=" << (currDict==0 ? m_numInputScores + m_numRealWordsInInput : 0) << " ";
ptLine << "table-limit=" << maxTargetPhrase[currDict] << " ";
if (implementation == SuffixArray) {
ptLine << "target-path=" << token[5] << " ";
ptLine << "alignment-path=" << token[6] << " ";
}
//This is needed for regression testing, but the phrase table
//might not really be loading here
IFVERBOSE(1)
PrintUserTime(string("Start loading PhraseTable ") + filePath);
VERBOSE(1,"filePath: " << filePath <<endl);
PhraseDictionaryFeature* pdf = new PhraseDictionaryFeature(ptLine.str());
//optional create sparse phrase feature
if (m_sparsePhraseDictionary.size() > currDict) {
SparsePhraseDictionaryFeature* spdf = m_sparsePhraseDictionary[currDict];
pdf->SetSparsePhraseDictionaryFeature(spdf);
}
m_phraseDictionary.push_back(pdf);
const vector<float> &weights = m_parameter->GetWeights("PhraseModel", currDict);
CHECK(weights.size() >= numScoreComponent);
SetWeights(m_phraseDictionary.back(),weights);
}
}
IFVERBOSE(1)
PrintUserTime("Finished loading phrase tables");
*/
return true;
}
void StaticData::LoadNonTerminals()
{
string defaultNonTerminals;
if (m_parameter->GetParam("non-terminals").size() == 0) {
defaultNonTerminals = "X";
} else {
vector<std::string> tokens = Tokenize(m_parameter->GetParam("non-terminals")[0]);
defaultNonTerminals = tokens[0];
}
FactorCollection &factorCollection = FactorCollection::Instance();
m_inputDefaultNonTerminal.SetIsNonTerminal(true);
const Factor *sourceFactor = factorCollection.AddFactor(Input, 0, defaultNonTerminals);
m_inputDefaultNonTerminal.SetFactor(0, sourceFactor);
m_outputDefaultNonTerminal.SetIsNonTerminal(true);
const Factor *targetFactor = factorCollection.AddFactor(Output, 0, defaultNonTerminals);
m_outputDefaultNonTerminal.SetFactor(0, targetFactor);
// for unknwon words
if (m_parameter->GetParam("unknown-lhs").size() == 0) {
UnknownLHSEntry entry(defaultNonTerminals, 0.0f);
m_unknownLHS.push_back(entry);
} else {
const string &filePath = m_parameter->GetParam("unknown-lhs")[0];
InputFileStream inStream(filePath);
string line;
while(getline(inStream, line)) {
vector<string> tokens = Tokenize(line);
CHECK(tokens.size() == 2);
UnknownLHSEntry entry(tokens[0], Scan<float>(tokens[1]));
m_unknownLHS.push_back(entry);
}
}
}
void StaticData::LoadChartDecodingParameters()
{
LoadNonTerminals();
// source label overlap
if (m_parameter->GetParam("source-label-overlap").size() > 0) {
m_sourceLabelOverlap = (SourceLabelOverlap) Scan<int>(m_parameter->GetParam("source-label-overlap")[0]);
} else {
m_sourceLabelOverlap = SourceLabelOverlapAdd;
}
m_ruleLimit = (m_parameter->GetParam("rule-limit").size() > 0)
? Scan<size_t>(m_parameter->GetParam("rule-limit")[0]) : DEFAULT_MAX_TRANS_OPT_SIZE;
}
bool StaticData::LoadDecodeGraphs()
{
const vector<string> &mappingVector = m_parameter->GetParam("mapping");
const vector<size_t> &maxChartSpans = Scan<size_t>(m_parameter->GetParam("max-chart-span"));
DecodeStep *prev = 0;
size_t prevDecodeGraphInd = 0;
for(size_t i=0; i<mappingVector.size(); i++) {
vector<string> token = Tokenize(mappingVector[i]);
size_t decodeGraphInd;
DecodeType decodeType;
size_t index;
if (token.size() == 2) {
decodeGraphInd = 0;
decodeType = token[0] == "T" ? Translate : Generate;
index = Scan<size_t>(token[1]);
} else if (token.size() == 3) {
// For specifying multiple translation model
decodeGraphInd = Scan<size_t>(token[0]);
//the vectorList index can only increment by one
CHECK(decodeGraphInd == prevDecodeGraphInd || decodeGraphInd == prevDecodeGraphInd + 1);
if (decodeGraphInd > prevDecodeGraphInd) {
prev = NULL;
}
decodeType = token[1] == "T" ? Translate : Generate;
index = Scan<size_t>(token[2]);
} else {
UserMessage::Add("Malformed mapping!");
CHECK(false);
}
DecodeStep* decodeStep = NULL;
switch (decodeType) {
case Translate:
if(index>=m_phraseDictionary.size()) {
stringstream strme;
strme << "No phrase dictionary with index "
<< index << " available!";
UserMessage::Add(strme.str());
CHECK(false);
}
decodeStep = new DecodeStepTranslation(m_phraseDictionary[index], prev);
break;
case Generate:
if(index>=m_generationDictionary.size()) {
stringstream strme;
strme << "No generation dictionary with index "
<< index << " available!";
UserMessage::Add(strme.str());
CHECK(false);
}
decodeStep = new DecodeStepGeneration(m_generationDictionary[index], prev);
break;
case InsertNullFertilityWord:
CHECK(!"Please implement NullFertilityInsertion.");
break;
}
CHECK(decodeStep);
if (m_decodeGraphs.size() < decodeGraphInd + 1) {
DecodeGraph *decodeGraph;
if (IsChart()) {
size_t maxChartSpan = (decodeGraphInd < maxChartSpans.size()) ? maxChartSpans[decodeGraphInd] : DEFAULT_MAX_CHART_SPAN;
cerr << "max-chart-span: " << maxChartSpans[decodeGraphInd] << endl;
decodeGraph = new DecodeGraph(m_decodeGraphs.size(), maxChartSpan);
} else {
decodeGraph = new DecodeGraph(m_decodeGraphs.size());
}
m_decodeGraphs.push_back(decodeGraph); // TODO max chart span
}
m_decodeGraphs[decodeGraphInd]->Add(decodeStep);
prev = decodeStep;
prevDecodeGraphInd = decodeGraphInd;
}
// set maximum n-gram size for backoff approach to decoding paths
// default is always use subsequent paths (value = 0)
for(size_t i=0; i<m_decodeGraphs.size(); i++) {
m_decodeGraphBackoff.push_back( 0 );
}
// if specified, record maxmimum unseen n-gram size
const vector<string> &backoffVector = m_parameter->GetParam("decoding-graph-backoff");
for(size_t i=0; i<m_decodeGraphs.size() && i<backoffVector.size(); i++) {
m_decodeGraphBackoff[i] = Scan<size_t>(backoffVector[i]);
}
return true;
}
const TranslationOptionList* StaticData::FindTransOptListInCache(const DecodeGraph &decodeGraph, const Phrase &sourcePhrase) const
{
std::pair<size_t, Phrase> key(decodeGraph.GetPosition(), sourcePhrase);
#ifdef WITH_THREADS
boost::mutex::scoped_lock lock(m_transOptCacheMutex);
#endif
std::map<std::pair<size_t, Phrase>, std::pair<TranslationOptionList*,clock_t> >::iterator iter
= m_transOptCache.find(key);
if (iter == m_transOptCache.end())
return NULL;
iter->second.second = clock(); // update last used time
return iter->second.first;
}
void StaticData::ReduceTransOptCache() const
{
if (m_transOptCache.size() <= m_transOptCacheMaxSize) return; // not full
clock_t t = clock();
// find cutoff for last used time
priority_queue< clock_t > lastUsedTimes;
std::map<std::pair<size_t, Phrase>, std::pair<TranslationOptionList*,clock_t> >::iterator iter;
iter = m_transOptCache.begin();
while( iter != m_transOptCache.end() ) {
lastUsedTimes.push( iter->second.second );
iter++;
}
for( size_t i=0; i < lastUsedTimes.size()-m_transOptCacheMaxSize/2; i++ )
lastUsedTimes.pop();
clock_t cutoffLastUsedTime = lastUsedTimes.top();
// remove all old entries
iter = m_transOptCache.begin();
while( iter != m_transOptCache.end() ) {
if (iter->second.second < cutoffLastUsedTime) {
std::map<std::pair<size_t, Phrase>, std::pair<TranslationOptionList*,clock_t> >::iterator iterRemove = iter++;
delete iterRemove->second.first;
m_transOptCache.erase(iterRemove);
} else iter++;
}
VERBOSE(2,"Reduced persistent translation option cache in " << ((clock()-t)/(float)CLOCKS_PER_SEC) << " seconds." << std::endl);
}
void StaticData::AddTransOptListToCache(const DecodeGraph &decodeGraph, const Phrase &sourcePhrase, const TranslationOptionList &transOptList) const
{
if (m_transOptCacheMaxSize == 0) return;
std::pair<size_t, Phrase> key(decodeGraph.GetPosition(), sourcePhrase);
TranslationOptionList* storedTransOptList = new TranslationOptionList(transOptList);
#ifdef WITH_THREADS
boost::mutex::scoped_lock lock(m_transOptCacheMutex);
#endif
m_transOptCache[key] = make_pair( storedTransOptList, clock() );
ReduceTransOptCache();
}
void StaticData::ClearTransOptionCache() const {
map<std::pair<size_t, Phrase>, std::pair< TranslationOptionList*, clock_t > >::iterator iterCache;
for (iterCache = m_transOptCache.begin() ; iterCache != m_transOptCache.end() ; ++iterCache) {
TranslationOptionList *transOptList = iterCache->second.first;
delete transOptList;
}
}
void StaticData::ReLoadParameter()
{
assert(false); // TODO completely redo. Too many hardcoded ff
/*
m_verboseLevel = 1;
if (m_parameter->GetParam("verbose").size() == 1) {
m_verboseLevel = Scan<size_t>( m_parameter->GetParam("verbose")[0]);
}
// check whether "weight-u" is already set
if (m_parameter->isParamShortNameSpecified("u")) {
if (m_parameter->GetParamShortName("u").size() < 1 ) {
PARAM_VEC w(1,"1.0");
m_parameter->OverwriteParamShortName("u", w);
}
}
//loop over all ScoreProducer to update weights
const TranslationSystem &transSystem = GetTranslationSystem(TranslationSystem::DEFAULT);
std::vector<const ScoreProducer*>::const_iterator iterSP;
for (iterSP = transSystem.GetFeatureFunctions().begin() ; iterSP != transSystem.GetFeatureFunctions().end() ; ++iterSP) {
std::string paramShortName = (*iterSP)->GetScoreProducerWeightShortName();
vector<float> Weights = Scan<float>(m_parameter->GetParamShortName(paramShortName));
if (paramShortName == "d") { //basic distortion model takes the first weight
if ((*iterSP)->GetScoreProducerDescription() == "Distortion") {
Weights.resize(1); //take only the first element
} else { //lexicalized reordering model takes the other
Weights.erase(Weights.begin()); //remove the first element
}
// std::cerr << "this is the Distortion Score Producer -> " << (*iterSP)->GetScoreProducerDescription() << std::cerr;
// std::cerr << "this is the Distortion Score Producer; it has " << (*iterSP)->GetNumScoreComponents() << " weights"<< std::cerr;
// std::cerr << Weights << std::endl;
} else if (paramShortName == "tm") {
continue;
}
SetWeights(*iterSP, Weights);
}
// std::cerr << "There are " << m_phraseDictionary.size() << " m_phraseDictionaryfeatures" << std::endl;
const vector<float> WeightsTM = Scan<float>(m_parameter->GetParamShortName("tm"));
// std::cerr << "WeightsTM: " << WeightsTM << std::endl;
const vector<float> WeightsLM = Scan<float>(m_parameter->GetParamShortName("lm"));
// std::cerr << "WeightsLM: " << WeightsLM << std::endl;
size_t index_WeightTM = 0;
for(size_t i=0; i<transSystem.GetPhraseDictionaries().size(); ++i) {
PhraseDictionaryFeature &phraseDictionaryFeature = *m_phraseDictionary[i];
// std::cerr << "phraseDictionaryFeature.GetNumScoreComponents():" << phraseDictionaryFeature.GetNumScoreComponents() << std::endl;
// std::cerr << "phraseDictionaryFeature.GetNumInputScores():" << phraseDictionaryFeature.GetNumInputScores() << std::endl;
vector<float> tmp_weights;
for(size_t j=0; j<phraseDictionaryFeature.GetNumScoreComponents(); ++j)
tmp_weights.push_back(WeightsTM[index_WeightTM++]);
// std::cerr << tmp_weights << std::endl;
SetWeights(&phraseDictionaryFeature, tmp_weights);
}
*/
}
void StaticData::ReLoadBleuScoreFeatureParameter(float weight)
{
assert(false);
/*
//loop over ScoreProducers to update weights of BleuScoreFeature
const TranslationSystem &transSystem = GetTranslationSystem(TranslationSystem::DEFAULT);
std::vector<const ScoreProducer*>::const_iterator iterSP;
for (iterSP = transSystem.GetFeatureFunctions().begin() ; iterSP != transSystem.GetFeatureFunctions().end() ; ++iterSP) {
std::string paramShortName = (*iterSP)->GetScoreProducerWeightShortName();
if (paramShortName == "bl") {
SetWeight(*iterSP, weight);
break;
}
}
*/
}
// ScoreComponentCollection StaticData::GetAllWeightsScoreComponentCollection() const {}
// in ScoreComponentCollection.h
void StaticData::SetExecPath(const std::string &path)
{
/*
namespace fs = boost::filesystem;
fs::path full_path( fs::initial_path<fs::path>() );
full_path = fs::system_complete( fs::path( path ) );
//Without file name
m_binPath = full_path.parent_path().string();
*/
// NOT TESTED
size_t pos = path.rfind("/");
if (pos != string::npos)
{
m_binPath = path.substr(0, pos);
}
cerr << m_binPath << endl;
}
const string &StaticData::GetBinDirectory() const
{
return m_binPath;
}
float StaticData::GetWeightWordPenalty() const {
float weightWP = GetWeight(m_wpProducer);
//VERBOSE(1, "Read weightWP from translation sytem: " << weightWP << std::endl);
return weightWP;
}
float StaticData::GetWeightUnknownWordPenalty() const {
return GetWeight(m_unknownWordPenaltyProducer);
}
float StaticData::GetWeightDistortion() const {
CHECK(m_distortionScoreProducer);
return StaticData::Instance().GetWeight(m_distortionScoreProducer);
}
void StaticData::ConfigDictionaries() {
for (vector<DecodeGraph*>::const_iterator i = m_decodeGraphs.begin();
i != m_decodeGraphs.end(); ++i) {
for (DecodeGraph::const_iterator j = (*i)->begin(); j != (*i)->end(); ++j) {
const DecodeStep* step = *j;
PhraseDictionaryFeature* pdict = const_cast<PhraseDictionaryFeature*>(step->GetPhraseDictionaryFeature());
if (pdict) {
pdict->InitDictionary(NULL);
}
GenerationDictionary* gdict = const_cast<GenerationDictionary*>(step->GetGenerationDictionaryFeature());
if (gdict) {
}
}
}
}
void StaticData::InitializeForInput(const InputType& source) const {
const std::vector<FeatureFunction*> &producers = FeatureFunction::GetFeatureFunctions();
for(size_t i=0;i<producers.size();++i) {
FeatureFunction &ff = *producers[i];
ff.InitializeForInput(source);
}
}
void StaticData::CleanUpAfterSentenceProcessing(const InputType& source) const {
const std::vector<FeatureFunction*> &producers = FeatureFunction::GetFeatureFunctions();
for(size_t i=0;i<producers.size();++i) {
FeatureFunction &ff = *producers[i];
ff.CleanUpAfterSentenceProcessing(source);
}
}
void StaticData::CollectFeatureFunctions()
{
const std::vector<FeatureFunction*> &ffs = FeatureFunction::GetFeatureFunctions();
std::vector<FeatureFunction*>::const_iterator iter;
for (iter = ffs.begin(); iter != ffs.end(); ++iter) {
const FeatureFunction *ff = *iter;
cerr << ff->GetScoreProducerDescription() << endl;
const LanguageModel *lm = dynamic_cast<const LanguageModel*>(ff);
if (lm) {
LanguageModel *lmNonConst = const_cast<LanguageModel*>(lm);
m_languageModel.Add(lmNonConst);
continue;
}
const GenerationDictionary *generation = dynamic_cast<const GenerationDictionary*>(ff);
if (generation) {
m_generationDictionary.push_back(generation);
continue;
}
}
}
bool StaticData::CheckWeights() const
{
set<string> weightNames = m_parameter->GetWeightNames();
const std::vector<FeatureFunction*> &ffs = FeatureFunction::GetFeatureFunctions();
for (size_t i = 0; i < ffs.size(); ++i) {
const FeatureFunction &ff = *ffs[i];
const string &descr = ff.GetScoreProducerDescription();
set<string>::iterator iter = weightNames.find(descr);
if (iter == weightNames.end()) {
cerr << "Can't find weights for feature function " << descr << endl;
}
else {
weightNames.erase(iter);
}
}
if (!weightNames.empty()) {
cerr << "The following weights have no feature function. Maybe incorrectly spelt weights: ";
set<string>::iterator iter;
for (iter = weightNames.begin(); iter != weightNames.end(); ++iter) {
cerr << *iter << ",";
}
return false;
}
return true;
}
} // namespace
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