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https://github.com/moses-smt/mosesdecoder.git
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106 lines
2.6 KiB
C++
106 lines
2.6 KiB
C++
/*
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* Data.h
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* mert - Minimum Error Rate Training
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*
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* Created by Nicola Bertoldi on 13/05/08.
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*
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*/
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#ifndef MERT_DATA_H_
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#define MERT_DATA_H_
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#include <vector>
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#include <boost/shared_ptr.hpp>
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#include "Util.h"
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#include "FeatureData.h"
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#include "ScoreData.h"
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namespace MosesTuning
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{
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class Scorer;
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typedef boost::shared_ptr<ScoreData> ScoreDataHandle;
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typedef boost::shared_ptr<FeatureData> FeatureDataHandle;
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// NOTE: there is no copy constructor implemented, so only the
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// compiler synthesised shallow copy is available.
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class Data
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{
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private:
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Scorer* m_scorer;
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std::string m_score_type;
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std::size_t m_num_scores;
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bool m_sparse_flag;
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ScoreDataHandle m_score_data;
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FeatureDataHandle m_feature_data;
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public:
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explicit Data(Scorer* scorer);
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Data();
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void clear() {
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m_score_data->clear();
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m_feature_data->clear();
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}
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ScoreDataHandle getScoreData() { return m_score_data; }
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FeatureDataHandle getFeatureData() { return m_feature_data; }
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Scorer* getScorer() { return m_scorer; }
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std::size_t NumberOfFeatures() const {
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return m_feature_data->NumberOfFeatures();
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}
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void NumberOfFeatures(std::size_t v) { m_feature_data->NumberOfFeatures(v); }
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std::string Features() const { return m_feature_data->Features(); }
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void Features(const std::string &f) { m_feature_data->Features(f); }
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bool hasSparseFeatures() const { return m_sparse_flag; }
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void mergeSparseFeatures();
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void loadNBest(const std::string &file);
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void load(const std::string &featfile, const std::string &scorefile);
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void save(const std::string &featfile, const std::string &scorefile, bool bin=false);
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//ADDED BY TS
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void removeDuplicates();
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//END_ADDED
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inline bool existsFeatureNames() const {
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return m_feature_data->existsFeatureNames();
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}
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inline std::string getFeatureName(std::size_t idx) const {
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return m_feature_data->getFeatureName(idx);
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}
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inline std::size_t getFeatureIndex(const std::string& name) const {
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return m_feature_data->getFeatureIndex(name);
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}
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/**
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* Create shard_count shards. If shard_size == 0, then the shards are non-overlapping
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* and exhaust the data. If 0 < shard_size <= 1, then shards are chosen by sampling
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* the data (with replacement) and shard_size is interpreted as the proportion
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* of the total size.
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*/
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void createShards(std::size_t shard_count, float shard_size, const std::string& scorerconfig,
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std::vector<Data>& shards);
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// Helper functions for loadnbest();
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void InitFeatureMap(const std::string& str);
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void AddFeatures(const std::string& str,
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const std::string& sentence_index);
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};
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}
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#endif // MERT_DATA_H_
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