mirror of
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126 lines
4.0 KiB
C++
126 lines
4.0 KiB
C++
/***********************************************************************
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Moses - factored phrase-based language decoder
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Copyright (C) 2014- University of Edinburgh
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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***********************************************************************/
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#ifndef MERT_FOREST_RESCORE_H
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#define MERT_FOREST_RESCORE_H
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#include <valarray>
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#include <vector>
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#include <boost/unordered_set.hpp>
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#include "BleuScorer.h"
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#include "Hypergraph.h"
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namespace MosesTuning
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{
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std::ostream& operator<<(std::ostream& out, const WordVec& wordVec);
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struct NgramHash : public std::unary_function<const WordVec&, std::size_t> {
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std::size_t operator()(const WordVec& ngram) const {
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return util::MurmurHashNative(&(ngram[0]), ngram.size() * sizeof(WordVec::value_type));
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}
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};
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struct NgramEquals : public std::binary_function<const WordVec&, const WordVec&, bool> {
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bool operator()(const WordVec& first, const WordVec& second) const {
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if (first.size() != second.size()) return false;
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return memcmp(&(first[0]), &(second[0]), first.size() * sizeof(WordVec::value_type)) == 0;
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}
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};
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typedef boost::unordered_map<WordVec, size_t, NgramHash, NgramEquals> NgramCounter;
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class ReferenceSet
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{
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public:
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void AddLine(size_t sentenceId, const StringPiece& line, Vocab& vocab);
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void Load(const std::vector<std::string>& files, Vocab& vocab);
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size_t NgramMatches(size_t sentenceId, const WordVec&, bool clip) const;
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size_t Length(size_t sentenceId) const {
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return lengths_[sentenceId];
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}
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private:
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//ngrams to (clipped,unclipped) counts
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typedef boost::unordered_map<WordVec, std::pair<std::size_t,std::size_t>, NgramHash,NgramEquals> NgramMap;
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std::vector<NgramMap> ngramCounts_;
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std::vector<size_t> lengths_;
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};
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struct VertexState {
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VertexState();
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std::vector<FeatureStatsType> bleuStats;
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WordVec leftContext;
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WordVec rightContext;
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size_t targetLength;
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};
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/**
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* Used to score an rule (ie edge) when we are applying it.
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**/
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class HgBleuScorer
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{
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public:
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HgBleuScorer(const ReferenceSet& references, const Graph& graph, size_t sentenceId, const std::vector<FeatureStatsType>& backgroundBleu):
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references_(references), sentenceId_(sentenceId), graph_(graph), backgroundBleu_(backgroundBleu),
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backgroundRefLength_(backgroundBleu[kBleuNgramOrder*2]) {
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vertexStates_.resize(graph.VertexSize());
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totalSourceLength_ = graph.GetVertex(graph.VertexSize()-1).SourceCovered();
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}
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FeatureStatsType Score(const Edge& edge, const Vertex& head, std::vector<FeatureStatsType>& bleuStats) ;
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void UpdateState(const Edge& winnerEdge, size_t vertexId, const std::vector<FeatureStatsType>& bleuStats);
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private:
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const ReferenceSet& references_;
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std::vector<VertexState> vertexStates_;
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size_t sentenceId_;
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size_t totalSourceLength_;
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const Graph& graph_;
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std::vector<FeatureStatsType> backgroundBleu_;
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FeatureStatsType backgroundRefLength_;
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void UpdateMatches(const NgramCounter& counter, std::vector<FeatureStatsType>& bleuStats) const;
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size_t GetTargetLength(const Edge& edge) const;
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};
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struct HgHypothesis {
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SparseVector featureVector;
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WordVec text;
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std::vector<FeatureStatsType> bleuStats;
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};
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void Viterbi(const Graph& graph, const SparseVector& weights, float bleuWeight, const ReferenceSet& references, size_t sentenceId, const std::vector<FeatureStatsType>& backgroundBleu, HgHypothesis* bestHypo);
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};
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#endif
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