mirror of
https://github.com/moses-smt/mosesdecoder.git
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161 lines
4.4 KiB
C++
161 lines
4.4 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_HOPEFEARDECODER_H
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#define MERT_HOPEFEARDECODER_H
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#include <vector>
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#include <boost/scoped_ptr.hpp>
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#include <boost/shared_ptr.hpp>
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#include "ForestRescore.h"
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#include "Hypergraph.h"
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#include "HypPackEnumerator.h"
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#include "MiraFeatureVector.h"
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#include "MiraWeightVector.h"
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//
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// Used by batch mira to get the hope, fear and model hypothesis. This wraps
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// the n-best list and lattice/hypergraph implementations
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//
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namespace MosesTuning {
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class Scorer;
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/** To be filled in by the decoder */
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struct HopeFearData {
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MiraFeatureVector modelFeatures;
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MiraFeatureVector hopeFeatures;
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MiraFeatureVector fearFeatures;
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std::vector<float> modelStats;
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std::vector<float> hopeStats;
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ValType hopeBleu;
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ValType fearBleu;
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bool hopeFearEqual;
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};
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//Abstract base class
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class HopeFearDecoder {
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public:
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//iterator methods
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virtual void reset() = 0;
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virtual void next() = 0;
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virtual bool finished() = 0;
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virtual ~HopeFearDecoder() {};
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/**
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* Calculate hope, fear and model hypotheses
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**/
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virtual void HopeFear(
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const std::vector<ValType>& backgroundBleu,
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const MiraWeightVector& wv,
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HopeFearData* hopeFear
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) = 0;
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/** Max score decoding */
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virtual void MaxModel(const AvgWeightVector& wv, std::vector<ValType>* stats)
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= 0;
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/** Calculate bleu on training set */
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ValType Evaluate(const AvgWeightVector& wv);
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protected:
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Scorer* scorer_;
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};
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/** Gets hope-fear from nbest lists */
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class NbestHopeFearDecoder : public virtual HopeFearDecoder {
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public:
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NbestHopeFearDecoder(const std::vector<std::string>& featureFiles,
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const std::vector<std::string>& scoreFiles,
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bool streaming,
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bool no_shuffle,
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bool safe_hope,
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Scorer* scorer
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);
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virtual void reset();
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virtual void next();
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virtual bool finished();
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virtual void HopeFear(
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const std::vector<ValType>& backgroundBleu,
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const MiraWeightVector& wv,
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HopeFearData* hopeFear
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);
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virtual void MaxModel(const AvgWeightVector& wv, std::vector<ValType>* stats);
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private:
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boost::scoped_ptr<HypPackEnumerator> train_;
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bool safe_hope_;
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};
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/** Gets hope-fear from hypergraphs */
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class HypergraphHopeFearDecoder : public virtual HopeFearDecoder {
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public:
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HypergraphHopeFearDecoder(
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const std::string& hypergraphDir,
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const std::vector<std::string>& referenceFiles,
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size_t num_dense,
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bool streaming,
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bool no_shuffle,
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bool safe_hope,
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size_t hg_pruning,
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const MiraWeightVector& wv,
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Scorer* scorer_
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);
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virtual void reset();
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virtual void next();
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virtual bool finished();
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virtual void HopeFear(
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const std::vector<ValType>& backgroundBleu,
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const MiraWeightVector& wv,
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HopeFearData* hopeFear
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);
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virtual void MaxModel(const AvgWeightVector& wv, std::vector<ValType>* stats);
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private:
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size_t num_dense_;
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//maps sentence Id to graph ptr
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typedef std::map<size_t, boost::shared_ptr<Graph> > GraphColl;
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GraphColl graphs_;
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std::vector<size_t> sentenceIds_;
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std::vector<size_t>::const_iterator sentenceIdIter_;
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ReferenceSet references_;
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Vocab vocab_;
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};
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};
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#endif
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