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329 lines
11 KiB
C++
329 lines
11 KiB
C++
// $Id$
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// vim:tabstop=2
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/***********************************************************************
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Moses - factored phrase-based language decoder
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Copyright (C) 2006 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 moses_Hypothesis_h
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#define moses_Hypothesis_h
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#include <iostream>
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#include <memory>
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#include <boost/scoped_ptr.hpp>
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#include <vector>
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#include "Phrase.h"
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#include "TypeDef.h"
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#include "WordsBitmap.h"
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#include "Sentence.h"
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#include "Phrase.h"
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#include "GenerationDictionary.h"
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#include "ScoreComponentCollection.h"
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#include "InputType.h"
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#include "ObjectPool.h"
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namespace Moses
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{
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class SquareMatrix;
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class StaticData;
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class TranslationOption;
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class WordsRange;
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class Hypothesis;
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class FFState;
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class StatelessFeatureFunction;
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class StatefulFeatureFunction;
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class Manager;
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typedef std::vector<Hypothesis*> ArcList;
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/** Used to store a state in the beam search
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for the best translation. With its link back to the previous hypothesis
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m_prevHypo, we can trace back to the sentence start to read of the
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(partial) translation to this point.
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The expansion of hypotheses is handled in the class Manager, which
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stores active hypothesis in the search in hypothesis stacks.
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***/
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class Hypothesis
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{
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friend std::ostream& operator<<(std::ostream&, const Hypothesis&);
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protected:
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static ObjectPool<Hypothesis> s_objectPool;
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const Hypothesis* m_prevHypo; /*! backpointer to previous hypothesis (from which this one was created) */
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// const Phrase &m_targetPhrase; /*! target phrase being created at the current decoding step */
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WordsBitmap m_sourceCompleted; /*! keeps track of which words have been translated so far */
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//TODO: how to integrate this into confusion network framework; what if
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//it's a confusion network in the end???
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InputType const& m_sourceInput;
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WordsRange m_currSourceWordsRange; /*! source word positions of the last phrase that was used to create this hypothesis */
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WordsRange m_currTargetWordsRange; /*! target word positions of the last phrase that was used to create this hypothesis */
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bool m_wordDeleted;
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float m_totalScore; /*! score so far */
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float m_futureScore; /*! estimated future cost to translate rest of sentence */
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/*! sum of scores of this hypothesis, and previous hypotheses. Lazily initialised. */
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mutable boost::scoped_ptr<ScoreComponentCollection> m_scoreBreakdown;
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ScoreComponentCollection m_currScoreBreakdown; /*! scores for this hypothesis only */
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std::vector<const FFState*> m_ffStates;
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const Hypothesis *m_winningHypo;
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ArcList *m_arcList; /*! all arcs that end at the same trellis point as this hypothesis */
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const TranslationOption &m_transOpt;
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Manager& m_manager;
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int m_id; /*! numeric ID of this hypothesis, used for logging */
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/*! used by initial seeding of the translation process */
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Hypothesis(Manager& manager, InputType const& source, const TranslationOption &initialTransOpt);
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/*! used when creating a new hypothesis using a translation option (phrase translation) */
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Hypothesis(const Hypothesis &prevHypo, const TranslationOption &transOpt);
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public:
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static ObjectPool<Hypothesis> &GetObjectPool() {
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return s_objectPool;
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}
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~Hypothesis();
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/** return the subclass of Hypothesis most appropriate to the given translation option */
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static Hypothesis* Create(const Hypothesis &prevHypo, const TranslationOption &transOpt);
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static Hypothesis* Create(Manager& manager, const WordsBitmap &initialCoverage);
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/** return the subclass of Hypothesis most appropriate to the given target phrase */
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static Hypothesis* Create(Manager& manager, InputType const& source, const TranslationOption &initialTransOpt);
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/** return the subclass of Hypothesis most appropriate to the given translation option */
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Hypothesis* CreateNext(const TranslationOption &transOpt) const;
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void PrintHypothesis() const;
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const InputType& GetInput() const {
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return m_sourceInput;
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}
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/** return target phrase used to create this hypothesis */
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// const Phrase &GetCurrTargetPhrase() const
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const TargetPhrase &GetCurrTargetPhrase() const;
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/** return input positions covered by the translation option (phrasal translation) used to create this hypothesis */
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inline const WordsRange &GetCurrSourceWordsRange() const {
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return m_currSourceWordsRange;
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}
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inline const WordsRange &GetCurrTargetWordsRange() const {
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return m_currTargetWordsRange;
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}
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Manager& GetManager() const {
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return m_manager;
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}
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/** output length of the translation option used to create this hypothesis */
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inline size_t GetCurrTargetLength() const {
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return m_currTargetWordsRange.GetNumWordsCovered();
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}
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void EvaluateWhenApplied(const SquareMatrix &futureScore);
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int GetId()const {
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return m_id;
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}
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const Hypothesis* GetPrevHypo() const;
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/** length of the partial translation (from the start of the sentence) */
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inline size_t GetSize() const {
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return m_currTargetWordsRange.GetEndPos() + 1;
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}
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std::string GetSourcePhraseStringRep(const std::vector<FactorType> factorsToPrint) const;
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std::string GetTargetPhraseStringRep(const std::vector<FactorType> factorsToPrint) const;
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std::string GetSourcePhraseStringRep() const;
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std::string GetTargetPhraseStringRep() const;
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/** curr - pos is relative from CURRENT hypothesis's starting index
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* (ie, start of sentence would be some negative number, which is
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* not allowed- USE WITH CAUTION) */
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inline const Word &GetCurrWord(size_t pos) const {
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return GetCurrTargetPhrase().GetWord(pos);
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}
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inline const Factor *GetCurrFactor(size_t pos, FactorType factorType) const {
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return GetCurrTargetPhrase().GetFactor(pos, factorType);
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}
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/** recursive - pos is relative from start of sentence */
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inline const Word &GetWord(size_t pos) const {
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const Hypothesis *hypo = this;
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while (pos < hypo->GetCurrTargetWordsRange().GetStartPos()) {
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hypo = hypo->GetPrevHypo();
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UTIL_THROW_IF2(hypo == NULL, "Previous hypothesis should not be NULL");
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}
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return hypo->GetCurrWord(pos - hypo->GetCurrTargetWordsRange().GetStartPos());
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}
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inline const Factor* GetFactor(size_t pos, FactorType factorType) const {
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return GetWord(pos)[factorType];
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}
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/***
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* \return The bitmap of source words we cover
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*/
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inline const WordsBitmap &GetWordsBitmap() const {
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return m_sourceCompleted;
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}
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inline bool IsSourceCompleted() const {
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return m_sourceCompleted.IsComplete();
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}
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int RecombineCompare(const Hypothesis &compare) const;
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void GetOutputPhrase(Phrase &out) const;
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void ToStream(std::ostream& out) const {
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Phrase ret;
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GetOutputPhrase(ret);
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out << ret;
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}
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void ToStringStream(std::stringstream& out) const {
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if (m_prevHypo != NULL) {
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m_prevHypo->ToStream(out);
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}
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out << (Phrase) GetCurrTargetPhrase();
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}
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std::string GetOutputString() const {
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std::stringstream out;
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ToStringStream(out);
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return out.str();
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}
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TO_STRING();
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inline void SetWinningHypo(const Hypothesis *hypo) {
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m_winningHypo = hypo;
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}
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inline const Hypothesis *GetWinningHypo() const {
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return m_winningHypo;
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}
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void AddArc(Hypothesis *loserHypo);
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void CleanupArcList();
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//! returns a list alternative previous hypotheses (or NULL if n-best support is disabled)
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inline const ArcList* GetArcList() const {
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return m_arcList;
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}
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const ScoreComponentCollection& GetScoreBreakdown() const {
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if (!m_scoreBreakdown.get()) {
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m_scoreBreakdown.reset(new ScoreComponentCollection());
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m_scoreBreakdown->PlusEquals(m_currScoreBreakdown);
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if (m_prevHypo) {
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m_scoreBreakdown->PlusEquals(m_prevHypo->GetScoreBreakdown());
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}
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}
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return *(m_scoreBreakdown.get());
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}
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float GetTotalScore() const {
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return m_totalScore;
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}
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float GetScore() const {
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return m_totalScore-m_futureScore;
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}
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const FFState* GetFFState(int idx) const {
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return m_ffStates[idx];
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}
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void SetFFState(int idx, FFState* state) {
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m_ffStates[idx] = state;
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}
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// Added by oliver.wilson@ed.ac.uk for async lm stuff.
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void EvaluateWhenApplied(const StatefulFeatureFunction &sfff, int state_idx);
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void EvaluateWhenApplied(const StatelessFeatureFunction &slff);
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//! target span that trans opt would populate if applied to this hypo. Used for alignment check
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size_t GetNextStartPos(const TranslationOption &transOpt) const;
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std::vector<std::vector<unsigned int> > *GetLMStats() const {
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return NULL;
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}
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const TranslationOption &GetTranslationOption() const {
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return m_transOpt;
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}
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void OutputAlignment(std::ostream &out) const;
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static void OutputAlignment(std::ostream &out, const std::vector<const Hypothesis *> &edges);
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static void OutputAlignment(std::ostream &out, const Moses::AlignmentInfo &ai, size_t sourceOffset, size_t targetOffset);
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void OutputInput(std::ostream& os) const;
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static void OutputInput(std::vector<const Phrase*>& map, const Hypothesis* hypo);
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void OutputBestSurface(std::ostream &out, const std::vector<Moses::FactorType> &outputFactorOrder, char reportSegmentation, bool reportAllFactors) const;
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void OutputSurface(std::ostream &out, const Hypothesis &edge, const std::vector<FactorType> &outputFactorOrder,
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char reportSegmentation, bool reportAllFactors) const;
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// creates a map of TARGET positions which should be replaced by word using placeholder
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std::map<size_t, const Moses::Factor*> GetPlaceholders(const Moses::Hypothesis &hypo, Moses::FactorType placeholderFactor) const;
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};
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std::ostream& operator<<(std::ostream& out, const Hypothesis& hypothesis);
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// sorting helper
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struct CompareHypothesisTotalScore {
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bool operator()(const Hypothesis* hypo1, const Hypothesis* hypo2) const {
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return hypo1->GetTotalScore() > hypo2->GetTotalScore();
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}
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};
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#ifdef USE_HYPO_POOL
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#define FREEHYPO(hypo) \
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{ \
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ObjectPool<Hypothesis> &pool = Hypothesis::GetObjectPool(); \
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pool.freeObject(hypo); \
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} \
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#else
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#define FREEHYPO(hypo) delete hypo
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#endif
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/** defines less-than relation on hypotheses.
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* The particular order is not important for us, we need just to figure out
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* which hypothesis are equal based on:
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* the last n-1 target words are the same
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* and the covers (source words translated) are the same
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* Directly using RecombineCompare is unreliable because the Compare methods
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* of some states are based on archictecture-dependent pointer comparisons.
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* That's why we use the hypothesis IDs instead.
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*/
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class HypothesisRecombinationOrderer
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{
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public:
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bool operator()(const Hypothesis* hypoA, const Hypothesis* hypoB) const {
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return (hypoA->RecombineCompare(*hypoB) < 0);
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}
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};
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}
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#endif
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