mirror of
https://github.com/moses-smt/mosesdecoder.git
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175 lines
6.4 KiB
C++
175 lines
6.4 KiB
C++
/***********************************************************************
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Moses - factored phrase-based language decoder
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Copyright (C) 2010 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 _MIRA_OPTIMISER_H_
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#define _MIRA_OPTIMISER_H_
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#include <vector>
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#include "moses/ScoreComponentCollection.h"
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namespace Mira {
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class Optimiser {
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public:
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Optimiser() {}
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virtual size_t updateWeightsHopeFear(
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Moses::ScoreComponentCollection& weightUpdate,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
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const std::vector<std::vector<float> >& bleuScoresHope,
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const std::vector<std::vector<float> >& bleuScoresFear,
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const std::vector<std::vector<float> >& modelScoresHope,
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const std::vector<std::vector<float> >& modelScoresFear,
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float learning_rate,
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size_t rank,
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size_t epoch,
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int updatePosition = -1) = 0;
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};
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class Perceptron : public Optimiser {
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public:
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virtual size_t updateWeightsHopeFear(
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Moses::ScoreComponentCollection& weightUpdate,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
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const std::vector<std::vector<float> >& bleuScoresHope,
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const std::vector<std::vector<float> >& bleuScoresFear,
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const std::vector<std::vector<float> >& modelScoresHope,
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const std::vector<std::vector<float> >& modelScoresFear,
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float learning_rate,
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size_t rank,
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size_t epoch,
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int updatePosition = -1);
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};
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class MiraOptimiser : public Optimiser {
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public:
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MiraOptimiser() :
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Optimiser() { }
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MiraOptimiser(
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float slack, bool scale_margin, bool scale_margin_precision,
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bool scale_update, bool scale_update_precision, bool boost, bool normaliseMargin, float sigmoidParam) :
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Optimiser(),
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m_slack(slack),
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m_scale_margin(scale_margin),
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m_scale_margin_precision(scale_margin_precision),
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m_scale_update(scale_update),
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m_scale_update_precision(scale_update_precision),
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m_precision(1),
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m_boost(boost),
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m_normaliseMargin(normaliseMargin),
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m_sigmoidParam(sigmoidParam) { }
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size_t updateWeights(
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Moses::ScoreComponentCollection& weightUpdate,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValues,
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const std::vector<std::vector<float> >& losses,
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const std::vector<std::vector<float> >& bleuScores,
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const std::vector<std::vector<float> >& modelScores,
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const std::vector< Moses::ScoreComponentCollection>& oracleFeatureValues,
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const std::vector< float> oracleBleuScores,
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const std::vector< float> oracleModelScores,
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float learning_rate,
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size_t rank,
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size_t epoch);
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virtual size_t updateWeightsHopeFear(
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Moses::ScoreComponentCollection& weightUpdate,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
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const std::vector<std::vector<float> >& bleuScoresHope,
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const std::vector<std::vector<float> >& bleuScoresFear,
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const std::vector<std::vector<float> >& modelScoresHope,
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const std::vector<std::vector<float> >& modelScoresFear,
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float learning_rate,
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size_t rank,
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size_t epoch,
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int updatePosition = -1);
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size_t updateWeightsHopeFearSelective(
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Moses::ScoreComponentCollection& weightUpdate,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
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const std::vector<std::vector<float> >& bleuScoresHope,
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const std::vector<std::vector<float> >& bleuScoresFear,
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const std::vector<std::vector<float> >& modelScoresHope,
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const std::vector<std::vector<float> >& modelScoresFear,
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float learning_rate,
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size_t rank,
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size_t epoch,
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int updatePosition = -1);
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size_t updateWeightsHopeFearSummed(
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Moses::ScoreComponentCollection& weightUpdate,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
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const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
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const std::vector<std::vector<float> >& bleuScoresHope,
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const std::vector<std::vector<float> >& bleuScoresFear,
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const std::vector<std::vector<float> >& modelScoresHope,
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const std::vector<std::vector<float> >& modelScoresFear,
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float learning_rate,
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size_t rank,
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size_t epoch,
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bool rescaleSlack,
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bool makePairs);
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size_t updateWeightsAnalytically(
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Moses::ScoreComponentCollection& weightUpdate,
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Moses::ScoreComponentCollection& featureValuesHope,
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Moses::ScoreComponentCollection& featureValuesFear,
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float bleuScoreHope,
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float bleuScoreFear,
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float modelScoreHope,
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float modelScoreFear,
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float learning_rate,
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size_t rank,
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size_t epoch);
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void setSlack(float slack) {
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m_slack = slack;
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}
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void setPrecision(float precision) {
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m_precision = precision;
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}
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private:
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// regularise Hildreth updates
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float m_slack;
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// scale margin with BLEU score or precision
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bool m_scale_margin, m_scale_margin_precision;
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// scale update with oracle BLEU score or precision
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bool m_scale_update, m_scale_update_precision;
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float m_precision;
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// boosting of updates on misranked candidates
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bool m_boost;
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// squash margin between 0 and 1 (or depending on m_sigmoidParam)
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bool m_normaliseMargin;
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// y=sigmoidParam is the axis that this sigmoid approaches
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float m_sigmoidParam ;
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};
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}
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#endif
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