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/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2010 University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#ifndef _MIRA_OPTIMISER_H_
#define _MIRA_OPTIMISER_H_
#include <vector>
#include "ScoreComponentCollection.h"
namespace Mira {
class Optimiser {
public:
Optimiser() {}
virtual size_t updateWeightsHopeFear(
Moses::ScoreComponentCollection& currWeights,
Moses::ScoreComponentCollection& weightUpdate,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
const std::vector<std::vector<float> >& bleuScoresHope,
const std::vector<std::vector<float> >& bleuScoresFear,
const std::vector<std::vector<float> >& modelScoresHope,
const std::vector<std::vector<float> >& modelScoresFear,
float learning_rate,
size_t rank,
size_t epoch,
int updatePosition = -1) = 0;
};
class Perceptron : public Optimiser {
public:
virtual size_t updateWeightsHopeFear(
Moses::ScoreComponentCollection& currWeights,
Moses::ScoreComponentCollection& weightUpdate,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
const std::vector<std::vector<float> >& bleuScoresHope,
const std::vector<std::vector<float> >& bleuScoresFear,
const std::vector<std::vector<float> >& modelScoresHope,
const std::vector<std::vector<float> >& modelScoresFear,
float learning_rate,
size_t rank,
size_t epoch,
int updatePosition = -1);
};
class MiraOptimiser : public Optimiser {
public:
MiraOptimiser() :
Optimiser() { }
MiraOptimiser(float slack, bool scale_margin, bool scale_margin_precision,
bool scale_update, bool scale_update_precision, bool boost, bool normaliseMargin, float sigmoidParam) :
Optimiser(),
m_slack(slack),
m_scale_margin(scale_margin),
m_scale_margin_precision(scale_margin_precision),
m_scale_update(scale_update),
m_scale_update_precision(scale_update_precision),
m_precision(1),
m_boost(boost),
m_normaliseMargin(normaliseMargin),
m_sigmoidParam(sigmoidParam) { }
size_t updateWeights(Moses::ScoreComponentCollection& currWeights,
Moses::ScoreComponentCollection& weightUpdate,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValues,
const std::vector<std::vector<float> >& losses,
const std::vector<std::vector<float> >& bleuScores,
const std::vector<std::vector<float> >& modelScores,
const std::vector< Moses::ScoreComponentCollection>& oracleFeatureValues,
const std::vector< float> oracleBleuScores,
const std::vector< float> oracleModelScores,
float learning_rate,
size_t rank,
size_t epoch);
virtual size_t updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
Moses::ScoreComponentCollection& weightUpdate,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
const std::vector<std::vector<float> >& bleuScoresHope,
const std::vector<std::vector<float> >& bleuScoresFear,
const std::vector<std::vector<float> >& modelScoresHope,
const std::vector<std::vector<float> >& modelScoresFear,
float learning_rate,
size_t rank,
size_t epoch,
int updatePosition = -1);
size_t updateWeightsAnalytically(Moses::ScoreComponentCollection& currWeights,
Moses::ScoreComponentCollection& weightUpdate,
Moses::ScoreComponentCollection& featureValuesHope,
Moses::ScoreComponentCollection& featureValuesFear,
float bleuScoreHope,
float bleuScoreFear,
float modelScoreHope,
float modelScoreFear,
float learning_rate,
size_t rank,
size_t epoch);
size_t updateWeightsRankModel(Moses::ScoreComponentCollection& currWeights,
Moses::ScoreComponentCollection& weightUpdate,
const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValues,
const std::vector<std::vector<float> >& bleuScores,
const std::vector<std::vector<float> >& modelScores,
float learning_rate,
size_t rank,
size_t epoch);
void setSlack(float slack) {
m_slack = slack;
}
void setPrecision(float precision) {
m_precision = precision;
}
private:
// regularise Hildreth updates
float m_slack;
// scale margin with BLEU score or precision
bool m_scale_margin, m_scale_margin_precision;
// scale update with oracle BLEU score or precision
bool m_scale_update, m_scale_update_precision;
float m_precision;
// boosting of updates on misranked candidates
bool m_boost;
// squash margin between 0 and 1 (or depending on m_sigmoidParam)
bool m_normaliseMargin;
// y=sigmoidParam is the axis that this sigmoid approaches
float m_sigmoidParam ;
};
}
#endif
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