mirror of
https://github.com/moses-smt/mosesdecoder.git
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020c71216b
git-svn-id: http://svn.statmt.org/repository/mira@3897 cc96ff50-19ce-11e0-b349-13d7f0bd23df
91 lines
2.8 KiB
C++
91 lines
2.8 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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#include "Optimiser.h"
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using namespace Moses;
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using namespace std;
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namespace Mira {
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vector<int> Perceptron::updateWeightsAnalytically(ScoreComponentCollection& currWeights,
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ScoreComponentCollection& featureValues,
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float loss,
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ScoreComponentCollection& oracleFeatureValues,
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float oracleBleuScore,
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size_t sentenceId,
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float learning_rate,
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float max_sentence_update,
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size_t rank,
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size_t epoch,
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bool controlUpdates) {
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vector<int> status(1);
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status[0] = 0;
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return status;
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}
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vector<int> Perceptron::updateWeightsHopeFear(Moses::ScoreComponentCollection& currWeights,
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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< size_t> sentenceId,
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float learning_rate,
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float max_sentence_update,
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size_t rank,
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size_t epoch,
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int updates_per_epoch,
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bool controlUpdates) {
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vector<int> status(1);
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status[0] = 0;
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return status;
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}
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vector<int> Perceptron::updateWeights(ScoreComponentCollection& currWeights,
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const vector< vector<ScoreComponentCollection> >& featureValues,
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const vector< vector<float> >& losses,
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const vector< vector<float> >& bleuScores,
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const vector< ScoreComponentCollection>& oracleFeatureValues,
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const vector< float> oracleBleuScores,
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const vector< size_t> dummy,
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float learning_rate,
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float max_sentence_update,
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size_t rank,
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size_t epoch,
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int updates_per_epoch,
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bool controlUpdates)
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{
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for (size_t i = 0; i < featureValues.size(); ++i) {
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for (size_t j = 0; j < featureValues[i].size(); ++j) {
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if (losses[i][j] > 0) {
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currWeights.MinusEquals(featureValues[i][j]);
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currWeights.PlusEquals(oracleFeatureValues[i]);
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}
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}
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}
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vector<int> status(1);
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status[0] = 0;
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return status;
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}
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}
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