public Optimizable.ByGradientValue newCRFOptimizable (CRF crf, InstanceList trainingData) { return new CRFOptimizableByLabelLikelihood (crf, trainingData); } }
public Optimizable.ByGradientValue newCRFOptimizable (CRF crf, InstanceList trainingData) { return new CRFOptimizableByLabelLikelihood (crf, trainingData); } }
public Optimizable.ByGradientValue newCRFOptimizable (CRF crf, InstanceList trainingData) { return new CRFOptimizableByLabelLikelihood (crf, trainingData); } }
new CRFOptimizableByLabelLikelihood(crf, labeled); likelihood.setGaussianPriorVariance(gaussianPriorVariance); this.bfgs = new LimitedMemoryBFGS(likelihood); CRFOptimizableByLabelLikelihood likelihood = new CRFOptimizableByLabelLikelihood(crf, labeled); likelihood.setGaussianPriorVariance(gaussianPriorVariance); CRFOptimizableByEntropyRegularization regularization = new CRFOptimizableByEntropyRegularization(crf, unlabeled);
new CRFOptimizableByLabelLikelihood(crf, labeled); likelihood.setGaussianPriorVariance(gaussianPriorVariance); this.bfgs = new LimitedMemoryBFGS(likelihood); CRFOptimizableByLabelLikelihood likelihood = new CRFOptimizableByLabelLikelihood(crf, labeled); likelihood.setGaussianPriorVariance(gaussianPriorVariance); CRFOptimizableByEntropyRegularization regularization = new CRFOptimizableByEntropyRegularization(crf, unlabeled);
new CRFOptimizableByLabelLikelihood(crf, labeled); likelihood.setGaussianPriorVariance(gaussianPriorVariance); this.bfgs = new LimitedMemoryBFGS(likelihood); CRFOptimizableByLabelLikelihood likelihood = new CRFOptimizableByLabelLikelihood(crf, labeled); likelihood.setGaussianPriorVariance(gaussianPriorVariance); CRFOptimizableByEntropyRegularization regularization = new CRFOptimizableByEntropyRegularization(crf, unlabeled);
public CRFOptimizableByLabelLikelihood getOptimizableCRF (InstanceList trainingSet) { if (cachedWeightsStructureStamp != crf.weightsStructureChangeStamp) { if (!useNoWeights) { if (useSparseWeights) crf.setWeightsDimensionAsIn (trainingSet, useSomeUnsupportedTrick); else crf.setWeightsDimensionDensely (); } //reallocateSufficientStatistics(); // Not necessary here because it is done in the constructor for OptimizableCRF ocrf = null; cachedWeightsStructureStamp = crf.weightsStructureChangeStamp; } if (ocrf == null || ocrf.trainingSet != trainingSet) { //ocrf = new OptimizableCRF (crf, trainingSet); ocrf = new CRFOptimizableByLabelLikelihood(crf, trainingSet); ocrf.setGaussianPriorVariance(gaussianPriorVariance); ocrf.setHyperbolicPriorSharpness(hyperbolicPriorSharpness); ocrf.setHyperbolicPriorSlope(hyperbolicPriorSlope); ocrf.setUseHyperbolicPrior(usingHyperbolicPrior); opt = null; } return ocrf; }
public CRFOptimizableByLabelLikelihood getOptimizableCRF (InstanceList trainingSet) { if (cachedWeightsStructureStamp != crf.weightsStructureChangeStamp) { if (!useNoWeights) { if (useSparseWeights) crf.setWeightsDimensionAsIn (trainingSet, useSomeUnsupportedTrick); else crf.setWeightsDimensionDensely (); } //reallocateSufficientStatistics(); // Not necessary here because it is done in the constructor for OptimizableCRF ocrf = null; cachedWeightsStructureStamp = crf.weightsStructureChangeStamp; } if (ocrf == null || ocrf.trainingSet != trainingSet) { //ocrf = new OptimizableCRF (crf, trainingSet); ocrf = new CRFOptimizableByLabelLikelihood(crf, trainingSet); ocrf.setGaussianPriorVariance(gaussianPriorVariance); ocrf.setHyperbolicPriorSharpness(hyperbolicPriorSharpness); ocrf.setHyperbolicPriorSlope(hyperbolicPriorSlope); ocrf.setUseHyperbolicPrior(usingHyperbolicPrior); opt = null; } return ocrf; }
public CRFOptimizableByLabelLikelihood getOptimizableCRF(InstanceList trainingSet) { if (cachedWeightsStructureStamp != crf.weightsStructureChangeStamp) { if (!useNoWeights) { if (useSparseWeights) { crf.setWeightsDimensionAsIn(trainingSet, useSomeUnsupportedTrick); } else { crf.setWeightsDimensionDensely(); } } // reallocateSufficientStatistics(); // Not necessary here because it is done in the constructor for // OptimizableCRF ocrf = null; cachedWeightsStructureStamp = crf.weightsStructureChangeStamp; } if (ocrf == null || ocrf.trainingSet != trainingSet) { // ocrf = new OptimizableCRF (crf, trainingSet); ocrf = new CRFOptimizableByLabelLikelihood(crf, trainingSet); ocrf.setGaussianPriorVariance(gaussianPriorVariance); ocrf.setHyperbolicPriorSharpness(hyperbolicPriorSharpness); ocrf.setHyperbolicPriorSlope(hyperbolicPriorSlope); ocrf.setUseHyperbolicPrior(usingHyperbolicPrior); opt = null; } return ocrf; }
public void train(TextBlock textBlock) throws Exception { InstanceList trainingData = new InstanceList(getPipes()); for (TextSentence textSentence : textBlock) { Instance textInstance = new TextInstance(textSentence, getTargetAlphabet()); trainingData.addThruPipe(textInstance); } if (crf == null) { crf = new CRF(getPipes(), null); crf.addFullyConnectedStatesForLabels(); crf.setWeightsDimensionAsIn(trainingData, false); CRFOptimizableByLabelLikelihood optLabel = new CRFOptimizableByLabelLikelihood(crf, trainingData); Optimizable.ByGradientValue[] opts = new Optimizable.ByGradientValue[] { optLabel }; crfTrainer = new CRFTrainerByValueGradients(crf, opts); crfTrainer.setMaxResets(0); } crfTrainer.train(trainingData, Integer.MAX_VALUE); }
optLikelihood = new CRFOptimizableByLabelLikelihood(crf,trainingSet); ((CRFOptimizableByLabelLikelihood)optLikelihood).setGaussianPriorVariance(gpv);
optLikelihood = new CRFOptimizableByLabelLikelihood(crf,trainingSet); ((CRFOptimizableByLabelLikelihood)optLikelihood).setGaussianPriorVariance(gpv);
optLikelihood = new CRFOptimizableByLabelLikelihood(crf,trainingSet); ((CRFOptimizableByLabelLikelihood)optLikelihood).setGaussianPriorVariance(gpv);