public boolean train(InstanceList trainingSet, int numIterations, int numIterationsBetweenEvaluation) { assert (expectations.structureMatches(crf.parameters)); assert (constraints.structureMatches(crf.parameters)); lambda = 1.0 / trainingSet.size(); t = 1.0 / (lambda * learningRate);
public boolean train(InstanceList trainingSet, int numIterations, int numIterationsBetweenEvaluation) { assert (expectations.structureMatches(crf.parameters)); assert (constraints.structureMatches(crf.parameters)); lambda = 1.0 / trainingSet.size(); t = 1.0 / (lambda * learningRate);
public boolean train(InstanceList trainingSet, int numIterations, int numIterationsBetweenEvaluation) { assert (expectations.structureMatches(crf.parameters)); assert (constraints.structureMatches(crf.parameters)); lambda = 1.0 / trainingSet.size(); t = 1.0 / (lambda * learningRate);
assert (expectations.structureMatches(crf.parameters)); expectations.zero();
assert (expectations.structureMatches(crf.parameters)); expectations.zero();
assert (expectations.structureMatches(crf.parameters)); expectations.zero();
protected void gatherConstraints (InstanceList ilist) { // Set the constraints by running forward-backward with the *output // label sequence provided*, thus restricting it to only those // paths that agree with the label sequence. // Zero the constraints[] // Reset constraints[] to zero before we fill them again assert (constraints.structureMatches(crf.parameters)); constraints.zero(); for (Instance instance : ilist) { FeatureVectorSequence input = (FeatureVectorSequence) instance.getData(); FeatureSequence output = (FeatureSequence) instance.getTarget(); double instanceWeight = ilist.getInstanceWeight(instance); //System.out.println ("Constraint-gathering on instance "+i+" of "+ilist.size()); Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight); new SumLatticeDefault (this.crf, input, output, incrementor); } // System.out.println ("testing Value and Gradient"); // TestOptimizable.testValueAndGradientCurrentParameters (this); }
protected void gatherConstraints (InstanceList ilist) { // Set the constraints by running forward-backward with the *output // label sequence provided*, thus restricting it to only those // paths that agree with the label sequence. // Zero the constraints[] // Reset constraints[] to zero before we fill them again assert (constraints.structureMatches(crf.parameters)); constraints.zero(); for (Instance instance : ilist) { FeatureVectorSequence input = (FeatureVectorSequence) instance.getData(); FeatureSequence output = (FeatureSequence) instance.getTarget(); double instanceWeight = ilist.getInstanceWeight(instance); //System.out.println ("Constraint-gathering on instance "+i+" of "+ilist.size()); Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight); new SumLatticeDefault (this.crf, input, output, incrementor); } // System.out.println ("testing Value and Gradient"); // TestOptimizable.testValueAndGradientCurrentParameters (this); }
/** * Set the constraints by running forward-backward with the <i>output label * sequence provided</i>, thus restricting it to only those paths that agree with * the label sequence. */ protected void gatherConstraints(InstanceList ilist) { logger.info("Gathering constraints..."); assert (constraints.structureMatches(crf.parameters)); constraints.zero(); for (Instance instance : ilist) { FeatureVectorSequence input = (FeatureVectorSequence) instance.getData(); FeatureSequence output = (FeatureSequence) instance.getTarget(); double instanceWeight = ilist.getInstanceWeight(instance); Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight); new SumLatticeDefault (this.crf, input, output, incrementor); } constraints.assertNotNaNOrInfinite(); }
/** * Set the constraints by running forward-backward with the <i>output label * sequence provided</i>, thus restricting it to only those paths that agree with * the label sequence. */ protected void gatherConstraints(InstanceList ilist) { logger.info("Gathering constraints..."); assert (constraints.structureMatches(crf.parameters)); constraints.zero(); for (Instance instance : ilist) { FeatureVectorSequence input = (FeatureVectorSequence) instance.getData(); FeatureSequence output = (FeatureSequence) instance.getTarget(); double instanceWeight = ilist.getInstanceWeight(instance); Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight); new SumLatticeDefault (this.crf, input, output, incrementor); } constraints.assertNotNaNOrInfinite(); }
protected void gatherConstraints (InstanceList ilist) { // Set the constraints by running forward-backward with the *output // label sequence provided*, thus restricting it to only those // paths that agree with the label sequence. // Zero the constraints[] // Reset constraints[] to zero before we fill them again assert (constraints.structureMatches(crf.parameters)); constraints.zero(); for (Instance instance : ilist) { FeatureVectorSequence input = (FeatureVectorSequence) instance.getData(); FeatureSequence output = (FeatureSequence) instance.getTarget(); double instanceWeight = ilist.getInstanceWeight(instance); //System.out.println ("Constraint-gathering on instance "+i+" of "+ilist.size()); Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight); new SumLatticeDefault (this.crf, input, output, incrementor); } // System.out.println ("testing Value and Gradient"); // TestOptimizable.testValueAndGradientCurrentParameters (this); }
/** * Set the constraints by running forward-backward with the <i>output label * sequence provided</i>, thus restricting it to only those paths that agree with * the label sequence. */ protected void gatherConstraints(InstanceList ilist) { logger.info("Gathering constraints..."); assert (constraints.structureMatches(crf.parameters)); constraints.zero(); for (Instance instance : ilist) { FeatureVectorSequence input = (FeatureVectorSequence) instance.getData(); FeatureSequence output = (FeatureSequence) instance.getTarget(); double instanceWeight = ilist.getInstanceWeight(instance); Transducer.Incrementor incrementor = instanceWeight == 1.0 ? constraints.new Incrementor() : constraints.new WeightedIncrementor(instanceWeight); new SumLatticeDefault (this.crf, input, output, incrementor); } constraints.assertNotNaNOrInfinite(); }
public boolean trainIncremental(Instance trainingInstance) { assert (expectations.structureMatches(crf.parameters)); trainIncrementalLikelihood(trainingInstance); return false; }
public boolean trainIncremental(Instance trainingInstance) { assert (expectations.structureMatches(crf.parameters)); trainIncrementalLikelihood(trainingInstance); return false; }
public boolean trainIncremental(Instance trainingInstance) { assert (expectations.structureMatches(crf.parameters)); trainIncrementalLikelihood(trainingInstance); return false; }