/** * Adjust the parameters by default learning rate according to the gradient * of this single Instance, and return the true label sequence likelihood. */ public double trainIncrementalLikelihood(Instance trainingInstance) { return trainIncrementalLikelihood(trainingInstance, learningRate); }
/** * Adjust the parameters by default learning rate according to the gradient * of this single Instance, and return the true label sequence likelihood. */ public double trainIncrementalLikelihood(Instance trainingInstance) { return trainIncrementalLikelihood(trainingInstance, learningRate); }
/** * Adjust the parameters by default learning rate according to the gradient * of this single Instance, and return the true label sequence likelihood. */ public double trainIncrementalLikelihood(Instance trainingInstance) { return trainIncrementalLikelihood(trainingInstance, learningRate); }
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; }
private double trainSample(InstanceList trainingSample, int numIterations, double rate) { double lambda = trainingSample.size(); double t = 1 / (lambda * rate); double loglik = Double.NEGATIVE_INFINITY; for (int i = 0; i < numIterations; i++) { loglik = 0.0; for (int j = 0; j < trainingSample.size(); j++) { rate = 1 / (lambda * t); loglik += trainIncrementalLikelihood(trainingSample.get(j), rate); t += 1.0; } } return loglik; }
private double trainSample(InstanceList trainingSample, int numIterations, double rate) { double lambda = trainingSample.size(); double t = 1 / (lambda * rate); double loglik = Double.NEGATIVE_INFINITY; for (int i = 0; i < numIterations; i++) { loglik = 0.0; for (int j = 0; j < trainingSample.size(); j++) { rate = 1 / (lambda * t); loglik += trainIncrementalLikelihood(trainingSample.get(j), rate); t += 1.0; } } return loglik; }
private double trainSample(InstanceList trainingSample, int numIterations, double rate) { double lambda = trainingSample.size(); double t = 1 / (lambda * rate); double loglik = Double.NEGATIVE_INFINITY; for (int i = 0; i < numIterations; i++) { loglik = 0.0; for (int j = 0; j < trainingSample.size(); j++) { rate = 1 / (lambda * t); loglik += trainIncrementalLikelihood(trainingSample.get(j), rate); t += 1.0; } } return loglik; }
for (int i = 0; i < trainingSet.size(); i++) { learningRate = 1.0 / (lambda * t); loglik += trainIncrementalLikelihood(trainingSet .get(trainingIndices.get(i))); t += 1.0;
for (int i = 0; i < trainingSet.size(); i++) { learningRate = 1.0 / (lambda * t); loglik += trainIncrementalLikelihood(trainingSet .get(trainingIndices.get(i))); t += 1.0;
for (int i = 0; i < trainingSet.size(); i++) { learningRate = 1.0 / (lambda * t); loglik += trainIncrementalLikelihood(trainingSet .get(trainingIndices.get(i))); t += 1.0;