/** Return a tool for evaluating the marginal probability of new documents * under this model */ public MarginalProbEstimator getProbEstimator() { return new MarginalProbEstimator(numTopics, alpha, alphaSum, beta, typeTopicCounts, tokensPerTopic); }
MarginalProbEstimator.read(new File(evaluatorFilename.value)); evaluator.setPrintWords(showWords.value); evaluator.setRandomSeed(randomSeed.value); outputStream.println(evaluator.evaluateLeftToRight(instances, numParticles.value, usingResampling.value, docProbabilityStream));
MarginalProbEstimator.read(new File(evaluatorFilename.value)); outputStream.println(evaluator.evaluateLeftToRight(instances, numParticles.value, usingResampling.value, docProbabilityStream));
for (int particle = 0; particle < numParticles; particle++) { particleProbabilities[particle] = leftToRight(tokenSequence, usingResampling);
MarginalProbEstimator.read(new File(evaluatorFilename.value)); evaluator.setPrintWords(showWords.value); evaluator.setRandomSeed(randomSeed.value); outputStream.println(evaluator.evaluateLeftToRight(instances, numParticles.value, usingResampling.value, docProbabilityStream));
for (int particle = 0; particle < numParticles; particle++) { particleProbabilities[particle] = leftToRight(tokenSequence, usingResampling);
/** Return a tool for evaluating the marginal probability of new documents * under this model */ public MarginalProbEstimator getProbEstimator() { return new MarginalProbEstimator(numTopics, alpha, alphaSum, beta, typeTopicCounts, tokensPerTopic); }
for (int particle = 0; particle < numParticles; particle++) { particleProbabilities[particle] = leftToRight(tokenSequence, usingResampling);
/** Return a tool for evaluating the marginal probability of new documents * under this model */ public MarginalProbEstimator getProbEstimator() { return new MarginalProbEstimator(numTopics, alpha, alphaSum, beta, typeTopicCounts, tokensPerTopic); }
return new MarginalProbEstimator(numTopics, alphas, alphaSum, beta, sparseTypeTopicCounts, tokensPerTopic);
return new MarginalProbEstimator(numTopics, alphas, alphaSum, beta, sparseTypeTopicCounts, tokensPerTopic);
return new MarginalProbEstimator(numTopics, alphas, alphaSum, beta, sparseTypeTopicCounts, tokensPerTopic);