TopicInferencer inferencer = model.getInferencer(); double[] topicProbs = inferencer.getSampledDistribution(newInstance, 100, 10, 10);
.getSampledDistribution(testing.get(0), 10, 1, 5); System.out.println("0\t" + testProbabilities[0]);
getSampledDistribution(instance, numIterations, thinning, burnIn); builder.append(doc);
Instance instance = instances.get(0); double[] distribution = inferencer.getSampledDistribution( instance, numIterations, thinning, burnIn); List<Topic> topics = new ArrayList<Topic>();
getSampledDistribution(instance, numIterations, thinning, burnIn); builder.append(doc);
getSampledDistribution(instance, numIterations, thinning, burnIn); builder.append(doc);
double[] testProbabilities = inferencer.getSampledDistribution(testing.get(0), 10, 1, 5); System.out.println("0\t" + testProbabilities[0]);
double[] testProbabilities = inferencer.getSampledDistribution(testing.get(0), 10, 1, 5); System.out.println("0\t" + testProbabilities[0]);
double[] testProbabilities = inferencer.getSampledDistribution(testing.get(0), 10, 1, 5); System.out.println("0\t" + testProbabilities[0]);
@Override protected void doProcess(JCas jCas) throws AnalysisEngineProcessException { InstanceList testing = new InstanceList(pipe); testing.addThruPipe(new Instance(jCas.getDocumentText(), null, "from jcas", null)); TopicInferencer inferencer = model.getInferencer(); double[] topicDistribution = inferencer.getSampledDistribution(testing.get(0), iterations, thining, burnIn); int topicIndex = new MaximumIndex(topicDistribution).find(); List<String> inferedTopic = topicWords.forTopic(topicIndex); Metadata md = new Metadata(jCas); md.setKey(metadataKey); md.setValue(inferedTopic.toString()); addToJCasIndex(md); }
@Override protected void doProcess(JCas jCas) throws AnalysisEngineProcessException { InstanceList testing = new InstanceList(pipe); testing.addThruPipe(new Instance(jCas.getDocumentText(), null, "from jcas", null)); TopicInferencer inferencer = model.getInferencer(); double[] topicDistribution = inferencer.getSampledDistribution(testing.get(0), iterations, thining, burnIn); int topicIndex = new MaximumIndex(topicDistribution).find(); List<String> inferedTopic = topicWords.forTopic(topicIndex); Metadata md = new Metadata(jCas); md.setKey(metadataKey); md.setValue(inferedTopic.toString()); addToJCasIndex(md); }
double[] topicDistribution = inferencer.getSampledDistribution( malletPipe.instanceFrom(instance), nIterations, thinning, burnIn);