buildInitialTypeTopicCounts(); initializeHistograms();
buildInitialTypeTopicCounts(); initializeHistograms();
buildInitialTypeTopicCounts(); initializeHistograms();
topicModel.numTypes = labeledLDA.numTypes; topicModel.betaSum = labeledLDA.betaSum; topicModel.buildInitialTypeTopicCounts();
topicModel.numTypes = labeledLDA.numTypes; topicModel.betaSum = labeledLDA.betaSum; topicModel.buildInitialTypeTopicCounts();
topicModel.numTypes = labeledLDA.numTypes; topicModel.betaSum = labeledLDA.betaSum; topicModel.buildInitialTypeTopicCounts();
public void addInstances (InstanceList training) { alphabet = training.getDataAlphabet(); numTypes = alphabet.size(); betaSum = beta * numTypes; Randoms random = null; if (randomSeed == -1) { random = new Randoms(); } else { random = new Randoms(randomSeed); } for (Instance instance : training) { FeatureSequence tokens = (FeatureSequence) instance.getData(); LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[ tokens.size() ]); int[] topics = topicSequence.getFeatures(); for (int position = 0; position < topics.length; position++) { int topic = random.nextInt(numTopics); topics[position] = topic; } TopicAssignment t = new TopicAssignment(instance, topicSequence); data.add(t); } buildInitialTypeTopicCounts(); initializeHistograms(); }
public void addInstances (InstanceList training) { alphabet = training.getDataAlphabet(); numTypes = alphabet.size(); betaSum = beta * numTypes; Randoms random = null; if (randomSeed == -1) { random = new Randoms(); } else { random = new Randoms(randomSeed); } for (Instance instance : training) { FeatureSequence tokens = (FeatureSequence) instance.getData(); LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[ tokens.size() ]); int[] topics = topicSequence.getFeatures(); for (int position = 0; position < topics.length; position++) { int topic = random.nextInt(numTopics); topics[position] = topic; } TopicAssignment t = new TopicAssignment(instance, topicSequence); data.add(t); } buildInitialTypeTopicCounts(); initializeHistograms(); }
public void addInstances (InstanceList training) { alphabet = training.getDataAlphabet(); numTypes = alphabet.size(); betaSum = beta * numTypes; Randoms random = null; if (randomSeed == -1) { random = new Randoms(); } else { random = new Randoms(randomSeed); } for (Instance instance : training) { FeatureSequence tokens = (FeatureSequence) instance.getData(); LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[ tokens.size() ]); int[] topics = topicSequence.getFeatures(); for (int position = 0; position < topics.length; position++) { int topic = random.nextInt(numTopics); topics[position] = topic; } TopicAssignment t = new TopicAssignment(instance, topicSequence); data.add(t); } buildInitialTypeTopicCounts(); initializeHistograms(); }