private void initializeModel() { TopicModel topicModel = new TopicModel(numTopics, numTerms, eta, alpha, RandomUtils.getRandom(), terms, numUpdatingThreads, initialModelCorpusFraction == 0 ? 1 : initialModelCorpusFraction * totalCorpusWeight); topicModel.setConf(getConf()); TopicModel updatedModel = initialModelCorpusFraction == 0 ? new TopicModel(numTopics, numTerms, eta, alpha, null, terms, numUpdatingThreads, 1) : topicModel; updatedModel.setConf(getConf()); docTopicCounts = new DenseMatrix(numDocuments, numTopics); docTopicCounts.assign(1.0 / numTopics); modelTrainer = new ModelTrainer(topicModel, updatedModel, numTrainingThreads, numTopics, numTerms); }
private void initializeModel() { TopicModel topicModel = new TopicModel(numTopics, numTerms, eta, alpha, RandomUtils.getRandom(), terms, numUpdatingThreads, initialModelCorpusFraction == 0 ? 1 : initialModelCorpusFraction * totalCorpusWeight); topicModel.setConf(getConf()); TopicModel updatedModel = initialModelCorpusFraction == 0 ? new TopicModel(numTopics, numTerms, eta, alpha, null, terms, numUpdatingThreads, 1) : topicModel; updatedModel.setConf(getConf()); docTopicCounts = new DenseMatrix(numDocuments, numTopics); docTopicCounts.assign(1.0 / numTopics); modelTrainer = new ModelTrainer(topicModel, updatedModel, numTrainingThreads, numTopics, numTerms); }
private void initializeModel() { TopicModel topicModel = new TopicModel(numTopics, numTerms, eta, alpha, RandomUtils.getRandom(), terms, numUpdatingThreads, initialModelCorpusFraction == 0 ? 1 : initialModelCorpusFraction * totalCorpusWeight); topicModel.setConf(getConf()); TopicModel updatedModel = initialModelCorpusFraction == 0 ? new TopicModel(numTopics, numTerms, eta, alpha, null, terms, numUpdatingThreads, 1) : topicModel; updatedModel.setConf(getConf()); docTopicCounts = new DenseMatrix(numDocuments, numTopics); docTopicCounts.assign(1.0 / numTopics); modelTrainer = new ModelTrainer(topicModel, updatedModel, numTrainingThreads, numTopics, numTerms); }