Java Code Examples for cc.mallet.topics.ParallelTopicModel#estimate()

The following examples show how to use cc.mallet.topics.ParallelTopicModel#estimate() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: MalletCalculator.java    From TagRec with GNU Affero General Public License v3.0 6 votes vote down vote up 
public void predictValuesProbs(boolean topicCreation) {
	ParallelTopicModel LDA = new ParallelTopicModel(this.numTopics, ALPHA * this.numTopics, BETA); // TODO
	LDA.addInstances(this.instances);
	LDA.setNumThreads(1);
	LDA.setNumIterations(NUM_ITERATIONS);
	LDA.setRandomSeed(43);
	try {
		LDA.estimate();
	} catch (Exception e) {
		e.printStackTrace();
	}
	this.docList = getMaxTopicsByDocs(LDA, this.numTopics);
	System.out.println("Fetched Doc-List");
	this.topicList = !topicCreation ? getMaxTermsByTopics(LDA, MAX_TERMS) : null;
	System.out.println("Fetched Topic-List");
}
Example 2
Source File: MalletCalculatorTweet.java    From TagRec with GNU Affero General Public License v3.0 6 votes vote down vote up 
/**
 * What does this boolean value signify.
 * @param topicCreation
 */
public void predictValuesProbs(boolean topicCreation) {
    
    ParallelTopicModel LDA = new ParallelTopicModel(this.numTopics, ALPHA * this.numTopics, BETA); // TODO
    LDA.addInstances(this.instances);
    LDA.setNumThreads(1);
    LDA.setNumIterations(NUM_ITERATIONS);
    LDA.setRandomSeed(43);
    try {
        LDA.estimate();
    } catch (Exception e) {
        e.printStackTrace();
    }
    this.docList = getMaxTopicsByDocs(LDA, this.numTopics);
    System.out.println("Fetched Doc-List");
    this.topicList = !topicCreation ? getMaxTermsByTopics(LDA, MAX_TERMS) : null;
    System.out.println("Fetched Topic-List");
}
Example 3
Source File: LDA.java    From topic-detection with Apache License 2.0 5 votes vote down vote up 
/**
 * Creates the LDA model on the specified document corpus
 * @param texts a list of documents
 * @param numTopics the number of desired documents
 * @param numIterations the number of LDA iterationss
 * @return An LDA topic model
 * @throws IOException
 */
private ParallelTopicModel createLDAModel(List<String> texts, int numTopics, int numIterations) throws IOException
{
	InstanceList instanceList = createInstanceList(texts);
	ParallelTopicModel model = new ParallelTopicModel(numTopics);
	model.addInstances(instanceList);
	model.setNumIterations(numIterations);
	model.estimate();
	return model;
}
Example 4
Source File: LDAModelEstimator.java    From RankSys with Mozilla Public License 2.0 3 votes vote down vote up 
/**
 * Estimate a topic model for collaborative filtering data.
 *
 * @param <U> user type
 * @param <I> item type
 * @param preferences preference data
 * @param k number of topics
 * @param alpha alpha in model
 * @param beta beta in model
 * @param numIterations number of iterations
 * @param burninPeriod burnin period
 * @return a topic model
 * @throws IOException when internal IO error occurs
 */
public static <U, I> ParallelTopicModel estimate(FastPreferenceData<U, I> preferences, int k, double alpha, double beta, int numIterations, int burninPeriod) throws IOException {
    
    ParallelTopicModel topicModel = new ParallelTopicModel(k, alpha * k, beta);
    topicModel.addInstances(new LDAInstanceList<>(preferences));
    topicModel.setTopicDisplay(numIterations + 1, 0);
    topicModel.setNumIterations(numIterations);
    topicModel.setBurninPeriod(burninPeriod);
    topicModel.setNumThreads(Runtime.getRuntime().availableProcessors());

    topicModel.estimate();

    return topicModel;
}