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MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
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package cc.mallet.topics.tui;

import cc.mallet.util.*;
import cc.mallet.types.*;
import cc.mallet.topics.*;

import java.io.*;

public class EvaluateTopics {

    static CommandOption.String evaluatorFilename = new CommandOption.String
        (EvaluateTopics.class, "evaluator", "FILENAME", true, null,
		 "A serialized topic evaluator from a trained topic model.\n" + 
         "By default this is null, indicating that no file will be read.", null);

	static CommandOption.String inputFile = new CommandOption.String
		(EvaluateTopics.class, "input", "FILENAME", true, null,
		 "The filename from which to read the list of instances\n" +
		 "for which topics should be inferred.  Use - for stdin.  " +
		 "The instances must be FeatureSequence or FeatureSequenceWithBigrams, not FeatureVector", null);
	
    static CommandOption.String docProbabilityFile = new CommandOption.String
        (EvaluateTopics.class, "output-doc-probs", "FILENAME", true, null,
         "The filename in which to write the inferred log probabilities\n" +
		 "per document.  " +
         "By default this is null, indicating that no file will be written.", null);

    static CommandOption.String probabilityFile = new CommandOption.String
        (EvaluateTopics.class, "output-prob", "FILENAME", true, "-",
         "The filename in which to write the inferred log probability of the testing set\n" +
         "Use - for stdout, which is the default.", null);

	static CommandOption.Integer numParticles = new CommandOption.Integer
        (EvaluateTopics.class, "num-particles", "INTEGER", true, 10,
         "The number of particles to use in left-to-right evaluation.", null);

	static CommandOption.Boolean showWords = new CommandOption.Boolean
        (EvaluateTopics.class, "show-words", "TRUE|FALSE", false, false,
         "If true, print the log likelihood of each individual token to standard out.", null);

	static CommandOption.Boolean usingResampling = new CommandOption.Boolean
        (EvaluateTopics.class, "use-resampling", "TRUE|FALSE", false, false,
         "Whether to resample topics in left-to-right evaluation. Resampling is more accurate, but leads to quadratic scaling in the lenght of documents.", null);

	static CommandOption.Integer numIterations = new CommandOption.Integer
        (EvaluateTopics.class, "num-iterations", "INTEGER", true, 100,
         "The number of iterations of Gibbs sampling.", null);

    static CommandOption.Integer sampleInterval = new CommandOption.Integer
        (EvaluateTopics.class, "sample-interval", "INTEGER", true, 10,
         "The number of iterations between saved samples.", null);

    static CommandOption.Integer burnInIterations = new CommandOption.Integer
        (EvaluateTopics.class, "burn-in", "INTEGER", true, 10,
         "The number of iterations before the first sample is saved.", null);

    static CommandOption.Integer randomSeed = new CommandOption.Integer
        (EvaluateTopics.class, "random-seed", "INTEGER", true, -1,
         "The random seed for the Gibbs sampler.  Default is -1, which will use the clock.", null);

	public static void main (String[] args) {

        // Process the command-line options
		CommandOption.setSummary (EvaluateTopics.class,
                                  "Estimate the marginal probability of new documents under ");
        CommandOption.process (EvaluateTopics.class, args);

		if (evaluatorFilename.value == null) {
			System.err.println("You must specify a serialized topic evaluator. Use --help to list options.");
			System.exit(0);
		}

		if (inputFile.value == null) {
			System.err.println("You must specify a serialized instance list. Use --help to list options.");
			System.exit(0);
		}

		try {
			
			PrintStream docProbabilityStream = null;
			if (docProbabilityFile.value != null) {
				docProbabilityStream = new PrintStream(docProbabilityFile.value);
			}
			
			PrintStream outputStream = System.out;
			if (probabilityFile.value != null &&
				! probabilityFile.value.equals("-")) {
				outputStream = new PrintStream(probabilityFile.value);
			}
			
			MarginalProbEstimator evaluator = 
				MarginalProbEstimator.read(new File(evaluatorFilename.value));
			
			evaluator.setPrintWords(showWords.value);
			
			evaluator.setRandomSeed(randomSeed.value);

			InstanceList instances = InstanceList.load (new File(inputFile.value));

			outputStream.println(evaluator.evaluateLeftToRight(instances, numParticles.value, 
															   usingResampling.value,
															   docProbabilityStream));
			

		} catch (Exception e) {
			e.printStackTrace();
			System.err.println(e.getMessage());
		}
	}
}