hmmexample.java

java实现的隐马尔科夫模型

package be.ac.ulg.montefiore.run.jahmm.test;


/*
 * HmmExample.java: A simple example file for the jahmm package.
 *
 * Written by Jean-Marc Francois <[e-mail]>
 *
 * The content of this file is public-domain.
 *
 * Compile with the following command:
 *      javac HmmExample.java
 * And run with:
 *      java HmmExample
 *
 * This file (or a newer version) can be found at this URL:
 *      http://www.run.montefiore.ulg.ac.be/~francois/software/jahmm/example/
 *
 *
 * Changelog:
 * 2004-03-01: Creation. (JMF)
 * 2004-04-27: Adapted to Jahmm 0.2.4. (JMF)
 * 2005-01-31: Minor adaption for release 0.3.0. (JMF)
 */

import java.util.*;
import be.ac.ulg.montefiore.run.jahmm.*;
import be.ac.ulg.montefiore.run.jahmm.toolbox.*;
import be.ac.ulg.montefiore.run.jahmm.learn.*;
import be.ac.ulg.montefiore.run.jahmm.draw.*;


public class HmmExample {

    static public void main(String[] argv) 
	throws java.io.IOException {

	Hmm hmm = buildHmm();
	
	/* Observation sequence generation */
	MarkovGenerator mg = new MarkovGenerator(hmm);
	Vector sequence = mg.observationSequence(1000);
	Vector sequences = new Vector();
	sequences.add(sequence);

	/* Baum-Welch learning */
	BaumWelchLearner bwl =
	    new BaumWelchLearner(2, new OpdfIntegerFactory(2), sequences);

	Hmm learntHmm = bwl.iterate(buildInitHmm());
	(new HmmIntegerDrawer()).write(learntHmm, "learnt1Hmm.dot");
	
	for (int i = 0; i < 10; i++) {
	    KullbackLeiblerDistanceCalculator klc =
		new KullbackLeiblerDistanceCalculator(hmm, learntHmm);
	    System.out.println(i + " " + klc.distance());
	    learntHmm = bwl.iterate(learntHmm);
	}

	/* Write the final result to a 'dot' (graphviz) file. */
	(new HmmIntegerDrawer()).write(learntHmm, "learnt11Hmm.dot");
    }

    /* The HMM this example is based on */
    static Hmm buildHmm() {
       	Hmm hmm = new Hmm(2, new OpdfIntegerFactory(2));
	
	hmm.setPi(0, 0.95);
	hmm.setPi(1, 0.05);

	hmm.setOpdf(0, new OpdfInteger(new double[] {0.95, 0.05}));
	hmm.setOpdf(1, new OpdfInteger(new double[] {0.2, 0.8}));

	hmm.setAij(0, 1, 0.05);
	hmm.setAij(0, 0, 0.95);
	hmm.setAij(1, 0, 0.1);
	hmm.setAij(1, 1, 0.9);

	return hmm;
    }

    /* Initial guess for the Baum-Welch algorithm */
    static Hmm buildInitHmm() {
	Hmm hmm = new Hmm(2, new OpdfIntegerFactory(2));
	
	hmm.setPi(0, 0.90);
	hmm.setPi(1, 0.10);

	hmm.setOpdf(0, new OpdfInteger(new double[] {0.8, 0.2}));
	hmm.setOpdf(1, new OpdfInteger(new double[] {0.1, 0.9}));

	hmm.setAij(0, 1, 0.2);
	hmm.setAij(0, 0, 0.8);
	hmm.setAij(1, 0, 0.15);
	hmm.setAij(1, 1, 0.85);

	return hmm;
	}
}