generate.java

java实现的隐马尔科夫模型

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

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


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

	
	/* Build two simple sequences */
	Vector oseq1 = new Vector();
        for (int i = 0; i < 3; i++)
            oseq1.add(new ObservationInteger(i));

        Vector oseq2 = new Vector();
        for (int i = 2; i < 5; i++)
            oseq2.add(new ObservationInteger(i));

        Vector obsSeqs = new Vector();
        obsSeqs.add(oseq1);
        obsSeqs.add(oseq2);
	
	
	/* Build a model according to those sequences using the k-means
	   algorithm */
	KMeansLearner kml = new KMeansLearner(5, new OpdfIntegerFactory(10),
					      obsSeqs);
	Hmm hmmModel = kml.learn();
	
	HmmDrawer hmmDrawer = new HmmIntegerDrawer();
	hmmDrawer.write(hmmModel, "graph-generate-model.dot");
	
	
	/* Generate sequences according to this model */
	MarkovGenerator mg = new MarkovGenerator(hmmModel);
	Vector sequences = new Vector();
	for (int i = 0; i < 100; i++)
	    sequences.add(mg.observationSequence(5));
	
	
	/* Build a model according to those sequences using the Baum-Welch
	   algorithm */
	Hmm initHmm = new Hmm(5, new OpdfIntegerFactory(10));
	for (int i = 1; i < 5; i++)
	    initHmm.getOpdf(i).fit(new Observation[] {
		new ObservationInteger(i)});
	initHmm.getOpdf(0).fit(new Observation[] { new ObservationInteger(0),
						   new ObservationInteger(1)});
	
	BaumWelchLearner bwl =
	    new BaumWelchLearner(5, new OpdfIntegerFactory(10), sequences);
	Hmm hmmLearnt = bwl.learn(initHmm);
	hmmDrawer.write(hmmLearnt, "graph-generate-learnt.dot");
    }
}