/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.hmm;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.ml.data.MLSequenceSet;
import org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator;
import org.encog.ml.hmm.alog.MarkovGenerator;
import org.encog.ml.hmm.distributions.ContinousDistribution;
import org.encog.ml.hmm.distributions.DiscreteDistribution;
import org.encog.ml.hmm.train.bw.TrainBaumWelch;
import org.encog.ml.hmm.train.kmeans.TrainKMeans;
public class TestHMM extends TestCase {
static HiddenMarkovModel buildContHMM()
{
double [] mean1 = {0.25, -0.25};
double [][] covariance1 = { {1, 2}, {1, 4} };
double [] mean2 = {0.5, 0.25};
double [][] covariance2 = { {4, 2}, {3, 4} };
HiddenMarkovModel hmm = new HiddenMarkovModel(2);
hmm.setPi(0, 0.8);
hmm.setPi(1, 0.2);
hmm.setStateDistribution(0, new ContinousDistribution(mean1,covariance1));
hmm.setStateDistribution(1, new ContinousDistribution(mean2,covariance2));
hmm.setTransitionProbability(0, 1, 0.05);
hmm.setTransitionProbability(0, 0, 0.95);
hmm.setTransitionProbability(1, 0, 0.10);
hmm.setTransitionProbability(1, 1, 0.90);
return hmm;
}
static HiddenMarkovModel buildContInitHMM()
{
double [] mean1 = {0.20, -0.20};
double [][] covariance1 = { {1.3, 2.2}, {1.3, 4.3} };
double [] mean2 = {0.5, 0.25};
double [][] covariance2 = { {4.1, 2.1}, {3.2, 4.4} };
HiddenMarkovModel hmm = new HiddenMarkovModel(2);
hmm.setPi(0, 0.9);
hmm.setPi(1, 0.1);
hmm.setStateDistribution(0, new ContinousDistribution(mean1,covariance1));
hmm.setStateDistribution(1, new ContinousDistribution(mean2,covariance2));
hmm.setTransitionProbability(0, 1, 0.10);
hmm.setTransitionProbability(0, 0, 0.90);
hmm.setTransitionProbability(1, 0, 0.15);
hmm.setTransitionProbability(1, 1, 0.85);
return hmm;
}
static HiddenMarkovModel buildDiscHMM()
{
HiddenMarkovModel hmm =
new HiddenMarkovModel(2, 2);
hmm.setPi(0, 0.95);
hmm.setPi(1, 0.05);
hmm.setStateDistribution(0, new DiscreteDistribution(new double[][] { { 0.95, 0.05 } }));
hmm.setStateDistribution(1, new DiscreteDistribution(new double[][] { { 0.20, 0.80 } }));
hmm.setTransitionProbability(0, 1, 0.05);
hmm.setTransitionProbability(0, 0, 0.95);
hmm.setTransitionProbability(1, 0, 0.10);
hmm.setTransitionProbability(1, 1, 0.90);
return hmm;
}
/* Initial guess for the Baum-Welch algorithm */
static HiddenMarkovModel buildDiscInitHMM()
{
HiddenMarkovModel hmm = new HiddenMarkovModel(2,2);
hmm.setPi(0, 0.50);
hmm.setPi(1, 0.50);
hmm.setStateDistribution(0, new DiscreteDistribution(new double[][] { { 0.8, 0.2 } }));
hmm.setStateDistribution(1, new DiscreteDistribution(new double[][] { { 0.1, 0.9 } }));
hmm.setTransitionProbability(0, 1, 0.2);
hmm.setTransitionProbability(0, 0, 0.8);
hmm.setTransitionProbability(1, 0, 0.2);
hmm.setTransitionProbability(1, 1, 0.8);
return hmm;
}
public void testDiscBWL() {
HiddenMarkovModel hmm = buildDiscHMM();
HiddenMarkovModel learntHmm = buildDiscInitHMM();
MarkovGenerator mg = new MarkovGenerator(hmm);
MLSequenceSet training = mg.generateSequences(200,100);
TrainBaumWelch bwl = new TrainBaumWelch(learntHmm,training);
KullbackLeiblerDistanceCalculator klc =
new KullbackLeiblerDistanceCalculator();
bwl.iteration(5);
learntHmm = (HiddenMarkovModel)bwl.getMethod();
double e = klc.distance(learntHmm, hmm);
Assert.assertTrue(e<0.01);
}
public void testContBWL() {
HiddenMarkovModel hmm = buildContHMM();
HiddenMarkovModel learntHmm = buildContInitHMM();
MarkovGenerator mg = new MarkovGenerator(hmm);
MLSequenceSet training = mg.generateSequences(200,100);
TrainBaumWelch bwl = new TrainBaumWelch(learntHmm,training);
KullbackLeiblerDistanceCalculator klc =
new KullbackLeiblerDistanceCalculator();
bwl.iteration(5);
learntHmm = (HiddenMarkovModel)bwl.getMethod();
double e = klc.distance(learntHmm, hmm);
Assert.assertTrue(e<0.01);
}
public void testDiscKMeans() {
HiddenMarkovModel hmm = buildDiscHMM();
MarkovGenerator mg = new MarkovGenerator(hmm);
MLSequenceSet sequences = mg.generateSequences(200,100);
TrainKMeans trainer = new TrainKMeans(hmm,sequences);
HiddenMarkovModel learntHmm = buildDiscInitHMM();
KullbackLeiblerDistanceCalculator klc =
new KullbackLeiblerDistanceCalculator();
trainer.iteration(5);
learntHmm = (HiddenMarkovModel)trainer.getMethod();
double e = klc.distance(learntHmm, hmm);
Assert.assertTrue(e<0.05);
}
}