/*
* 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.persist;
import java.io.File;
import java.io.IOException;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.ml.hmm.HiddenMarkovModel;
import org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator;
import org.encog.ml.hmm.distributions.ContinousDistribution;
import org.encog.ml.hmm.distributions.DiscreteDistribution;
import org.encog.util.TempDir;
import org.encog.util.obj.SerializeObject;
public class TestPersistHMM extends TestCase {
public final TempDir TEMP_DIR = new TempDir();
public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
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 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;
}
public void validate(HiddenMarkovModel result, HiddenMarkovModel source)
{
KullbackLeiblerDistanceCalculator klc =
new KullbackLeiblerDistanceCalculator();
double e = klc.distance(result, source);
Assert.assertTrue(e<0.01);
}
public void testDiscPersistEG()
{
HiddenMarkovModel sourceHMM = buildDiscHMM();
EncogDirectoryPersistence.saveObject(EG_FILENAME, sourceHMM);
HiddenMarkovModel resultHMM = (HiddenMarkovModel)EncogDirectoryPersistence.loadObject(EG_FILENAME);
validate(resultHMM,sourceHMM);
}
public void testDiscPersistSerial() throws IOException, ClassNotFoundException
{
HiddenMarkovModel sourceHMM = buildDiscHMM();
SerializeObject.save(SERIAL_FILENAME, sourceHMM);
HiddenMarkovModel resultHMM = (HiddenMarkovModel)SerializeObject.load(SERIAL_FILENAME);
validate(resultHMM,sourceHMM);
}
public void testContPersistEG()
{
HiddenMarkovModel sourceHMM = buildContHMM();
EncogDirectoryPersistence.saveObject(EG_FILENAME, sourceHMM);
HiddenMarkovModel resultHMM = (HiddenMarkovModel)EncogDirectoryPersistence.loadObject(EG_FILENAME);
validate(resultHMM,sourceHMM);
}
public void testContPersistSerial() throws IOException, ClassNotFoundException
{
HiddenMarkovModel sourceHMM = buildContHMM();
SerializeObject.save(SERIAL_FILENAME, sourceHMM);
HiddenMarkovModel resultHMM = (HiddenMarkovModel)SerializeObject.load(SERIAL_FILENAME);
validate(resultHMM,sourceHMM);
}
@Override
protected void tearDown() throws Exception {
super.tearDown();
TEMP_DIR.dispose();
}
}