Package org.encog.ml.hmm

Source Code of org.encog.ml.hmm.TestHMM

/*
* 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.
*  
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*/
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);
  }

}
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