/*
* 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.alog;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.MLSequenceSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.data.basic.BasicMLSequenceSet;
import org.encog.ml.hmm.HiddenMarkovModel;
/**
* This class is used to generate random sequences based on a Hidden Markov
* Model. These sequences represent the random probabilities that the HMM
* models.
*
*/
public class MarkovGenerator {
private final HiddenMarkovModel hmm;
private int currentState;
public MarkovGenerator(final HiddenMarkovModel hmm) {
this.hmm = hmm;
newSequence();
}
public MLSequenceSet generateSequences(final int observationCount,
final int observationLength) {
final MLSequenceSet result = new BasicMLSequenceSet();
for (int i = 0; i < observationCount; i++) {
result.startNewSequence();
result.add(observationSequence(observationLength));
}
return result;
}
public int getCurrentState() {
return this.currentState;
}
public void newSequence() {
final double rand = Math.random();
double current = 0.0;
for (int i = 0; i < (this.hmm.getStateCount() - 1); i++) {
current += this.hmm.getPi(i);
if (current > rand) {
this.currentState = i;
return;
}
}
this.currentState = this.hmm.getStateCount() - 1;
}
public MLDataPair observation() {
final MLDataPair o = this.hmm.getStateDistribution(this.currentState)
.generate();
double rand = Math.random();
for (int j = 0; j < (this.hmm.getStateCount() - 1); j++) {
if ((rand -= this.hmm
.getTransitionProbability(this.currentState, j)) < 0) {
this.currentState = j;
return o;
}
}
this.currentState = this.hmm.getStateCount() - 1;
return o;
}
public MLDataSet observationSequence(int length) {
final MLDataSet sequence = new BasicMLDataSet();
while (length-- > 0) {
sequence.add(observation());
}
newSequence();
return sequence;
}
}