/*
 * Encog(tm) Core v3.4 - Java Version
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-core
 
 * Copyright 2008-2016 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;
	}
}