/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.features.weighting;

import java.util.LinkedList;
import java.util.List;
import java.util.Random;

import com.rapidminer.operator.features.Individual;
import com.rapidminer.operator.features.IndividualOperator;


/**
 * Changes the weight for all attributes by multiplying them with a gaussian
 * distribution.
 * 
 * @author Ingo Mierswa
 */
public class WeightingMutation extends IndividualOperator {

	private double variance;

	private boolean bounded;

	private Random random;
	
	private boolean[] isNominal;
	
	private double nominalMutationProb;

	public WeightingMutation(double variance, boolean bounded, boolean[] isNominal, double nominalMutationProb, Random random) {
		this.variance = variance;
		this.bounded = bounded;
		this.random = random;
		this.isNominal = isNominal;
		this.nominalMutationProb = nominalMutationProb;
	}

	public void setVariance(double variance) {
		this.variance = variance;
	}

	public double getVariance() {
		return variance;
	}

	@Override
	public List<Individual> operate(Individual individual) {
		double[] weights = individual.getWeightsClone();
		List<Individual> l = new LinkedList<Individual>();
		for (int i = 0; i < weights.length; i++) {
			if (!isNominal[i]) {
				if (random.nextDouble() < nominalMutationProb) {
					if (weights[i] > 0)
						weights[i] = 0;
					else
						weights[i] = 1;
				}
			} else {
				double weight = weights[i] + random.nextGaussian() * variance;
				if ((!bounded) || ((weight >= 0) && (weight <= 1)))
					weights[i] = weight;
			} 
		}
		Individual newIndividual = new Individual(weights);
		if (newIndividual.getNumberOfUsedAttributes() > 0)
			l.add(newIndividual);
		return l;
	}
}