/**
* Copyright (C) 2001-2017 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.aggregation;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
/**
* Performs a usual GA crossover on integer arrays. Supports one-point and uniform crossover.
*
* @author Ingo Mierswa Exp $
*/
public class AggregationCrossover {
/** The names for the crossover types. */
public static final String[] CROSSOVER_TYPES = { "one_point", "uniform" };
/** Indicates a one-point crossover type. */
public static final int CROSSOVER_ONE_POINT = 0;
/** Indicates a uniform crossover type. */
public static final int CROSSOVER_UNIFORM = 1;
/** The crossover type. */
private int crossoverType = CROSSOVER_UNIFORM;
/** The crossover probability. */
private double crossoverProb = 0.9;
private Random random;
/** Creates a new aggregation crossover operator. */
public AggregationCrossover(int type, double probability, Random random) {
this.crossoverType = type;
this.crossoverProb = probability;
this.random = random;
}
/** Checks if at least one feature is selected. */
private boolean isValid(int[] individual) {
for (int i = 0; i < individual.length; i++) {
if (individual[i] >= 0) {
return true;
}
}
return false;
}
/**
* Randomly selects parents from the population and performs crossover. The parents are kept.
*/
public void crossover(List<AggregationIndividual> population) {
List<AggregationIndividual> children = new ArrayList<AggregationIndividual>();
for (int i = 0; i < population.size(); i++) {
if (random.nextDouble() < crossoverProb) {
int[] parent1 = population.get(random.nextInt(population.size())).getIndividual();
int[] parent2 = population.get(random.nextInt(population.size())).getIndividual();
int[] child1 = new int[parent1.length];
for (int j = 0; j < child1.length; j++) {
child1[j] = parent1[j];
}
int[] child2 = new int[parent2.length];
for (int j = 0; j < child2.length; j++) {
child2[j] = parent2[j];
}
crossover(child1, child2);
if (isValid(child1)) {
children.add(new AggregationIndividual(child1));
}
if (isValid(child2)) {
children.add(new AggregationIndividual(child2));
}
}
}
population.addAll(children);
}
/**
* Changes the individual. Make clones if original individuals should be kept.
*/
private void crossover(int[] individual1, int[] individual2) {
switch (crossoverType) {
case CROSSOVER_ONE_POINT:
int n = 1 + random.nextInt(individual1.length - 1);
for (int i = n; i < individual1.length; i++) {
int dummy = individual1[i];
individual1[i] = individual2[i];
individual2[i] = dummy;
}
break;
case CROSSOVER_UNIFORM:
for (int i = 0; i < individual1.length; i++) {
if (random.nextBoolean()) {
int dummy = individual1[i];
individual1[i] = individual2[i];
individual2[i] = dummy;
}
}
break;
default:
break;
}
}
}