/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.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.selection;

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

import com.rapidminer.operator.features.Individual;
import com.rapidminer.operator.features.Population;
import com.rapidminer.operator.features.PopulationOperator;


/**
 * Crossover operator for the used bitlists of example sets. An example set is
 * selected with a given fixed propability and a mating partner is determined
 * randomly. Crossover can be either one point, uniform or shuffled. Please note
 * that shuffled crossover first uniformly determines the number of attributes
 * which should be swapped. Therefore uniform and shuffle crossover are not
 * equivalent. <br>
 * 
 * Only useful if all example sets have the same (number of) attributes.
 * 
 * @author Simon Fischer, Ingo Mierswa
 *          Exp $
 */
public class SelectionCrossover implements PopulationOperator {

	public static final String[] CROSSOVER_TYPES = { "one_point", "uniform", "shuffle" };

	public static final int ONE_POINT = 0;

	public static final int UNIFORM = 1;

	public static final int SHUFFLE = 2;

	private int type;

	private double prob;

    private Random random;
    
    private int minNumber;
    
    private int maxNumber;
    
    private int exactNumber;
    
    
	public SelectionCrossover(int type, double prob, Random random, int minNumber, int maxNumber, int exactNumber) {
		this.prob        = prob;
		this.type        = type;
        this.random      = random;
        this.minNumber   = minNumber;
        this.maxNumber   = maxNumber;
        this.exactNumber = exactNumber;
	}

	/** The default implementation returns true for every generation. */
	public boolean performOperation(int generation) {
		return true;
	}

	public int getType() {
		return type;
	}

	public void crossover(double[] weights1, double[] weights2) {
		switch (type) {				
			case ONE_POINT:
				int n = 1 + random.nextInt(weights1.length - 1);
				for (int index = n; index < weights1.length; index++) {
					double dummy = weights1[index];
					weights1[index] = weights2[index];
					weights2[index] = dummy;
				}
				break;
			case UNIFORM:
				boolean[] swap = new boolean[weights1.length];
				for (int i = 0; i < swap.length; i++) {
					swap[i] = random.nextBoolean();;
				}
				swapAttributes(weights1, weights2, swap);
				break;
			case SHUFFLE:
				swap = new boolean[weights1.length];
				List<Integer> indices = new ArrayList<Integer>();
				for (int i = 0; i < swap.length; i++) {
					indices.add(i);
				}
				if (indices.size() > 0) {
					int toSwap = random.nextInt(indices.size() - 1) + 1;
					for (int i = 0; i < toSwap; i++) {
						swap[indices.remove(random.nextInt(indices.size()))] = true;
					}
				}
				swapAttributes(weights1, weights2, swap);
				break;
			default:
				break;
		}
	}

	private void swapAttributes(double[] weights1, double[] weights2, boolean[] swap) {
		for (int index = 0; index < weights1.length; index++) {
			if (swap[index]) {
				double dummy = weights1[index];
				weights1[index] = weights2[index];
				weights2[index] = dummy;
			}
		}
	}

	public void operate(Population population) {
		if (population.getNumberOfIndividuals() < 2)
			return;

		LinkedList<double[]> matingPool = new LinkedList<double[]>();
		for (int i = 0; i < population.getNumberOfIndividuals(); i++)
			matingPool.add(population.get(i).getWeightsClone());

		List<Individual> l = new LinkedList<Individual>();

		while (matingPool.size() > 1) {
			double[] p1 = matingPool.remove(random.nextInt(matingPool.size()));
			double[] p2 = matingPool.remove(random.nextInt(matingPool.size()));

			if (random.nextDouble() < prob) {
				crossover(p1, p2);
				
				Individual newIndividual1 = new Individual(p1);
				int numberOfFeatures = newIndividual1.getNumberOfUsedAttributes(); 
				if (numberOfFeatures > 0) {
					if (exactNumber > 0) {
						if (numberOfFeatures == exactNumber) {
							l.add(newIndividual1);
						}
					} else {
						if (((maxNumber < 1) || (numberOfFeatures <= maxNumber)) && (numberOfFeatures >= minNumber)) {
							l.add(newIndividual1);
						}
					}
				}
				
				Individual newIndividual2 = new Individual(p2);
				numberOfFeatures = newIndividual2.getNumberOfUsedAttributes(); 
				if (numberOfFeatures > 0) {
					if (exactNumber > 0) {
						if (numberOfFeatures == exactNumber) {
							l.add(newIndividual2);
						}
					} else {
						if (((maxNumber < 1) || (numberOfFeatures <= maxNumber)) && (numberOfFeatures >= minNumber)) {
							l.add(newIndividual2);
						}
					}
				}
			}
		}

		population.addAllIndividuals(l);
	}
}