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/*
 * Java Genetic Algorithm Library (jenetics-3.8.0).
 * Copyright (c) 2007-2017 Franz Wilhelmstötter
 *
 * 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.
 *
 * Author:
 *    Franz Wilhelmstötter ([email protected])
 */
package org.jenetics;

import static java.lang.Math.min;
import static java.lang.String.format;
import static org.jenetics.internal.math.random.nextDouble;

import java.util.Random;

import org.jenetics.internal.util.Hash;
import org.jenetics.internal.util.require;

import org.jenetics.util.MSeq;
import org.jenetics.util.RandomRegistry;

/**
 * This alterer takes two chromosome (treating it as vectors) and creates a
 * linear combination of this vectors as result. The  line-recombination depends
 * on a variable p which determines how far out along the line (defined
 * by the two multidimensional points/vectors) the children are allowed to be.
 * If p = 0 then the children will be located along the line within the
 * hypercube between the two points. If p > 0 then the children may
 * be located anywhere on the line, even somewhat outside of the hypercube.
 * 
 * Points outside of the allowed numeric range are rejected and a new points are
 * generated, until they lie in the valid range. The strategy on how
 * out-of-range points are handled, is the difference to the very similar
 * {@link LineCrossover}.
 *
 * @see 
 *       Essentials of Metaheuristic, page 42
 * @see LineCrossover
 *
 * @author Franz Wilhelmstötter
 * @version 3.8
 * @since 3.8
 */
public class IntermediateCrossover<
	G extends NumericGene,
	C extends Comparable
>
	extends Crossover
{

	private final double _p;

	/**
	 * Creates a new intermediate-crossover with the given recombination
	 * probability and the line-scaling factor p.
	 * 
	 * When the value for p is greater then 0, the crossover point
	 * generation must be repeated until the points lie within the allowed
	 * range. Values greater then 10 are usually not recommended, since this
	 * leads to unnecessary crossover point generation.
	 *
	 * @param probability the recombination probability.
	 * @param p defines the possible location of the recombined chromosomes. If
	 *        p = 0 then the children will be located along the line
	 *        within the hypercube between the two points. If p > 0
	 *        then the children may be located anywhere on the line, even
	 *        somewhat outside of the hypercube.
	 * @throws IllegalArgumentException if the {@code probability} is not in the
	 *         valid range of {@code [0, 1]} or if {@code p} is smaller then zero
	 */
	public IntermediateCrossover(final double probability, final double p) {
		super(probability);
		_p = require.nonNegative(p, "p");
	}

	/**
	 * Creates a new intermediate-crossover with the given recombination
	 * probability. The parameter p is set to zero, which restricts the
	 * recombined chromosomes within the hypercube of the selected chromosomes
	 * (vectors).
	 *
	 * @param probability the recombination probability.
	 * @throws IllegalArgumentException if the {@code probability} is not in the
	 *         valid range of {@code [0, 1]}
	 */
	public IntermediateCrossover(final double probability) {
		this(probability, 0);
	}

	/**
	 * Creates a new intermediate-crossover with default recombination
	 * probability ({@link #DEFAULT_ALTER_PROBABILITY}) and a p value
	 * of zero, which restricts the recombined chromosomes within the hypercube
	 * of the selected chromosomes (vectors).
	 */
	public IntermediateCrossover() {
		this(DEFAULT_ALTER_PROBABILITY, 0);
	}

	@Override
	protected int crossover(final MSeq v, final MSeq w) {
		final Random random = RandomRegistry.getRandom();

		final double min = v.get(0).getMin().doubleValue();
		final double max = v.get(0).getMax().doubleValue();

		boolean changed = false;
		for (int i = 0, n = min(v.length(), w.length()); i < n; ++i) {
			final double vi = v.get(i).doubleValue();
			final double wi = w.get(i).doubleValue();

			double t, s;
			do {
				final double a = nextDouble(random, -_p, 1 + _p);
				final double b = nextDouble(random, -_p, 1 + _p);

				t = a*vi + (1 - a)*wi;
				s = b*wi + (1 - b)*vi;
			} while (t < min || s < min || t >= max || s >= max);

			v.set(i, v.get(i).newInstance(t));
			w.set(i, w.get(i).newInstance(s));
		}

		return 2;
	}

	@Override
	public int hashCode() {
		return Hash.of(getClass()).and(super.hashCode()).value();
	}

	@Override
	public boolean equals(final Object obj) {
		return obj instanceof IntermediateCrossover && super.equals(obj);
	}

	@Override
	public String toString() {
		return format("%s[p=%f]", getClass().getSimpleName(), _probability);
	}

}