/*
 * Java Genetic Algorithm Library (jenetics-8.0.0).
 * Copyright (c) 2007-2024 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 (franz.wilhelmstoetter@gmail.com)
 */
package io.jenetics;

import static java.lang.String.format;
import static io.jenetics.internal.math.Randoms.indexes;

import java.util.random.RandomGenerator;

import io.jenetics.internal.math.Subset;
import io.jenetics.util.MSeq;
import io.jenetics.util.RandomRegistry;
import io.jenetics.util.Seq;

/**
 * <p>
 * An enhanced genetic algorithm (EGA) combine elements of existing solutions in
 * order to create a new solution, with some of the properties of each parent.
 * Recombination creates a new chromosome by combining parts of two (or more)
 * parent chromosomes. This combination of chromosomes can be made by selecting
 * one or more crossover points, splitting these chromosomes on the selected
 * points, and merge those portions of different chromosomes to form new ones.
 * </p>
 * <p>
 * The recombination probability <i>P(r)</i> determines the probability that a
 * given individual (genotype, not gene) of a population is selected for
 * recombination. The (<i>mean</i>) number of changed individuals depend on the
 * concrete implementation and can be vary from
 * <i>P(r)</i>&middot;<i>N<sub>G</sub></i> to
 * <i>P(r)</i>&middot;<i>N<sub>G</sub></i>&middot;<i>O<sub>R</sub></i>, where
 * <i>O<sub>R</sub></i> is the order of the recombination, which is the number
 * of individuals involved int the {@link #recombine} method.
 * </p>
 *
 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
 * @since 1.0
 * @version 6.0
 */
public abstract class Recombinator<
        G extends Gene<?, G>,
        C extends Comparable<? super C>
>
        extends AbstractAlterer<G, C>
{

        private final int _order;

        /**
         * Constructs an alterer with a given recombination probability.
         *
         * @param probability The recombination probability.
         * @param order the number of individuals involved in the
         *        {@link #recombine(MSeq, int[], long)} step
         * @throws IllegalArgumentException if the {@code probability} is not in the
         *         valid range of {@code [0, 1]} or the given {@code order} is
         *         smaller than two.
         */
        protected Recombinator(final double probability, final int order) {
                super(probability);
                if (order < 2) {
                        throw new IllegalArgumentException(format(
                                "Order must be greater than one, but was %d.", order
                        ));
                }
                _order = order;
        }

        /**
         * Return the number of individuals involved in the
         * {@link #recombine(MSeq, int[], long)} step.
         *
         * @return the number of individuals involved in the recombination step.
         */
        public int order() {
                return _order;
        }

        @Override
        public final AltererResult<G, C> alter(
                final Seq<Phenotype<G, C>> population,
                final long generation
        ) {
                final AltererResult<G, C> result;
                if (population.size() >= 2) {
                        final var random = RandomRegistry.random();
                        final int order = Math.min(_order, population.size());

                        final MSeq<Phenotype<G, C>> pop = MSeq.of(population);
                        final int count = indexes(random, population.size(), _probability)
                                .mapToObj(i -> individuals(i, population.size(), order, random))
                                .mapToInt(ind -> recombine(pop, ind, generation))
                                .sum();

                        result = new AltererResult<>(pop.toISeq(), count);
                } else {
                        result = new AltererResult<>(population.asISeq());
                }

                return result;
        }

        static int[] individuals(
                final int index,
                final int size,
                final int order,
                final RandomGenerator random
        ) {
                final int[] ind = Subset.next(random, size, order);

                // Find the correct slot for the "master" individual.
                // This prevents duplicate index entries.
                int i = 0;
                while (ind[i] < index && i < ind.length - 1) {
                        ++i;
                }
                ind[i] = index;

                return ind;
        }

        /**
         * Recombination template method. This method is called 0 to n times. It is
         * guaranteed that this method is only called by one thread.
         *
         * @param population the population to recombine
         * @param individuals the array with the indexes of the individuals which
         *        are involved in the <i>recombination</i> step. The length of the
         *        array is {@link #order()}. The first individual is the
         *        <i>primary</i> individual.
         * @param generation the current generation.
         * @return the number of genes that has been altered.
         */
        protected abstract int recombine(
                final MSeq<Phenotype<G, C>> population,
                final int[] individuals,
                final long generation
        );

}