/*
 * 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.ext.moea;

import static java.util.Objects.requireNonNull;

import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.function.ToIntFunction;
import java.util.stream.IntStream;

import io.jenetics.Gene;
import io.jenetics.Optimize;
import io.jenetics.Phenotype;
import io.jenetics.Selector;
import io.jenetics.util.ISeq;
import io.jenetics.util.ProxySorter;
import io.jenetics.util.Seq;

/**
 * This selector selects the first {@code count} elements of the population,
 * which has been sorted by the <em>Crowded-Comparison Operator</em>, as
 * described in <a href="http://ieeexplore.ieee.org/document/996017/">
 *     A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II</a>
 * <p>
 *  <b>Reference:</b><em>
 *      K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist
 *      multiobjective genetic algorithm: NSGA-II. Trans. Evol. Comp 6, 2
 *      (April 2002), 182-197. DOI=<a href="http://dx.doi.org/10.1109/4235.996017">
 *          10.1109/4235.996017</a></em>
 *
 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
 * @version 4.1
 * @since 4.1
 */
public class NSGA2Selector<
        G extends Gene<?, G>,
        C extends Comparable<? super C>
>
        implements Selector<G, C>
{

        private final Comparator<Phenotype<G, C>> _dominance;
        private final ElementComparator<Phenotype<G, C>> _comparator;
        private final ElementDistance<Phenotype<G, C>> _distance;
        private final ToIntFunction<Phenotype<G, C>> _dimension;

        /**
         * Creates a new {@code NSGA2Selector} with the functions needed for
         * handling the multi-objective result type {@code C}. For the {@link Vec}
         * classes, a selector is created like in the following example:
         * {@snippet lang="java":
         * new NSGA2Selector<>(
         *     Vec<T>::dominance,
         *     Vec<T>::compare,
         *     Vec<T>::distance,
         *     Vec<T>::length
         * );
         * }
         *
         * @see #ofVec()
         *
         * @param dominance the pareto dominance comparator
         * @param comparator the vector element comparator
         * @param distance the vector element distance
         * @param dimension the dimensionality of vector type {@code C}
         */
        public NSGA2Selector(
                final Comparator<? super C> dominance,
                final ElementComparator<? super C> comparator,
                final ElementDistance<? super C> distance,
                final ToIntFunction<? super C> dimension
        ) {
                requireNonNull(dominance);
                requireNonNull(comparator);
                requireNonNull(distance);
                requireNonNull(dimension);

                _dominance = (a, b) -> dominance.compare(a.fitness(), b.fitness());
                _comparator = comparator.map(Phenotype::fitness);
                _distance = distance.map(Phenotype::fitness);
                _dimension = v -> dimension.applyAsInt(v.fitness());
        }

        @Override
        public ISeq<Phenotype<G, C>> select(
                final Seq<Phenotype<G, C>> population,
                final int count,
                final Optimize opt
        ) {
                final CrowdedComparator<Phenotype<G, C>> cc = new CrowdedComparator<>(
                        population,
                        opt,
                        _dominance,
                        _comparator,
                        _distance,
                        _dimension
                );

                final int[] idx = ProxySorter.sort(
                        init(new int[population.size()]),
                        population.size(),
                        (a, i, j) -> cc.compare(a[j], a[i])
                );

                final List<Phenotype<G, C>> result = new ArrayList<>();
                while (result.size() < count) {
                        IntStream.of(idx)
                                .limit(count - result.size())
                                .mapToObj(population)
                                .forEach(result::add);
                }

                return ISeq.of(result);
        }

        private static int[] init(final int[] indexes) {
                for (int i = 0; i < indexes.length; ++i) indexes[i] = i;
                return indexes;
        }

        /**
         * Return a new selector for the given result type {@code V}. This method is
         * a shortcut for
         * {@snippet lang="java":
         * new NSGA2Selector<>(
         *     Vec<T>::dominance,
         *     Vec<T>::compare,
         *     Vec<T>::distance,
         *     Vec<T>::length
         * );
         * }
         *
         * @param <G> the gene type
         * @param <T> the array type, e.g. {@code double[]}
         * @param <V> the multi object result type vector
         * @return a new selector for the given result type {@code V}
         */
        public static <G extends Gene<?, G>, T, V extends Vec<T>>
        NSGA2Selector<G, V> ofVec() {
                return new NSGA2Selector<>(
                        Vec::dominance,
                        Vec::compare,
                        Vec::distance,
                        Vec::length
                );
        }

}