package eva2.optimization.operator.crossover;

import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.individuals.InterfaceESIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;

/**
 * The children are randomized intermediate combinations of the parents,
 * namely c[i]=Sum_j (r_i * p_ji)
 * where r_i are uniform random numbers normed to the sum of one and
 * p_ji is the i-th component of parent j.
 */
@Description("This is an arithmetical crossover between m ES individuals.")
public class CrossoverESArithmetical implements InterfaceCrossover, java.io.Serializable {

    private InterfaceOptimizationProblem optimizationProblem;

    public CrossoverESArithmetical() {
    }

    public CrossoverESArithmetical(CrossoverESArithmetical c) {
        this.optimizationProblem = c.optimizationProblem;
    }

    /**
     * This method will enable you to clone a given mutation operator
     *
     * @return The clone
     */
    @Override
    public Object clone() {
        return new CrossoverESArithmetical(this);
    }

    /**
     * This method performs crossover on two individuals. If the individuals do
     * not implement InterfaceGAIndividual, then nothing will happen.
     *
     * @param indy1    The first individual
     * @param partners The second individual
     */
    @Override
    public AbstractEAIndividual[] mate(AbstractEAIndividual indy1, Population partners) {
        AbstractEAIndividual[] result = null;
        double[][] parents, children;

        result = new AbstractEAIndividual[partners.size() + 1];
        result[0] = (AbstractEAIndividual) (indy1).clone();
        for (int i = 0; i < partners.size(); i++) {
            result[i + 1] = (AbstractEAIndividual) partners.get(i).clone();
        }
        //for (int i = 0; i < result.length; i++) System.out.println("Before Crossover: " +result[i].getSolutionRepresentationFor());

        if ((indy1 instanceof InterfaceESIndividual) && (partners.get(0) instanceof InterfaceESIndividual)) {
            double intermediate;
            parents = new double[partners.size() + 1][];
            children = new double[partners.size() + 1][];
            for (int i = 0; i < result.length; i++) {
                parents[i] = new double[((InterfaceESIndividual) result[i]).getDGenotype().length];
                children[i] = new double[parents[i].length];
                System.arraycopy(((InterfaceESIndividual) result[i]).getDGenotype(), 0, parents[i], 0, parents[i].length);
                System.arraycopy(((InterfaceESIndividual) result[i]).getDGenotype(), 0, children[i], 0, parents[i].length);
            }

            double[] alpha = new double[parents.length];
            double sum = 0;
            for (int i = 0; i < children.length; i++) {
                sum = 0;
                for (int j = 0; j < alpha.length; j++) {
                    alpha[j] = RNG.randomDouble(0, 1);
                    sum += alpha[j];
                }
                for (int j = 0; j < alpha.length; j++) {
                    alpha[j] /= sum;
                }
                for (int n = 0; n < children[i].length; n++) {
                    children[i][n] = 0;
                    for (int m = 0; m < parents.length; m++) {
                        children[i][n] += alpha[m] * parents[m][n];
                    }
                }
            }
            // write the result back
            for (int i = 0; i < result.length; i++) {
                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
            }
        }
        //in case the crossover was successful lets give the mutation operators a chance to mate the strategy parameters
        for (int i = 0; i < result.length; i++) {
            result[i].getMutationOperator().crossoverOnStrategyParameters(indy1, partners);
        }
        //for (int i = 0; i < result.length; i++) System.out.println("After Crossover: " +result[i].getSolutionRepresentationFor());
        return result;
    }

    /**
     * This method allows you to evaluate wether two crossover operators
     * are actually the same.
     *
     * @param crossover The other crossover operator
     */
    @Override
    public boolean equals(Object crossover) {
        return crossover instanceof CrossoverESArithmetical;
    }

    /**
     * This method will allow the crossover operator to be initialized depending on the
     * individual and the optimization problem. The optimization problem is to be stored
     * since it is to be called during crossover to calculate the exogene parameters for
     * the offsprings.
     *
     * @param individual The individual that will be mutated.
     * @param opt        The optimization problem.
     */
    @Override
    public void init(AbstractEAIndividual individual, InterfaceOptimizationProblem opt) {
        this.optimizationProblem = opt;
    }

    @Override
    public String getStringRepresentation() {
        return this.getName();
    }

    /**
     * This method allows the CommonJavaObjectEditorPanel to read the
     * name to the current object.
     *
     * @return The name.
     */
    public String getName() {
        return "ES arithmetical crossover";
    }
}
