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/*
 * PhrGeneticAlgrithm.java 0.1.1 11th December 2000
 *
 * Copyright (C) 2000 Adam Kirby
 *
 * 
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License
 * as published by the Free Software Foundation; either version 2
 * 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 General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
 */

package jm.music.tools.ga;

import jm.music.data.Phrase;

/**
 * @author Adam Kirby
 * @version 0.1.1, 11th December 2000
 */
public class PhrGeneticAlgorithm {
    protected Phrase[] population;

    protected double[] fitness;

    protected PopulationInitialiser populationInitialiser;

    protected FitnessEvaluater fitnessEvaluater;

    protected TerminationCriteria terminationCriteria;

    protected ParentSelector parentSelector;

    protected Recombiner recombiner;

    protected Mutater mutater;

    protected SurvivorSelector survivorSelector;

    protected int beatsPerBar;

    protected long iteration;

    protected double initialLength;

    protected int initialSize, originalSize;

    protected boolean finished;

    public PhrGeneticAlgorithm(Phrase phrase,
                               int beatsPerBar,
                               PopulationInitialiser populationInitialiser,
                               FitnessEvaluater fitnessEvaluater,
                               TerminationCriteria terminationCriteria,
                               ParentSelector parentSelector,
                               Recombiner recombiner,
                               Mutater mutater,
                               SurvivorSelector survivorSelector) {

        this.beatsPerBar = beatsPerBar;
        initialLength = phrase.getEndTime();
        initialSize = phrase.size();
        originalSize = initialSize;

        this.populationInitialiser = populationInitialiser;
        this.fitnessEvaluater = fitnessEvaluater;
        this.terminationCriteria = terminationCriteria;
        this.parentSelector = parentSelector;
        this.recombiner = recombiner;
        this.mutater = mutater;
        this.survivorSelector = survivorSelector;

        setUpNewPopulation(phrase);

    }

    private static int rand(final int left, final int right) {
        return left + (int) (Math.random() * (right - left)) + 1;
    }

    public void setUpNewPopulation(Phrase phrase) {
        iteration = 0;
        population = populationInitialiser.initPopulation(phrase, beatsPerBar);
        fitness = fitnessEvaluater.evaluate(population);
    }

    public void setBeatsPerBar(int beats) {
        this.beatsPerBar = beats;
    }

    public void zeroInitialSize() {
        this.originalSize = this.initialSize;
        this.initialSize = 0;
    }

    public void restoreInitialSize() {
        this.initialSize = this.originalSize;
    }

    public long getIteration() {
        return iteration;
    }

    /**
     * Evolve the population through one generation.
     */
    public boolean iterate() {
        finished = terminationCriteria.isFinished(population);
        if (!finished) {
            iteration++;

            Phrase[] parents = parentSelector.selectParents(population, fitness);
            Phrase[] recombined = recombiner.recombine(
                    parents, fitness, initialLength, initialSize, beatsPerBar);
            Phrase[] children = mutater.mutate(recombined, initialLength, initialSize, beatsPerBar);

            double[] parentsFitness = fitnessEvaluater.evaluate(children);
            population = survivorSelector.selectSurvivors(
                    population, fitness, children,
                    parentsFitness);
            fitness = fitnessEvaluater.evaluate(population);

            return true;
        } else {
            return false;
        }
    }

    /**
     * Evolve the population through the specified number of generations.
     */
    public long iterate(long iterations) {

        long iterationsProcessed = iterations;
        for (int i = 0; i < iterations; i++) {
            iterate();
            if (finished) {
                iterationsProcessed = i;
                break;
            }
        }
        return iterationsProcessed;
    }

    public double[] getFitness() {
        return fitness;
    }

    public Phrase[] getPopulation() {
        return population;
    }

    /**
     * Returns the population ordered by fitness with better fitness' first.
     */
    public Phrase[] getOrderedPopulation() {
        quicksort();
        return population;
    }

    private void quicksort() {
        quicksort(0, population.length - 1);
    }

    private void quicksort(final int left, final int right) {
        int last;

        if (left >= right) {
            return;
        }
        swap(left, rand(left, right));
        last = left;
        for (int i = left + 1; i <= right; i++) {
            if (fitness[i] < fitness[left]) {
                swap(++last, i);
            }
        }
        swap(left, last);
        quicksort(left, last - 1);
        quicksort(last + 1, right);
    }

    private void swap(final int i, final int j) {
        Phrase tempPhrase = population[i];
        population[i] = population[j];
        population[j] = tempPhrase;

        double tempDouble = fitness[i];
        fitness[i] = fitness[j];
        fitness[j] = tempDouble;
    }

    public double getBestFitness() {
        double bestFitness = 0;
        int index = -1;
        for (int i = 0; i < fitness.length; i++) {
            if (fitness[i] > bestFitness) {
                bestFitness = fitness[i];
                index = i;
            }
        }
        return bestFitness;
    }

    public double getAverageFitness() {
        double fitnessCount = 0.0;
        for (int i = 0; i < fitness.length; i++) {
            fitnessCount += fitness[i];
        }
        return fitnessCount / fitness.length;
    }

    public double getStandardDeviation() {
        double avgFitness = getAverageFitness();
        double stDevCount = 0.0;
        for (int i = 0; i < fitness.length; i++) {
            stDevCount += (avgFitness - fitness[i]) * (avgFitness - fitness[i]);
        }
        return Math.sqrt(stDevCount) / fitness.length;
    }

    public Phrase getBestIndividual() {
        double bestFitness = Double.MAX_VALUE;
        int index = -1;
        for (int i = 0; i < fitness.length; i++) {
            if (fitness[i] < bestFitness) {
                bestFitness = fitness[i];
                index = i;
            }
        }
        return population[index];
    }

    public Mutater getMutater() {
        return mutater;
    }
}