/***********************************************************************

	This file is part of KEEL-software, the Data Mining tool for regression, 
	classification, clustering, pattern mining and so on.

	Copyright (C) 2004-2010
	
	F. Herrera (herrera@decsai.ugr.es)
    L. Sánchez (luciano@uniovi.es)
    J. Alcalá-Fdez (jalcala@decsai.ugr.es)
    S. García (sglopez@ujaen.es)
    A. Fernández (alberto.fernandez@ujaen.es)
    J. Luengo (julianlm@decsai.ugr.es)

	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 3 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, see http://www.gnu.org/licenses/
  
**********************************************************************/

package keel.Algorithms.RE_SL_Postprocess.MamSelect;
import java.lang.Math;
import org.core.*;

class AG  {
	public double prob_cruce, prob_mutacion;
	public int Mu_next, Trials;
	public double Best_current_perf;
	public int Best_guy;
	public int long_poblacion, n_genes;

	public int [] sample;
	public int last;

	public Structure [] Old;
	public Structure [] New;
	public Structure [] temp;
	public Adap fun_adap;


	public AG (int n_poblacion, int genes, double cruce, double mutacion, Adap funcion) {
		int i;

		long_poblacion = n_poblacion;
		n_genes = genes;
		prob_cruce = cruce;
		prob_mutacion = mutacion / (double) n_genes;
		fun_adap = funcion;

		sample = new int[long_poblacion];

		Old = new Structure[long_poblacion];
		New = new Structure[long_poblacion];

		for (i=0; i<long_poblacion; i++) {
			Old[i] = new Structure(n_genes);
			New[i] = new Structure(n_genes);
		}
	}


	public void Intercambio () {
		temp=Old;
		Old=New;
		New=temp;
	}

	/** Inicialization of the population */
	public void Initialize () {
		int i, j;

		last = (int) ((prob_cruce * long_poblacion) - 0.5);

		Trials = 0;

		if (prob_mutacion < 1.0) {
			Mu_next = (int) (Math.log(Randomize.Rand()) / Math.log(1.0 - prob_mutacion));
			Mu_next++;
		}

		else  Mu_next = 1;

		for (j=0; j<n_genes; j++)  New[0].Gene[j] = '1';
		New[0].n_e = 1;

		for (i=1; i < long_poblacion; i++) {
			for (j=0; j<n_genes; j++)
				if (Randomize.RandintClosed(0,1) == 0)  New[i].Gene[j] = '0';
				else  New[i].Gene[j] = '1';

			New[i].n_e = 1;
		}
	}


	/* Selection based on the Baker's Estocastic Universal Sampling */	
	void Select() {
		double expected, factor, perf, ptr, sum, rank_max, rank_min;
		int i, j, k, best, temp;
		
		rank_min = 0.75;

		/* we assign the ranking to each element:
		     The best: ranking = long_poblacion-1
			 The worse: ranking = 0 */
		for (i=0; i<long_poblacion; i++)  Old[i].n_e = 0;

		/* we look for the best ordered non element */
		for (i=0; i<long_poblacion-1; i++) {
			best = -1;
			perf = 0.0;
			for (j=0; j<long_poblacion; j++) {
				if ((Old[j].n_e==0) && (best==-1 || Old[j].Perf < perf)) {
					perf = Old[j].Perf;
					best = j;
				}
			}

			/* we assign the ranking */
			Old[best].n_e = long_poblacion - 1 - i;
		}

		/* we normalize the ranking */
		rank_max = 2.0 - rank_min;
		factor = (rank_max-rank_min)/(double)(long_poblacion-1);

		/* we assign the number of replicas of each chormosome according to the select probability */
		k = 0;
		ptr = Randomize.Rand ();
		for (sum = i = 0; i<long_poblacion; i++) {
			expected = rank_min + Old[i].n_e * factor;
			for (sum += expected; sum>=ptr; ptr++)  sample[k++] = i;
		}

		/* we complete the population if necessary */
		if (k != long_poblacion) {
			for (;k<long_poblacion; k++)  sample[k] = Randomize.RandintClosed (0, long_poblacion-1);
		}

		/* we shuffle the selected chromosomes */
		for (i=0; i<long_poblacion; i++) {
			j = Randomize.RandintClosed (i, long_poblacion-1);
			temp = sample[j];
			sample[j] = sample[i];
			sample[i] = temp;
		}

		/* we create the new population */
		for (i=0; i<long_poblacion; i++) {
			k = sample[i];
			for (j=0; j<n_genes; j++)  New[i].Gene[j] = Old[k].Gene[j];

			New[i].Perf = Old[k].Perf;
			New[i].n_e = 0;
		}
	}


	/* Multipoint Crossover */
	void Cruce_Multipunto () {
		int mom, dad, xpoint1, xpoint2, i, j;
		char temp;

		for (mom=0; mom<last; mom+=2) {
			dad = mom+1;

			/* we generate the 2 crossing points */
			xpoint1 = Randomize.RandintClosed (0,n_genes-1);

			if (xpoint1 != n_genes-1)
				xpoint2 = Randomize.RandintClosed (xpoint1 + 1, n_genes - 1);

			else  xpoint2 = n_genes - 1;

			/* we cross the individuals between xpoint1 and xpoint2 */
			for (i=xpoint1; i<=xpoint2; i++) {
				temp = New[mom].Gene[i];
				New[mom].Gene[i] = New[dad].Gene[i];
				New[dad].Gene[i] = temp;
			}

			New[mom].n_e = 1;
			New[dad].n_e = 1;
		}
	}


	/* Operador de Mutacion Uniforme */
	void Mutacion_Uniforme () {
		int posiciones, i, j;
		double m;

		posiciones = n_genes * long_poblacion;

		if (prob_mutacion>0) {
			while (Mu_next<posiciones) {
				/* we determinate the chromosome and the gene */
				i = Mu_next / n_genes;
				j = Mu_next % n_genes;

				/* we mutate the gene */
				if (New[i].Gene[j]=='0')  New[i].Gene[j]='1';
				else  New[i].Gene[j]='0';

				
				New[i].n_e=1;

				/* we calculate the next position */
				if (prob_mutacion<1) {
					m = Randomize.Rand();
					Mu_next += (int) (Math.log(m) / Math.log(1.0 - prob_mutacion)) + 1;
				}
				else  Mu_next += 1;
			}
		}

		Mu_next -= posiciones;
	}

	/** Fitness Function */
	void Evaluate() {
		double performance;
		int i, j;

		for (i=0; i<long_poblacion; i++) {
			/* if the chromosome aren't evaluated, it's evaluate */
			if (New[i].n_e == 1) {
				New[i].Perf = fun_adap.eval (New[i].Gene);
				performance = New[i].Perf;
				New[i].n_e = 0;
				Trials++;       /* we increment the number of evaluated chromosomes */

			}
			else  performance = New[i].Perf;

			/* we calculate the position of the best individual */
			if (i == 0) {
				Best_current_perf = performance;
				Best_guy = 0;
			}
			else if (performance < Best_current_perf) {
				Best_current_perf = performance;
				Best_guy = i;
			}
		}
	}


	/* Elitist selection */
	void Elitist () {
		 int i, k, found;

		 /* if the best individual of the old population aren't in the new population, we remplace the last individual for this */
		 for (i=0, found=0; i<long_poblacion && (found==0); i++)
			for (k=0, found=1; (k<n_genes) && (found==1); k++)
				if (New[i].Gene[k] != Old[Best_guy].Gene[k])  found = 0;

		 if (found == 0) {
			for (k=0; k<n_genes; k++)
				New[long_poblacion-1].Gene[k] = Old[Best_guy].Gene[k];

			New[long_poblacion-1].Perf = Old[Best_guy].Perf;
			New[long_poblacion-1].n_e = 0;
		}
	}

	/** Returns the best solution*/
	public char [] solucion () {
		return (New[Best_guy].Gene);
	}

	/** Returns the fitness of the best solution */
	public double solucion_ec () {
		return (New[Best_guy].Perf);
	}
}

