BaseD.java

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

	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. S�nchez (luciano@uniovi.es)
    J. Alcal�-Fdez (jalcala@decsai.ugr.es)
    S. Garc�a (sglopez@ujaen.es)
    A. Fern�ndez (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_Methods.mogulIRL;

/**
 * <p>Title: </p>
 *
 * <p>Description: </p>
 *
 * <p>Copyright: Copyright (c) 2007</p>
 *
 * <p>Company: </p>
 *
 * @author not attributable
 * @version 1.0
 */
class BaseD {

        public Difuso [][]BaseDatos;
        public int n_variables;
        public int [] n_etiquetas;

        public TipoIntervalo [] extremos;
        public TipoIntervalo [][]intervalos;


        public BaseD (int MaxEtiquetas, int n_var) {
                int i, j;

                n_variables = n_var;

                intervalos = new TipoIntervalo [n_variables][MaxEtiquetas];
                BaseDatos = new Difuso[n_variables][MaxEtiquetas];

                for (i=0; i < n_variables; i++) {
                        BaseDatos[i] = new Difuso[MaxEtiquetas];
                        intervalos[i] = new TipoIntervalo [MaxEtiquetas];
                        for (j=0; j < MaxEtiquetas; j++) {
                                BaseDatos[i][j] = new Difuso();
                                intervalos[i][j] = new TipoIntervalo();
                        }
                }

                n_etiquetas = new int[n_variables];

                extremos = new TipoIntervalo[n_variables];
                for (i=0; i<n_variables; i++)  extremos[i] = new TipoIntervalo();
        }



        /** Rounds the generated value for the semantics */
        public double Asigna (double val, double tope) {
                if ((val > -1E-4) && (val < 1E-4))  return (0);
                if ((val > tope-1E-4) && (val < tope+1E-4))  return (tope);

                return (val);
        }


        /** Generates the semantics of the linguistic variables with triangular fuzzy sets and the mutation intervals to mutate */
        public void Semantica () {
                int var, etq;
                double marca, valor;
                double [] punto = new double[3];
                double [] punto_medio = new double[2];

                /* we generate the fuzzy partitions of the variables */
                for (var=0; var<n_variables; var++) {
                        marca = (extremos[var].max-extremos[var].min) / ((double)n_etiquetas[var]-1);
                        for (etq=0; etq<n_etiquetas[var]; etq++) {
                                valor = extremos[var].min+marca*(etq-1);
                                BaseDatos[var][etq].x0 = Asigna (valor,extremos[var].max);
                                valor = extremos[var].min + marca * etq;
                                BaseDatos[var][etq].x1 = Asigna (valor,extremos[var].max);
                                BaseDatos[var][etq].x2 = BaseDatos[var][etq].x1;
                                valor = extremos[var].min + marca * (etq + 1);
                                BaseDatos[var][etq].x3 = Asigna (valor,extremos[var].max);
                                BaseDatos[var][etq].y = 1;
                                BaseDatos[var][etq].Nombre = "V" + (var+1);
                                BaseDatos[var][etq].Etiqueta = "E" + (etq+1);
                        }
                }

                /* we generate the mutation intervals for each gene */
                for (var=0; var < n_variables; var++) {
                        for (etq=0; etq < n_etiquetas[var]; etq++) {
                                punto[0] = BaseDatos[var][etq].x0;
                                punto[1] = BaseDatos[var][etq].x1;
                                punto[2] = BaseDatos[var][etq].x3;
                                punto_medio[0] = (punto[1] - punto[0]) / 2.0;
                                punto_medio[1] = (punto[2] - punto[1]) / 2.0;
                                intervalos[var][etq].min = punto[0] - punto_medio[0];
                                intervalos[var][etq].max = punto[2] + punto_medio[1];
                        }
                }
        }

}