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.mogulHC;

class BaseD {

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

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

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

    n_variables = n_var;

    intervalos = new TipoIntervalo[n_variables][MaxEtiquetas][3];
    BaseDatos = new Difuso[n_variables][MaxEtiquetas];

    for (i = 0; i < n_variables; i++) {
      BaseDatos[i] = new Difuso[MaxEtiquetas];
      intervalos[i] = new TipoIntervalo[MaxEtiquetas][3];
      for (j = 0; j < MaxEtiquetas; j++) {
        BaseDatos[i][j] = new Difuso();
        intervalos[i][j] = new TipoIntervalo[3];
        for (k = 0; k < 3; k++) {
          intervalos[i][j][k] = new TipoIntervalo();
        }
      }
    }

    n_etiquetas = new int[n_variables];
    S = new double[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);
      //System.out.println("Max: " + extremos[var].max + "  Min: " +extremos[var].min);
      //System.out.println("Marca: " + marca + "  Netiquetas: " + n_etiquetas[var]);
      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][0].min = punto[0] - punto_medio[0];
        intervalos[var][etq][0].max = punto[0] + punto_medio[0];
        intervalos[var][etq][1].min = punto[1] - punto_medio[0];
        intervalos[var][etq][1].max = punto[1] + punto_medio[1];
        intervalos[var][etq][2].min = punto[2] - punto_medio[1];
        intervalos[var][etq][2].max = punto[2] + punto_medio[1];
      }
    }

    /* Inicialization of the vector of values for adapting the generic sigma of the ES */
    for (var = 0; var < n_variables; var++) {
      S[var] = (BaseDatos[var][0].x1 - BaseDatos[var][0].x0) / 4.0;
    }

  }

  /** DB to String */
  public String BDtoString() {
    int i, j;
    String cadena = "";

    for (i = 0; i < n_variables; i++) {
      cadena += "  Variable " + (i + 1) + ":\n";
      for (j = 0; j < n_etiquetas[i]; j++) {
        cadena += "    Label " + (j + 1) + ": (" + BaseDatos[i][j].x0 + "," +
            BaseDatos[i][j].x1 + "," + BaseDatos[i][j].x3 + ")\n";
      }

      cadena += "\n";
    }

    return (cadena);
  }
}