/*********************************************************************** 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.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal; /** * <p> * @author Written by Alvaro Lopez * @version 1.0 * @since JDK1.6 * </p> */ public class Gene { /** * <p> * It is used for representing and handling a gene throughout the evolutionary learning * </p> */ private double[] displacements; /** * <p> * It creates a new gene by setting up its displacements * </p> * @param displacements The displacements values to set up for the gene */ public Gene(double[] displacements) { this.setDisplacements(displacements); } private void setDisplacements(double[] displacements) { this.displacements = new double[ displacements.length ]; for (int i=0; i < this.displacements.length; i++) this.displacements[i] = displacements[i]; } /** * <p> * It returns the displacements of a gene * </p> * @return An array of displacements values for the gene being considered */ public double[] getDisplacements() { return this.displacements; } /** * It computes the overlap factor for the membership functions involved in a gene * @return A value indicating the overlap factor */ public double calculateOverlapFactor(FuzzyAttribute uniform_fuzzy_attr) { int r; double x3_mf1, x0_mf2, x1_mf1, x0_mf1, overlap_factor; FuzzyRegion[] initial_fuzzy_regions; initial_fuzzy_regions = uniform_fuzzy_attr.getFuzzyRegions(); overlap_factor = 0.0; for (r=0; r < (this.displacements.length-1); r++) { x3_mf1 = initial_fuzzy_regions[r].getX3() + this.displacements[r]; x0_mf2 = initial_fuzzy_regions[r+1].getX0() + this.displacements[r+1]; if (x3_mf1 > x0_mf2) { x0_mf1 = initial_fuzzy_regions[r].getX0() + this.displacements[r]; x1_mf1 = initial_fuzzy_regions[r].getX1() + this.displacements[r]; overlap_factor += ( Math.max((x3_mf1 - x0_mf2) / (x1_mf1 - x0_mf1), 1.0) - 1.0); } } return overlap_factor; } /** * <p> * It allows to clone correctly a gene * </p> * @return A copy of the gene */ public Gene clone() { Gene gene = new Gene(this.displacements); return gene; } /** * <p> * It indicates whether some other gene is "equal to" this one * </p> * @param obj The reference object with which to compare * @return True if this gene is the same as the argument; False otherwise */ public boolean equals(Object obj) { Gene g = (Gene)obj; boolean ok = true; for (int i=0; i < this.displacements.length && ok; i++) if (g.displacements[i] != this.displacements[i]) ok = false; return ok; } /** * <p> * It returns a raw string representation of a gene * </p> * @return A raw string representation of the gene */ public String toString() { int i; String str = "["; for (i=0; i < this.displacements.length - 1; i++) { str += this.displacements[i] + "; "; } str += this.displacements[i] + "]"; return str; } }