/***********************************************************************
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.LQD.methods.FGFS_Minimum_Risk;
import java.io.BufferedReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.Vector;
/**
*
* File: fuzzy.java
*
* Properties and functions of individual of the population
*
* @author Written by Ana Palacios Jimenez (University of Oviedo) 25/006/2010
* @version 1.0
*/
public class IndMichigan {
fuzzy[][] X;
Vector<Vector<Float>> Y;
fuzzyRule individuo;
Interval fitness;
IndMichigan(fuzzy[][] x,Vector<Vector<Float>> y,Vector<fuzzyPartition> pentradas,int classes, int COST, int alfa,
Vector<Float> values_classes,Vector<Vector<fuzzy>> pesos,int instance, int asign_weight_rule,
String label,Vector<Float> p, int es_crisp) throws IOException
{
individuo= new fuzzyRule(pentradas,classes,asign_weight_rule);//initialize the individual
int contador=0;
if(es_crisp==0)
{
//Obtain the antecedents of the rule and the consequent
if(instance<(x.length))
individuo.obtain_rule(x,y,pentradas,classes,COST, alfa,values_classes,pesos,instance,label,p);
else
{
while(instance>=x.length)
{
instance=instance-x.length;
}
individuo.obtain_rule_random_eje(x,y,pentradas,classes,COST, alfa,values_classes,pesos,label,p, instance);
}
}
else
{
if(instance<(x.length))
individuo.obtain_rule(x,y,pentradas,classes,COST, alfa,values_classes,pesos,instance,label,p);
else
individuo.obtain_rule_random(x,y,pentradas,classes,COST, alfa,values_classes,pesos,label,p);
}
X=x;
Y=y;
}
//Obtain the new individual (rule) from two parents
IndMichigan(Vector<Integer> ant,fuzzy[][] x,Vector<Vector<Float>> y,Vector<fuzzyPartition> pentradas,int classes, int COST, int alfa,
Vector<Float> value_classes,Vector<Vector<fuzzy>> pesos,int asign_weight_rule,
String etiqueta,Vector<Float> p) throws IOException
{
individuo= new fuzzyRule(pentradas,classes, asign_weight_rule);
Integer[]a= new Integer[ant.size()];
for(int i=0;i<ant.size();i++)
{
a[i]= ant.get(i);
}
individuo.setantecedent(a);
individuo.calculateconsequent(x, y, pentradas, classes,COST,alfa,value_classes,pesos,etiqueta,p);
X=x;
Y=y;
}
public fuzzyRule getregla(){return individuo;}
public int size() { return individuo.size(); }
public Interval getfitness()
{
return fitness;
}
public fuzzy[][] getX()
{
return X;
}
public Vector<Vector<Float>> getY()
{
return Y;
}
public void asignaejemplos(fuzzy[][] x,Vector<Vector<Float>> y)
{
X=x;
Y=y;
}
}