/***********************************************************************
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/
**********************************************************************/
/**
*
* File: CHC.java
*
* The CHC evolutionary model for Instance Selection.
*
* @author Written by Salvador Garc�a (University of Granada) 20/07/2004
* @version 0.1
* @since JDK1.5
*
*/
package keel.Algorithms.Instance_Selection.CHC;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class CHC extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private int tamPoblacion;
private int nEval;
private double alfa;
private double r;
private double prob0to1Rec;
private double prob0to1Div;
private int kNeigh;
/**
* Default builder. Construct the algoritm by using the superclass builder.
*
*/
public CHC (String ficheroScript) {
super (ficheroScript);
}//end-method
/**
* Executes the algorithm
*/
public void ejecutar () {
int i, j, k, l;
int nClases;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
int ev = 0;
Cromosoma C[];
int baraje[];
int pos, tmp;
Cromosoma newPob[];
int d = datosTrain.length / 4;
int tamC;
Cromosoma pobTemp[];
long tiempo = System.currentTimeMillis();
/*Getting the number of different classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Random inicialization of the poblation*/
Randomize.setSeed (semilla);
poblacion = new Cromosoma[tamPoblacion];
baraje = new int[tamPoblacion];
for (i=0; i<tamPoblacion; i++)
poblacion[i] = new Cromosoma (datosTrain.length);
/*Initial evaluation of the poblation*/
for (i=0; i<tamPoblacion; i++)
poblacion[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
/*Until stop condition*/
while (ev < nEval) {
C = new Cromosoma[tamPoblacion];
/*Selection(r) of C(t) from P(t)*/
for (i=0; i<tamPoblacion; i++)
baraje[i] = i;
for (i=0; i<tamPoblacion; i++) {
pos = Randomize.Randint (i, tamPoblacion-1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
for (i=0; i<tamPoblacion; i++)
C[i] = new Cromosoma (datosTrain.length, poblacion[baraje[i]]);
/*Structure recombination in C(t) constructing C'(t)*/
tamC = recombinar (C, d);
newPob = new Cromosoma[tamC];
for (i=0, l=0; i<C.length; i++) {
if (C[i].esValido()) { //the cromosome must be copied to the new poblation C'(t)
newPob[l] = new Cromosoma (datosTrain.length, C[i]);
l++;
}
}
/*Structure evaluation in C'(t)*/
for (i=0; i<newPob.length; i++) {
newPob[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
ev++;
}
/*Selection(s) of P(t) from C'(t) and P(t-1)*/
Arrays.sort(poblacion);
Arrays.sort(newPob);
/*If the better of C' is worse than the worst of P(t-1), then there will no changes*/
if (tamC==0 || newPob[0].getCalidad() < poblacion[tamPoblacion-1].getCalidad()) {
d--;
} else {
pobTemp = new Cromosoma[tamPoblacion];
for (i=0, j=0, k=0; i<tamPoblacion && k<tamC; i++) {
if (poblacion[j].getCalidad() > newPob[k].getCalidad()) {
pobTemp[i] = new Cromosoma (datosTrain.length, poblacion[j]);
j++;
} else {
pobTemp[i] = new Cromosoma (datosTrain.length, newPob[k]);
k++;
}
}
if (k == tamC) { //there are cromosomes for copying
for (; i<tamPoblacion; i++) {
pobTemp[i] = new Cromosoma (datosTrain.length, poblacion[j]);
j++;
}
}
poblacion = pobTemp;
}
/*Last step of the algorithm*/
if (d < 0) {
for (i=1; i<tamPoblacion; i++) {
poblacion[i].divergeCHC (r, poblacion[0], prob0to1Div);
}
for (i=0; i<tamPoblacion; i++)
if (!(poblacion[i].estaEvaluado())) {
poblacion[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
ev++;
}
/*Reinicialization of d value*/
d = (int)(r*(1.0-r)*(double)datosTrain.length);
}
}
Arrays.sort(poblacion);
nSel = poblacion[0].genesActivos();
/*Construction of S set from the best cromosome*/
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i=0, l=0; i<datosTrain.length; i++) {
if (poblacion[0].getGen(i)) { //the instance must be copied to the solution
for (j=0; j<datosTrain[i].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
System.out.println("CHC "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
// trucar ficheroSalida -> fucking cheater!!!
String Subset = new String(ficheroSalida[0].substring(11, ficheroSalida[0].length()));
Subset = "../datasets/"+Subset; //Cipote done
// OutputIS.escribeSalida(Subset, conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
//OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
//Working on training
for ( i=0; i<datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i],conjS, nClases, clasesS, this.kNeigh);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
//Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
//Check time
for (i=0; i<realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0]= KNN.evaluate(datosTest[i],conjS, nClases, clasesS, this.kNeigh);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}//end-method
/**
* Function that determines the cromosomes who have to be crossed and the other ones who have to be removed
* It returns the number of remaining cromosomes in the poblation
*
* @param C Array of chromosomes to recombine
* @param d Minimun distance to recombine
*
* @return Number of chromosomes combinated
*/
private int recombinar (Cromosoma C[], int d) {
int i, j;
int distHamming;
int tamC = 0;
for (i=0; i<C.length/2; i++) {
distHamming = 0;
for (j=0; j<datosTrain.length; j++)
if (C[i*2].getGen(j) != C[i*2+1].getGen(j))
distHamming++;
if ((distHamming/2) > d) {
for (j=0; j<datosTrain.length; j++) {
if ((C[i*2].getGen(j) != C[i*2+1].getGen(j)) && Randomize.Rand() < 0.5) {
if (C[i*2].getGen(j)) C[i*2].setGen(j,false);
else if (Randomize.Rand() < prob0to1Rec) C[i*2].setGen(j,true);
if (C[i*2+1].getGen(j)) C[i*2+1].setGen(j,false);
else if (Randomize.Rand() < prob0to1Rec) C[i*2+1].setGen(j,true);
}
}
tamC += 2;
} else {
C[i*2].borrar();
C[i*2+1].borrar();
}
}
return tamC;
}//end-method
/**
* Reads configuration script, and extracts its contents.
*
* @param ficheroScript Name of the configuration script
*
*/
public void leerConfiguracion (String ficheroScript) {
String fichero, linea, token;
StringTokenizer lineasFichero, tokens;
byte line[];
int i, j;
ficheroSalida = new String[2];
fichero = Fichero.leeFichero (ficheroScript);
lineasFichero = new StringTokenizer (fichero,"\n\r");
lineasFichero.nextToken();
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the name of training and test files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTraining = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroValidation = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTest = new String (line,i,j-i);
/*Obtainin the path and the base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the name of output files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[0] = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[1] = new String (line,i,j-i);
/*Getting the seed*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
semilla = Long.parseLong(tokens.nextToken().substring(1));
/*Getting the size of the poblation and the number of evaluations*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
tamPoblacion = Integer.parseInt(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
nEval = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the equilibrate alfa factor and r value*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
alfa = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
r = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the probability of change bits*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
prob0to1Rec = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
prob0to1Div = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
kNeigh = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the type of distance function*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
distanceEu = tokens.nextToken().substring(1).equalsIgnoreCase("Euclidean")?true:false;
}//end-method
}//end-class