/***********************************************************************
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: SGA.java
*
* Steady-State Genetic algorithm for Instance Selection.
*
* @author Written by Salvador Garc�a (University of Granada) 20/07/2004
* @version 0.1
* @since JDK1.5
*
*/
package keel.Algorithms.Preprocess.Instance_Selection.SGA;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class SGA extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private double pMutacion1to0;
private double pMutacion0to1;
private double pCruce;
private int tamPoblacion;
private int nEval;
private double alfa;
private boolean torneo;
private int kNeigh;
/**
* Default builder. Construct the algoritm by using the superclass builder.
*
*/
public SGA (String ficheroScript) {
super (ficheroScript);
}//end-method
/**
* Executes the algorithm
*/
public void ejecutar () {
int i, j, l;
int nClases;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
int ev = 0;
double prob[];
double NUmax = 1.5;
double NUmin = 0.5; //used for lineal ranking
double aux;
double pos1, pos2;
int sel1, sel2, comp1, comp2;
Cromosoma newPob[];
long tiempo = System.currentTimeMillis();
/*Getting the number of different clases*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Random inicialization of the population*/
Randomize.setSeed (semilla);
poblacion = new Cromosoma[tamPoblacion];
for (i=0; i<tamPoblacion; i++)
poblacion[i] = new Cromosoma (datosTrain.length);
/*Initial evaluation of the population*/
for (i=0; i<tamPoblacion; i++)
poblacion[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
if (torneo) {
while (ev < nEval) {
newPob = new Cromosoma[2];
/*Binary tournament selection*/
comp1 = Randomize.Randint(0,tamPoblacion-1);
do {
comp2 = Randomize.Randint(0,tamPoblacion-1);
} while (comp2 == comp1);
if (poblacion[comp1].getCalidad() > poblacion[comp2].getCalidad())
sel1 = comp1;
else sel1 = comp2;
comp1 = Randomize.Randint(0,tamPoblacion-1);
do {
comp2 = Randomize.Randint(0,tamPoblacion-1);
} while (comp2 == comp1);
if (poblacion[comp1].getCalidad() > poblacion[comp2].getCalidad())
sel2 = comp1;
else sel2 = comp2;
if (Randomize.Rand() < pCruce) { //there is cross
crucePMX (poblacion, newPob, sel1, sel2);
} else { //there is not cross
newPob[0] = new Cromosoma (datosTrain.length, poblacion[sel1]);
newPob[1] = new Cromosoma (datosTrain.length, poblacion[sel2]);
}
/*Mutation of the cromosomes*/
for (i=0; i<2; i++)
newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the population*/
for (i=0; i<2; i++)
if (!(newPob[i].estaEvaluado())) {
newPob[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
ev++;
}
/*Replace the two worst*/
Arrays.sort(poblacion);
poblacion[tamPoblacion-2] = new Cromosoma (datosTrain.length, newPob[0]);
poblacion[tamPoblacion-1] = new Cromosoma (datosTrain.length, newPob[1]);
}
} else {
/*Get the probabilities of lineal ranking in case of not use binary tournament*/
prob = new double[tamPoblacion];
for (i=0; i<tamPoblacion; i++) {
aux= (double)(NUmax-NUmin)*((double)i/(tamPoblacion-1));
prob[i]=(double)(1.0/(tamPoblacion)) * (NUmax-aux);
}
for (i=1; i<tamPoblacion; i++)
prob[i] = prob[i] + prob[i-1];
while (ev < nEval) {
/*Sort the population by quality criterion*/
Arrays.sort(poblacion);
newPob = new Cromosoma[2];
pos1 = Randomize.Rand();
pos2 = Randomize.Rand();
for (j=0; j<tamPoblacion && prob[j]<pos1; j++);
sel1 = j;
for (j=0; j<tamPoblacion && prob[j]<pos2; j++);
sel2 = j;
if (Randomize.Rand() < pCruce) { //there is cross
crucePMX (poblacion, newPob, sel1, sel2);
} else { //there is not cross
newPob[0] = new Cromosoma (datosTrain.length, poblacion[sel1]);
newPob[1] = new Cromosoma (datosTrain.length, poblacion[sel2]);
}
/*Mutation of the cromosomes*/
for (i=0; i<2; i++)
newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the population*/
for (i=0; i<2; i++)
if (!(newPob[i].estaEvaluado())) {
newPob[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
ev++;
}
/*Replace the two worst*/
poblacion[tamPoblacion-2] = new Cromosoma (datosTrain.length, newPob[0]);
poblacion[tamPoblacion-1] = new Cromosoma (datosTrain.length, newPob[1]);
}
}
nSel = poblacion[0].genesActivos();
/*Building of S set from the best cromosome obtained*/
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[0].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("SGA "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
OutputIS.escribeSalida(ficheroSalida[0], conjR, conjN, conjM, clasesS, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}//end-method
/**
* PMX cross operator
*
* @param poblacion Population of chromosomes
* @param newPob New population
* @param sel1 First parent
* @param sel2 Second parent
*/
public void crucePMX (Cromosoma poblacion[], Cromosoma newPob[], int sel1, int sel2) {
int e1, e2;
int limSup, limInf;
int i;
boolean temp[];
temp = new boolean[datosTrain.length];
e1 = Randomize.Randint (0, datosTrain.length-1);
e2 = Randomize.Randint (0, datosTrain.length-1);
if (e1 > e2) {
limSup = e1;
limInf = e2;
} else {
limSup = e2;
limInf = e1;
}
for (i=0; i<datosTrain.length; i++) {
if (i < limInf || i > limSup)
temp[i] = poblacion[sel1].getGen(i);
else temp[i] = poblacion[sel2].getGen(i);
}
newPob[0] = new Cromosoma (temp);
for (i=0; i<datosTrain.length; i++) {
if (i < limInf || i > limSup)
temp[i] = poblacion[sel2].getGen(i);
else temp[i] = poblacion[sel1].getGen(i);
}
newPob[1] = new Cromosoma (temp);
}//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 names 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++);
ficheroTest = new String (line,i,j-i);
/*Getting the path and base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the names 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 mutation and cross probability*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pMutacion1to0 = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pMutacion0to1 = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pCruce = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the size of the population and 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 alfa equilibrate factor*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
alfa = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the type of selection*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
token = token.substring(1);
if (token.equalsIgnoreCase("binary_tournament")) torneo = true;
else torneo = false;
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