/***********************************************************************
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 Menetic 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.SSMA;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class SSMA extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private int tamPoblacion;
private double nEval;
private double pCross;
private double pMut;
private int kNeigh;
/**
* Default builder. Construct the algoritm by using the superclass builder.
*
*/
public SSMA (String ficheroScript) {
super (ficheroScript);
}//end-method
/**
* Executes the algorithm
*/
public void ejecutar () {
int i, j, l;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
double ev = 0;
double dMatrix[][];
int sel1, sel2, comp1, comp2;
Cromosoma hijos[];
double umbralOpt;
boolean veryLarge;
double GAeffort=0, LSeffort=0, temporal;
double fAcierto=0, fReduccion=0;
int contAcierto=0, contReduccion=0;
int nClases;
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++;
if (datosTrain.length > 9000) {
veryLarge = true;
} else {
veryLarge = false;
}
if (veryLarge == false) {
/*Construct a distance matrix of the instances*/
dMatrix = new double[datosTrain.length][datosTrain.length];
for (i = 0; i < dMatrix.length; i++) {
for (j = i + 1; j < dMatrix[i].length; j++) {
dMatrix[i][j] = KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu);
}
}
for (i = 0; i < dMatrix.length; i++) {
dMatrix[i][i] = Double.POSITIVE_INFINITY;
}
for (i = 0; i < dMatrix.length; i++) {
for (j = i - 1; j >= 0; j--) {
dMatrix[i][j] = dMatrix[j][i];
}
}
} else {
dMatrix = null;
}
/*Random inicialization of the population*/
Randomize.setSeed (semilla);
poblacion = new Cromosoma[tamPoblacion];
for (i=0; i<tamPoblacion; i++)
poblacion[i] = new Cromosoma (kNeigh, datosTrain.length, dMatrix, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
/*Initial evaluation of the population*/
for (i=0; i<tamPoblacion; i++) {
poblacion[i].evaluacionCompleta(nClases, kNeigh, clasesTrain);
}
umbralOpt = 0;
/*Until stop condition*/
while (ev < nEval) {
Arrays.sort(poblacion);
if (fAcierto >= (double)poblacion[0].getFitnessAc()*100.0/(double)datosTrain.length) {
contAcierto++;
} else {
contAcierto=0;
}
fAcierto = (double)poblacion[0].getFitnessAc()*100.0/(double)datosTrain.length;
if (fReduccion >= (1.0-((double)poblacion[0].genesActivos()/(double)datosTrain.length))*100.0) {
contReduccion++;
} else {
contReduccion=0;
}
fReduccion = (1.0-((double)poblacion[0].genesActivos()/(double)datosTrain.length))*100.0;
if (contReduccion >= 10 || contAcierto >= 10){
if (Randomize.Randint(0,1)==0) {
if (contAcierto >= 10) {
contAcierto = 0;
umbralOpt++;
} else {
contReduccion = 0;
umbralOpt--;
}
} else {
if (contReduccion >= 10) {
contReduccion = 0;
umbralOpt--;
} else {
contAcierto = 0;
umbralOpt++;
}
}
}
/*Binary tournament selection*/
comp1 = Randomize.Randint(0,tamPoblacion-1);
do {
comp2 = Randomize.Randint(0,tamPoblacion-1);
} while (comp2 == comp1);
if (poblacion[comp1].getFitness() > poblacion[comp2].getFitness())
sel1 = comp1;
else sel1 = comp2;
comp1 = Randomize.Randint(0,tamPoblacion-1);
do {
comp2 = Randomize.Randint(0,tamPoblacion-1);
} while (comp2 == comp1);
if (poblacion[comp1].getFitness() > poblacion[comp2].getFitness())
sel2 = comp1;
else
sel2 = comp2;
hijos = new Cromosoma[2];
hijos[0] = new Cromosoma (kNeigh, poblacion[sel1], poblacion[sel2], pCross,datosTrain.length);
hijos[1] = new Cromosoma (kNeigh, poblacion[sel2], poblacion[sel1], pCross,datosTrain.length);
hijos[0].mutation (kNeigh, pMut, dMatrix, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
hijos[1].mutation (kNeigh, pMut, dMatrix, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
/*Evaluation of offsprings*/
hijos[0].evaluacionCompleta(nClases, kNeigh, clasesTrain);
hijos[1].evaluacionCompleta(nClases, kNeigh, clasesTrain);
ev+=2;
GAeffort += 2;
temporal = ev;
if (hijos[0].getFitness() > poblacion[tamPoblacion-1].getFitness() || Randomize.Rand() < 0.0625) {
ev += hijos[0].optimizacionLocal(nClases, kNeigh, clasesTrain,dMatrix,umbralOpt, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
}
if (hijos[1].getFitness() > poblacion[tamPoblacion-1].getFitness() || Randomize.Rand() < 0.0625) {
ev += hijos[1].optimizacionLocal(nClases, kNeigh, clasesTrain,dMatrix,umbralOpt, datosTrain, realTrain, nominalTrain, nulosTrain, distanceEu);
}
LSeffort += (ev - temporal);
/*Replace the two worst*/
if (hijos[0].getFitness() > poblacion[tamPoblacion-1].getFitness()) {
poblacion[tamPoblacion-1] = new Cromosoma (kNeigh, datosTrain.length, hijos[0]);
}
if (hijos[1].getFitness() > poblacion[tamPoblacion-2].getFitness()) {
poblacion[tamPoblacion-2] = new Cromosoma (kNeigh, datosTrain.length, hijos[1]);
}
}
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("SSMA "+ 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
/**
* 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++);
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 = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the probabilities of evolutionary operators*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pCross = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pMut = 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