/***********************************************************************
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/
**********************************************************************/
//
// IGA.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 23-3-2006.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Instance_Selection.IGA;
import keel.Algorithms.Preprocess.Basic.*;
import java.util.StringTokenizer;
import java.util.Vector;
import java.util.Arrays;
import org.core.*;
public class IGA extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private double pMutacion1to0;
private double pMutacion0to1;
private int tamPoblacion;
private int nEval;
private double alpha;
private int kNeigh;
public IGA (String ficheroScript) {
super (ficheroScript);
}
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;
int pos1, pos2;
Cromosoma newPob[];
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];
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, alpha, kNeigh, nClases, distanceEu);
while (ev < nEval) {
newPob = new Cromosoma[tamPoblacion];
for (i=0; i<tamPoblacion-1; i+=2) {
pos1 = Randomize.Randint(0,tamPoblacion-1);
do {
pos2 = Randomize.Randint(0, tamPoblacion-1);
} while (pos1 == pos2);
ev += cruceOrtogonal (poblacion, newPob, pos1, pos2, i, ev, nEval, nClases);
}
/*Mutation of the cromosomes*/
for (i=0; i<tamPoblacion; i++)
newPob[i].mutacion(pMutacion1to0, pMutacion0to1);
/*Evaluation of the poblation*/
for (i=0; i<tamPoblacion; i++) {
poblacion[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alpha, kNeigh, nClases, distanceEu);
ev++;
}
poblacion = newPob;
}
Arrays.sort(poblacion);
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("IGA "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
// 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);
}
/*Function that implements the uniform crossover between two selected cromosomes*/
public int cruceOrtogonal (Cromosoma poblacion[], Cromosoma newPob[], int sel1, int sel2, int pos, int nEv, int nEval, int nClases) {
int i, j;
int gamma = 0;
int omega;
int levels = 2;
int ortogonal[][];
double fitness[];
boolean cuerpo[];
Vector <Integer> posiciones;
Cromosoma cTemp;
int ev = 0;
double Sjk[][];
double diff = Double.POSITIVE_INFINITY;
int posDiff = -1;
Cromosoma grupo[] = new Cromosoma[4];
int resto = nEval - nEv;
int omegaBueno;
grupo[0] = new Cromosoma(datosTrain.length,poblacion[sel1]);
grupo[1] = new Cromosoma(datosTrain.length,poblacion[sel2]);
cuerpo = new boolean[poblacion[sel1].getSize()];
posiciones = new Vector <Integer>();
/*Contabilize the number of bits different between parents (Hamming Distance)*/
for (i=0; i<poblacion[sel1].getSize(); i++) {
if (poblacion[sel1].getGen(i) != poblacion[sel2].getGen(i)) {
gamma++;
posiciones.add(new Integer(i));
} else {
cuerpo[i] = poblacion[sel1].getGen(i);
}
}
omega = (int)Math.pow(2,Math.ceil((Math.log(gamma+1)/Math.log(2.0))));
fitness = new double[omega];
ortogonal = ortogonalArray (omega,gamma,levels);
if (resto < omega)
omegaBueno = resto;
else
omegaBueno = omega;
for (i=0; i<omegaBueno; i++) {
for (j=0; j<posiciones.size(); j++) {
if (ortogonal[i][j] == 1)
cuerpo[((Integer)posiciones.elementAt(j)).intValue()] = poblacion[sel1].getGen(((Integer)posiciones.elementAt(j)).intValue());
else
cuerpo[((Integer)posiciones.elementAt(j)).intValue()] = poblacion[sel2].getGen(((Integer)posiciones.elementAt(j)).intValue());
}
cTemp = new Cromosoma (cuerpo);
cTemp.evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alpha, kNeigh, nClases, distanceEu);
ev++;
fitness[i] = cTemp.getCalidad();
}
Sjk = new double[2][posiciones.size()];
for (i=0; i<posiciones.size(); i++) {
for (j=0; j<omegaBueno; j++) {
if (ortogonal[j][i] == 1)
Sjk[0][i] += fitness[j];
else
Sjk[1][i] += fitness[j];
}
}
for (i=0; i<posiciones.size(); i++) {
if (Sjk[0][i] > Sjk[1][i])
cuerpo[((Integer)posiciones.elementAt(i)).intValue()] = poblacion[sel1].getGen(((Integer)posiciones.elementAt(i)).intValue());
else
cuerpo[((Integer)posiciones.elementAt(i)).intValue()] = poblacion[sel2].getGen(((Integer)posiciones.elementAt(i)).intValue());
if (Math.abs(Sjk[0][i]-Sjk[1][i]) < diff) {
diff = Math.abs(Sjk[0][i]-Sjk[1][i]);
posDiff = i;
}
}
grupo[2] = new Cromosoma (cuerpo);
grupo[2].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alpha, kNeigh, nClases, distanceEu);
if (posDiff >= 0) {
cuerpo[((Integer)posiciones.elementAt(posDiff)).intValue()] = !cuerpo[((Integer)posiciones.elementAt(posDiff)).intValue()];
grupo[3] = new Cromosoma (cuerpo);
grupo[3].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alpha, kNeigh, nClases, distanceEu);
} else {
grupo[3] = new Cromosoma (cuerpo);
grupo[3].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alpha, kNeigh, nClases, distanceEu);
}
ev+=2;
Arrays.sort(grupo);
newPob[pos] = new Cromosoma (datosTrain.length,grupo[0]);
newPob[pos+1] = new Cromosoma (datosTrain.length,grupo[1]);
return ev;
}
/*This function calculates an Orthogonal Array using the general algorithm*/
int[][] ortogonalArray (int QJ, int N, int Q) {
int J;
int matrix[][];
int i, j, k, s, t;
J = (int)(Math.log(QJ)/Math.log(Q));
matrix = new int[QJ][QJ];
/*Construction of basic columns*/
for (k=1; k<=J; k++) {
j = ((int)(Math.pow(Q,k-1))-1)/(Q-1) + 1;
for (i=1; i<= QJ; i++) {
matrix[i-1][j-1] = ((int)(Math.floor(((double)i-1)/(Math.pow(Q,J-k))))) % Q;
}
}
/*Construction of non-basic columns*/
for (k=2; k<=J; k++) {
j = ((int)(Math.pow(Q,k-1))-1)/(Q-1) + 1;
for (s=1; s<=(j-1); s++) {
for (t=1; t<=(Q-1); t++) {
for (i=0; i<QJ; i++) {
matrix[i][j+(s-1)*(Q-1)+t-1] = (matrix[i][s-1]*t+matrix[i][j-1]) % Q;
}
}
}
}
for (i=0; i<QJ; i++)
for (j=0; j<N; j++)
matrix[i][j]++;
return matrix;
}
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 the 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);
/*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 the 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));
/*Getting the size of the poblation 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));
/*Obtain the weight factor values*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
alpha = 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;
}
}