/***********************************************************************
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/
**********************************************************************/
//
// Explore.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 1-8-2004.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Instance_Selection.Explore;
import keel.Algorithms.Preprocess.Basic.*;
import java.util.StringTokenizer;
import java.util.Arrays;
import org.core.*;
public class Explore extends Metodo{
/*Own parameters of the algorithm*/
private long semilla;
private int k;
public Explore (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, l, m;
int nClases;
int tClase[];
int C = 0;
boolean marcas[];
int nSel;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int baraje[];
int pos, tmp;
double coste_mejor, coste_actual;
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++;
/*Get the different classes that exist in the set*/
tClase = new int[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
tClase[i] = Integer.MAX_VALUE;
for (i=0; i<datosTrain.length; i++) {
if (Arrays.binarySearch(tClase,clasesTrain[i]) < 0) { //not found
tClase[C] = clasesTrain[i];
C++;
Arrays.sort(tClase);
}
}
/*Shuffle the train set to random the presentation of instances*/
Randomize.setSeed (semilla);
baraje = new int[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
baraje[i] = i;
for (i=0; i<datosTrain.length; i++) {
pos = Randomize.Randint(i,datosTrain.length-1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
/*Inicialization of the flagged instaces vector to a posterior copy*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
marcas[i] = false;
if (datosTrain.length > 0) {
marcas[baraje[0]] = true; //insert the first one always
nSel = 1;
} else {
System.err.println("Input dataset is empty");
nSel = 0;
}
/*Building of the S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
coste_mejor = EncodingLength.evaluaEL (datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, conjS, conjR, conjN, conjM, clasesS, C, k, nClases, distanceEu);
/*Body of the ELH algorithm. For each instane in T, it is included in T if improves the cost function Encoding Length*/
for (i=1; i<datosTrain.length; i++) {
marcas[baraje[i]] = true;
nSel++;
/*Building of the S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
coste_actual = EncodingLength.evaluaEL (datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, conjS, conjR, conjN, conjM, clasesS, C, k, nClases, distanceEu);
if (coste_actual < coste_mejor)
coste_mejor = coste_actual;
else {
marcas[baraje[i]] = false;
nSel--;
}
}
/*Building of the S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
/*Do another shuffle for now eliminate instances*/
for (i=0; i<datosTrain.length; i++)
baraje[i] = i;
for (i=0; i<datosTrain.length; i++) {
pos = Randomize.Randint(i,datosTrain.length-1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
/*2� part of ELGrow. Now, eliminate instances to see if the cost improves*/
for (i=0; i<datosTrain.length; i++) {
if (marcas[baraje[i]]) { //It is in S
marcas[baraje[i]] = false;
nSel--;
/*Building of the S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
coste_actual = EncodingLength.evaluaEL (datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, conjS, conjR, conjN, conjM, clasesS, C, k, nClases, distanceEu);
if (coste_actual < coste_mejor)
coste_mejor = coste_actual;
else {
marcas[baraje[i]] = true;
nSel++;
}
}
}
/*3� part of Explore. Do 1000 randoms mutations and maintain the best results*/
for (i=0; i<1000; i++) {
pos = Randomize.Randint (0, datosTrain.length-1);
if (marcas[pos]) {
marcas[pos] = false;
nSel--;
} else {
marcas[pos] = true;
nSel++;
}
/*Building S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
coste_actual = EncodingLength.evaluaEL (datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, conjS, conjR, conjN, conjM, clasesS, C, k, nClases, distanceEu);
if (coste_actual < coste_mejor)
coste_mejor = coste_actual;
else {
if (marcas[pos]) {
marcas[pos] = false;
nSel--;
} else {
marcas[pos] = true;
nSel++;
}
}
}
/*Building the S set from the flags*/
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 (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
conjR[l][j] = realTrain[m][j];
conjN[l][j] = nominalTrain[m][j];
conjM[l][j] = nulosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
System.out.println("Explore "+ 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.k);
}
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.k);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}
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 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 number of neighbors*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
k = 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;
}
}