/***********************************************************************
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/
**********************************************************************/
//
// MSS.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 25-11-2005.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Instance_Selection.MSS;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
public class MSS extends Metodo {
public MSS (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, k, l;
int nClases;
boolean marcas[];
boolean disponible[];
int nSel;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
double distEnemy[];
double distancia, minDistancia;
int pos;
long tiempo = System.currentTimeMillis();
/*Inicialization of the flagged instances vector for a posterior copy*/
marcas = new boolean[datosTrain.length];
disponible = new boolean[datosTrain.length];
distEnemy = new double[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
marcas[i] = false;
disponible[i] = true;
}
nSel = 0;
/*Getting the number of different classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Body of the algorithm. Order the instances by the mininum distance to the nearest enemy and include in the
MSS subset if it is a representative example of the relative neighbour of a instance*/
for (i=0; i<datosTrain.length; i++) {
minDistancia = Double.POSITIVE_INFINITY;
for (j=0; j<datosTrain.length; j++) {
if (i != j && clasesTrain[i] != clasesTrain[j]) {
distancia = KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu);
if (distancia < minDistancia)
minDistancia = distancia;
}
}
distEnemy[i] = minDistancia;
}
for (i=0; i<nClases; i++) {
pos = 0;
while (pos >= 0) {
minDistancia = Double.POSITIVE_INFINITY;
pos = -1;
for (j = 0; j < datosTrain.length; j++) {
if (clasesTrain[j] == i && disponible[j]) {
if (distEnemy[j] < minDistancia) {
minDistancia = distEnemy[j];
pos = j; //pos is the instance with minimun distance of the nearest enemy
}
}
}
if (pos >= 0) {
marcas[pos] = true;
disponible[pos] = false;
for (k = 0; k < datosTrain.length; k++) {
if (clasesTrain[k] == i) {
if (disponible[k] &&
KNN.distancia(datosTrain[pos], realTrain[pos], nominalTrain[pos], nulosTrain[pos], datosTrain[k], realTrain[k], nominalTrain[k], nulosTrain[k], distanceEu) < distEnemy[pos]) {
disponible[k] = false;
}
}
}
}
}
}
/*Building of the S set from the flags*/
nSel = 0;
for (i=0; i<datosTrain.length; i++)
if (marcas[i]) nSel++;
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 (marcas[i]) { //the instance will 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("MSS "+ 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, 1);
}
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, 1);
}
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 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 type of distance function*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
distanceEu = tokens.nextToken().substring(1).equalsIgnoreCase("Euclidean")?true:false;
}
}