/***********************************************************************
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/
**********************************************************************/
//
// HMNEI.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 7-7-2008.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.HMNEI;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
public class HMNEI extends Metodo {
/*Own parameters of the algorithm*/
private double epsilon;
public HMNEI (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, k, l, m;
int nClases;
int claseObt;
boolean marcas[];
int nSel = 0;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
double conjS2[][];
double conjR2[][];
int conjN2[][];
boolean conjM2[][];
int clasesS2[];
double dist, minDist;
double acierto, aciertoAct = 0.0;
int hit[], miss[];
int pos, cont;
double w[];
int cc[];
int seleccionadosAnt;
long tiempo = System.currentTimeMillis();
/*Getting the number of differents classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Building of the S set from the flags*/
conjS2 = new double[datosTrain.length][datosTrain[0].length];
conjR2 = new double[datosTrain.length][datosTrain[0].length];
conjN2 = new int[datosTrain.length][datosTrain[0].length];
conjM2 = new boolean[datosTrain.length][datosTrain[0].length];
clasesS2 = new int[datosTrain.length];
for (m=0, l=0; m<datosTrain.length; m++) {
for (j=0; j<datosTrain[0].length; j++) {
conjS2[l][j] = datosTrain[m][j];
conjR2[l][j] = realTrain[m][j];
conjN2[l][j] = nominalTrain[m][j];
conjM2[l][j] = nulosTrain[m][j];
}
clasesS2[l] = clasesTrain[m];
l++;
}
nSel = datosTrain.length;
do {
acierto = aciertoAct;
seleccionadosAnt = 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<nSel; m++) {
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = conjS2[m][j];
conjR[l][j] = conjR2[m][j];
conjN[l][j] = conjN2[m][j];
conjM[l][j] = conjM2[m][j];
}
clasesS[l] = clasesS2[m];
l++;
}
/*Inicialization of the flagged instances vector from the S*/
marcas = new boolean[nSel];
for (i=0; i<nSel; i++) {
marcas[i] = true;
}
hit = new int[nSel];
miss = new int[nSel];
for (i=0; i<conjS.length; i++) {
for (j=0; j<nClases; j++) {
minDist = Double.POSITIVE_INFINITY;
pos = -1;
for (k=0; k<conjS.length; k++) {
if (i!=k && clasesS[k] == j) {
dist = KNN.distancia(conjS[i], conjR[i], conjN[i], conjM[i], conjS[k], conjR[k], conjN[k], conjM[k], distanceEu);
if (dist < minDist) {
minDist = dist;
pos = k;
}
}
}
if (pos >= 0) {
if (clasesS[i] == j) {
hit[pos]++;
} else {
miss[pos]++;
}
}
}
}
w = new double[nClases];
cc = new int[nClases];
for (i=0; i<w.length; i++) {
cont = 0;
for (j=0; j<clasesS.length; j++) {
if (clasesS[j] == i) {
cont++;
}
}
cc[i] = cont;
w[i] = (double)cont / (double)nSel;
}
/*RULE R1*/
for (i=0; i<hit.length; i++) {
if ((w[clasesS[i]] * (double)miss[i] + epsilon) > ((1-w[clasesS[i]]) * (double)hit[i])) {
marcas[i] = false;
nSel--;
}
}
/*RULE R2*/
for (i=0; i<nClases; i++) {
cont = 0;
for (j=0; j<hit.length && cont < 4; j++) {
if (clasesS[j] == i && marcas[j]) {
cont++;
}
}
if (cont < 4) {
for (j=0; j<hit.length; j++) {
if (clasesS[j] == i && !marcas[j] && (hit[j]+miss[j]) > 0) {
marcas[j] = true;
nSel++;
}
}
}
}
/*RULE R3*/
if (nClases > 3) {
for (i=0; i<hit.length; i++) {
if (!marcas[i] && (miss[i]+hit[i] > 0) && miss[i] < (nClases/2)) {
marcas[i] = true;
nSel++;
}
}
}
/*RULE R4*/
for (i=0; i<hit.length; i++) {
if (!marcas[i] && hit[i] >= (cc[clasesS[i]] / 4)) {
marcas[i] = true;
nSel++;
}
}
/*Building of the S set from the flags*/
conjS2 = new double[nSel][datosTrain[0].length];
conjR2 = new double[nSel][datosTrain[0].length];
conjN2 = new int[nSel][datosTrain[0].length];
conjM2 = new boolean[nSel][datosTrain[0].length];
clasesS2 = new int[nSel];
for (m=0, l=0; m<conjS.length; m++) {
if (marcas[m]) { //the instance will be evaluated
for (j=0; j<datosTrain[0].length; j++) {
conjS2[l][j] = conjS[m][j];
conjR2[l][j] = conjR[m][j];
conjN2[l][j] = conjN[m][j];
conjM2[l][j] = conjM[m][j];
}
clasesS2[l] = clasesS[m];
l++;
}
}
aciertoAct = 0;
for (i=0; i<datosTrain.length; i++) {
claseObt = KNN.evaluacionKNN2(1, conjS2, conjR2, conjN2, conjM2, clasesS2, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu);
if (claseObt == clasesTrain[i]) {
aciertoAct++;
}
}
} while (aciertoAct >= acierto && nSel < seleccionadosAnt);
System.out.println("HMNEI "+ 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);
}
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++);
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 epsilon value*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
epsilon = Double.parseDouble(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;
}
}