/***********************************************************************
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/
**********************************************************************/
//
// ENNTh.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 11-7-2004.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.ENNTh;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class ENNTh extends Metodo {
/*Own parameters of the algorithm*/
private int k;
private double mu;
public ENNTh (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, l;
int nClases;
int claseObt;
boolean marcas[];
int nSel = 0;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int vecinos[];
double prob[];
double sumProb;
double maxProb;
int pos;
long tiempo = System.currentTimeMillis();
/*Inicialization of the flagged instances vector for a posterior copy*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
marcas[i] = false;
/*Getting the number of differents classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
vecinos = new int[k];
prob = new double[nClases];
/*Body of the algorithm. For each instance in T, search the correspond class conform his mayority
from the nearest neighborhood. Is it is positive, the instance is selected.*/
for (i=0; i<datosTrain.length; i++) {
KNN.evaluacionKNN2(k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, vecinos);
Arrays.fill(prob, 0.0);
for (j=0; j<vecinos.length; j++){
if (vecinos[j]>=0) {
prob[clasesTrain[vecinos[j]]] += 1.0 / (1.0 + KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[vecinos[j]], realTrain[vecinos[j]], nominalTrain[vecinos[j]], nulosTrain[vecinos[j]], distanceEu));
}
}
sumProb = 0.0;
for (j=0; j<prob.length; j++) {
sumProb += prob[j];
}
for (j=0; j<prob.length; j++) {
prob[j] /= sumProb;
}
maxProb = prob[0];
pos = 0;
for (j=1; j<prob.length; j++) {
if (prob[j] > maxProb) {
maxProb = prob[j];
pos = j;
}
}
claseObt = pos;
if (claseObt == clasesTrain[i] && maxProb > mu) { //agree with your majority, it is included in the solution set
marcas[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 (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("ENNTh "+ 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 the number of neighbors*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
k = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the noise threshold*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
mu = 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;
}
}