/***********************************************************************
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/
**********************************************************************/
//
// MCNN.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 7-4-2007.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.MCNN;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.*;
public class MCNN extends Metodo {
public MCNN (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, l, m;
int nClases;
boolean marcas[];
int nSel;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
boolean St[];
boolean Sm[];
boolean Sr[];
double centers[][];
int contadores[];
int represent[];
double minDist, distan;
int pos;
boolean paso1, paso2, entra;
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++;
/*Inicialization of the flagged instaces vector for a posterior elimination*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
marcas[i] = false;
nSel = 0;
St = new boolean[datosTrain.length];
Sr = new boolean[datosTrain.length];
Sm = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
St[i] = true;
}
centers = new double[nClases][datosTrain[0].length];
contadores = new int[nClases];
represent = new int[nClases];
do {
do {
for (i = 0; i < nClases; i++) {
Arrays.fill(centers[i], 0.0);
}
Arrays.fill(contadores, 0);
/*Compute the centroids of the instances selected*/
for (i = 0; i < datosTrain.length; i++) {
if (St[i]) {
for (j = 0; j < datosTrain[0].length; j++) {
centers[clasesTrain[i]][j] += datosTrain[i][j];
}
contadores[clasesTrain[i]]++;
}
}
for (i = 0; i < nClases; i++) {
for (j = 0; j < datosTrain[0].length; j++) {
centers[i][j] /= (double) contadores[i];
}
}
/*Search the nearest instance to each center of its own class*/
for (i = 0; i < nClases; i++) {
minDist = Double.POSITIVE_INFINITY;
pos = -1;
for (j = 0; j < datosTrain.length; j++) {
if (clasesTrain[j] == i) {
distan = KNN.distancia(centers[i], datosTrain[j]);
if (distan < minDist) {
minDist = distan;
pos = j;
}
}
}
represent[i] = pos;
}
Arrays.fill(Sr, false);
Arrays.fill(Sm, false);
/*Classify the instances belonging to St*/
paso1 = false;
for (i = 0; i < datosTrain.length; i++) {
if (St[i]) {
minDist = Double.POSITIVE_INFINITY;
pos = -1;
for (j = 0; j < nClases; j++) {
if (represent[j] != -1) {
distan = KNN.distancia(datosTrain[represent[j]], datosTrain[i]);
} else {
distan = Double.POSITIVE_INFINITY;
}
if (distan < minDist) {
minDist = distan;
pos = j;
}
}
if (clasesTrain[i] == pos) {
Sr[i] = true;
}
else {
Sm[i] = true;
paso1 = true;
}
}
}
for (i = 0; i < datosTrain.length; i++) {
St[i] = Sr[i];
}
}
while (paso1 == true);
/*Include the most representative points to the selected subset*/
entra = false;
for (i = 0; i < nClases; i++) {
if (represent[i] >= 0) {
if (!marcas[represent[i]]) {
marcas[represent[i]] = true;
nSel++;
entra = true;
}
}
}
Arrays.fill(Sr, false);
Arrays.fill(Sm, false);
if (nSel == 0) {
nSel = 1;
marcas[0] = true;
}
/*Classify the instance belonging to the training data set*/
paso2 = false;
for (i = 0; i < datosTrain.length; i++) {
minDist = Double.POSITIVE_INFINITY;
pos = -1;
for (j = 0; j < datosTrain.length; j++) {
if (marcas[j]) {
distan = KNN.distancia(datosTrain[i], datosTrain[j]);
if (distan < minDist) {
minDist = distan;
pos = j;
}
}
}
if (pos >= 0 && clasesTrain[i] == clasesTrain[pos]) {
Sr[i] = true;
}
else {
Sm[i] = true;
paso2 = true;
}
}
for (i = 0; i < datosTrain.length; i++) {
St[i] = Sm[i];
}
} while (paso2 == true && entra == true);
/*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++;
}
}
System.out.println("MCNN "+ 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);
}
}