/***********************************************************************
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/
**********************************************************************/
//
// FCNN.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 26-9-2008.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.FCNN;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
import java.util.Vector;
public class FCNN extends Metodo {
/*Own parameters of the algorithm*/
private int k;
public FCNN (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int S[];
int i, j, l, m;
int nClases;
int pos;
int tamS;
int nearest[][];
Vector <Integer> deltaS = new Vector <Integer> ();
double centroid[];
int nCentroid;
double dist, minDist;
int rep[];
boolean insert;
int votes[];
int max;
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++;
if (nClases < 2) {
System.err.println("Input dataset has only one class");
nClases = 0;
}
nearest = new int[datosTrain.length][k];
for (i=0; i<datosTrain.length; i++) {
Arrays.fill(nearest[i],-1);
}
/*Inicialization of the candidates set*/
S = new int[datosTrain.length];
for (i=0; i<S.length; i++)
S[i] = Integer.MAX_VALUE;
tamS = 0;
/*Inserting an element of each class*/
centroid = new double[datosTrain[0].length];
for (i=0; i<nClases; i++) {
nCentroid = 0;
Arrays.fill(centroid, 0);
for (j=0; j<datosTrain.length; j++) {
if (clasesTrain[j] == i) {
for (l=0; l<datosTrain[j].length; l++) {
centroid[l] += datosTrain[j][l];
}
nCentroid++;
}
}
for (j=0; j<centroid.length; j++) {
centroid[j] /= (double)nCentroid;
}
pos = -1;
minDist = Double.POSITIVE_INFINITY;
for (j=0; j<datosTrain.length; j++) {
if (clasesTrain[j] == i) {
dist = KNN.distancia(centroid, datosTrain[j]);
if (dist < minDist) {
minDist = dist;
pos = j;
}
}
}
if (pos >= 0)
deltaS.add(pos);
}
/*Algorithm body*/
rep = new int[datosTrain.length];
votes = new int[nClases];
while (deltaS.size() > 0) {
for (i=0; i<deltaS.size(); i++) {
S[tamS] = deltaS.elementAt(i);
tamS++;
}
Arrays.sort(S);
Arrays.fill(rep, -1);
for (i=0; i<datosTrain.length; i++) {
if (Arrays.binarySearch(S, i) < 0) {
for (j=0; j<deltaS.size(); j++) {
insert = false;
for (l=0; l<nearest[i].length && !insert; l++) {
if (nearest[i][l] < 0) {
nearest[i][l] = deltaS.elementAt(j);
insert = true;
} else {
if (KNN.distancia(datosTrain[nearest[i][l]], datosTrain[i]) > KNN.distancia(datosTrain[i], datosTrain[deltaS.elementAt(j)])) {
for (m = k - 1; m >= l+1; m--) {
nearest[i][m] = nearest[i][m-1];
}
nearest[i][l] = deltaS.elementAt(j);
insert = true;
}
}
}
}
Arrays.fill(votes, 0);
for (j=0; j<nearest[i].length; j++) {
if (nearest[i][j] >= 0) {
votes[clasesTrain[nearest[i][j]]]++;
}
}
max = votes[0];
pos = 0;
for (j=1; j<votes.length; j++) {
if (votes[j] > max) {
max = votes[j];
pos = j;
}
}
if (clasesTrain[i] != pos) {
for (j=0; j<nearest[i].length; j++) {
if (nearest[i][j] >= 0) {
if (rep[nearest[i][j]] < 0) {
rep[nearest[i][j]] = i;
} else {
if (KNN.distancia(datosTrain[nearest[i][j]], datosTrain[i]) < KNN.distancia(datosTrain[nearest[i][j]], datosTrain[rep[nearest[i][j]]])) {
rep[nearest[i][j]] = i;
}
}
}
}
}
}
}
deltaS.removeAllElements();
for (i=0; i<tamS; i++) {
if (rep[S[i]] >= 0 && !deltaS.contains(rep[S[i]]))
deltaS.add(rep[S[i]]);
}
}
/*Construction of the S set from the previous vector S*/
conjS = new double[tamS][datosTrain[0].length];
conjR = new double[tamS][datosTrain[0].length];
conjN = new int[tamS][datosTrain[0].length];
conjM = new boolean[tamS][datosTrain[0].length];
clasesS = new int[tamS];
for (j=0; j<tamS; j++) {
for (l=0; l<datosTrain[0].length; l++) {
conjS[j][l] = datosTrain[S[j]][l];
conjR[j][l] = realTrain[S[j]][l];
conjN[j][l] = nominalTrain[S[j]][l];
conjM[j][l] = nulosTrain[S[j]][l];
}
clasesS[j] = clasesTrain[S[j]];
}
System.out.println("FCNN "+ 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));
}
}