/***********************************************************************
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/
**********************************************************************/
//
// MNV.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 22-2-2008.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.MNV;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class MNV extends Metodo {
/*Own parameters of the algorithm*/
private int k;
public MNV (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
double conjS2[][];
double conjR2[][];
int conjN2[][];
boolean conjM2[][];
int clasesS2[];
int S[];
int i, j, l, m;
int nClases;
int tamS;
int claseObt;
int cont;
int busq;
boolean continuar;
double dist, minDist;
int instance;
ReferenciaMNV orderSet[];
boolean marcas[];
int nSel, aciertosIni=0, aciertos;
long tiempo = System.currentTimeMillis();
/*Inicialization of the candidates set*/
S = new int[datosTrain.length];
for (i=0; i<S.length; i++)
S[i] = Integer.MAX_VALUE;
/*Getting the number of different classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
tamS = 0;
if (nClases < 2) {
System.err.println("Input dataset is empty");
nClases = 0;
}
orderSet = new ReferenciaMNV[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
minDist = Double.MAX_VALUE;
instance = 0;
for (j=0; j<datosTrain.length; j++) {
if (clasesTrain[i] != clasesTrain[j]) {
dist = KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu);
if (dist < minDist) {
minDist = dist;
instance = j;
}
}
}
cont = 0;
for (j=0; j<datosTrain.length; j++) {
if (clasesTrain[j] != clasesTrain[instance] && KNN.distancia(datosTrain[instance], realTrain[instance], nominalTrain[instance], nulosTrain[instance], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu) < minDist) {
cont++;
}
}
orderSet[i] = new ReferenciaMNV(i, cont, minDist);
}
Arrays.sort(orderSet);
S[0] = orderSet[0].entero;
tamS++;
/*Algorithm body. We resort randomly the instances of T and compare with the rest of S.
If an instance doesn�t classified correctly, it is inserted in S*/
do {
continuar = false;
for (i=0; i<datosTrain.length; 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]];
}
Arrays.sort(S);
busq = Arrays.binarySearch(S, orderSet[i].entero);
if (busq < 0) {
/*Do KNN to the instance*/
claseObt = KNN.evaluacionKNN(k, conjS, conjR, conjN, conjM, clasesS, datosTrain[orderSet[i].entero], realTrain[orderSet[i].entero], nominalTrain[orderSet[i].entero], nulosTrain[orderSet[i].entero], nClases, distanceEu);
if (claseObt != clasesTrain[orderSet[i].entero]) { //fail in the class, it is included in S
continuar = true;
S[tamS] = orderSet[i].entero;
tamS++;
}
}
}
} while (continuar == true);
/*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]];
}
/*RNN Process*/
/*Inicializaci�n del vector de instancias marcadas para su elminiaci�n posterior*/
marcas = new boolean[conjS.length];
for (i=0; i<conjS.length; i++)
marcas[i] = true;
nSel = conjS.length;
/*Calculate the number of correct clasifications considering the same train set using leave-one out.*/
for (i=0; i<conjS.length; i++) {
claseObt = KNN.evaluacionKNN2 (k, conjS, conjR, conjN, conjM, clasesS, conjS[i], conjR[i], conjN[i], conjM[i], nClases, distanceEu);
if (claseObt == clasesS[i])
aciertosIni++;
}
/*Body of the RNN algorithm. Eliminating instances and calculating improves. If
a remove of an instance not improves classification, the instance is not removed.*/
for (i=0; i<conjS.length; i++) {
marcas[i] = false;
nSel--;
/*Building of the S set from the flags*/
conjS2 = new double[nSel][conjS[0].length];
conjR2 = new double[nSel][conjS[0].length];
conjN2 = new int[nSel][conjS[0].length];
conjM2 = new boolean[nSel][conjS[0].length];
clasesS2 = new int[nSel];
for (m=0, l=0; m<conjS.length; m++) {
if (marcas[m]) { //the instance must be copied to the solution
for (j=0; j<conjS[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++;
}
}
/*Get the accuracy considering the S set*/
aciertos = 0;
for (j=0; j<conjS.length; j++) {
claseObt = KNN.evaluacionKNN2 (k, conjS2, conjR2, conjN2, conjM2, clasesS2, conjS[j], conjR[j], conjN[j], conjM[j], nClases, distanceEu);
if (claseObt == clasesS[j])
aciertos++;
}
/*Is the instance removed?*/
if (aciertos < aciertosIni) {
marcas[i] = true;
nSel++;
}
}
/*Building the final S set from the existents flags*/
conjS2 = new double[nSel][conjS[0].length];
conjR2 = new double[nSel][conjS[0].length];
conjN2 = new int[nSel][conjS[0].length];
conjM2 = new boolean[nSel][conjS[0].length];
clasesS2 = new int[nSel];
for (m=0, l=0; m<conjS.length; m++) {
if (marcas[m]) {
for (j=0; j<conjS[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++;
}
}
System.out.println("MNV "+ 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 type of distance function*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
distanceEu = tokens.nextToken().substring(1).equalsIgnoreCase("Euclidean")?true:false;
}
}