/***********************************************************************
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/
**********************************************************************/
//
// IKNN.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 2-6-2009.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.IKNN;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class IKNN extends Metodo {
/*Own parameters of the algorithm*/
double gammaRate;
double xiMultiplicative;
double xiExponential;
public IKNN (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[];
int attractive[];
double minDist, dist;
boolean stop = false;
Referencia order[];
int nElem;
int Xi_t, t;
int numberClass[];
int minClass;
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++;
numberClass = new int[nClases];
Arrays.fill(numberClass, 0);
for (i=0; i<clasesTrain.length; i++) {
numberClass[clasesTrain[i]]++;
}
minClass = numberClass[0];
for (i=1; i<numberClass.length; i++) {
if (numberClass[i] < minClass) {
minClass = numberClass[i];
}
}
/*Inicialization of the flagged instances vector for a posterior copy*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
marcas[i] = true;
nSel = datosTrain.length;
attractive = new int[datosTrain.length];
/*Body of the algorithm. The attractive capacity is computed for all instances in S and those with capacities greater than gamma and
are among the Xi(t) portion of the highest capacities are eliminated in each iteration*/
t=1;
while (!stop) {
Arrays.fill(attractive, 0);
/*STEP 2*/
for (i=0; i<datosTrain.length; i++) {
if (marcas[i]) {
minDist = Double.POSITIVE_INFINITY;
for (j=0; j<datosTrain.length; j++) {
if (marcas[j] && 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;
}
}
}
for (j=0; j<datosTrain.length; j++) {
if (marcas[j] && i!=j) {
dist = KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], distanceEu);
if (dist < minDist) {
attractive[i]++;
}
}
}
}
}
/*STEP 3*/
stop = true;
nElem=0;
for (i=0; i<datosTrain.length; i++) {
if (marcas[i]) {
if (attractive[i] >= ((int)((double)minClass)*gammaRate)) {
stop = false;
nElem++;
}
}
}
if (!stop) {
/*STEP 4*/
order = new Referencia[nElem];
j = 0;
for (i=0; i<datosTrain.length; i++) {
if (marcas[i]) {
if (attractive[i] >= ((int)((double)minClass)*gammaRate)) {
order[j] = new Referencia(i,attractive[i]);
j++;
}
}
}
Arrays.sort(order);
Xi_t = (int)((xiMultiplicative * Math.pow(((double)(t+1)),xiExponential))*datosTrain.length);
for (i=0; i<Xi_t && i<order.length; i++) {
marcas[order[i].entero] = false;
nSel--;
}
t++;
}
}
/*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 will 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("IKNN "+ 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 Gamma*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
gammaRate = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting Gamma*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
xiMultiplicative = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting Gamma*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
xiExponential = 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;
}
}