/***********************************************************************
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/
**********************************************************************/
//
// RMHC.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 17-7-2004.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.RMHC;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
public class RMHC extends Metodo{
/*Own parameters of the algorithm*/
private long semilla;
private int k;
private double porcentaje;
private int n;
public RMHC (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, l, m, o;
int nClases;
int claseObt;
boolean marcas[];
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int eleS[], eleT[];
int bestAc, aciertos;
int temp[];
int pos, tmp, pos2;
long tiempo = System.currentTimeMillis();
/*Getting the numebr of different classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Inicialization if the flagged instances vector of the S set*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
marcas[i] = false;
/*Allocate memory for random selection*/
m = (int)((porcentaje * datosTrain.length) / 100.0);
eleS = new int[m];
eleT = new int[datosTrain.length - m];
temp = new int[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
temp[i] = i;
/*Initial random distribution of instances in each set*/
Randomize.setSeed (semilla);
for (i=0; i<eleS.length; i++) {
pos = Randomize.Randint (i, datosTrain.length-1);
tmp = temp[i];
temp[i] = temp[pos];
temp[pos] = tmp;
eleS[i] = temp[i];
}
for (i=0; i<eleT.length; i++) {
pos = Randomize.Randint (m+i, datosTrain.length-1);
tmp = temp[m+i];
temp[m+i] = temp[pos];
temp[pos] = tmp;
eleT[i] = temp[m+i];
}
for (i=0; i<eleS.length; i++)
marcas[eleS[i]] = true;
/*Building of S set from the flags*/
conjS = new double[m][datosTrain[0].length];
conjR = new double[m][datosTrain[0].length];
conjN = new int[m][datosTrain[0].length];
conjM = new boolean[m][datosTrain[0].length];
clasesS = new int[m];
for (o=0, l=0; o<datosTrain.length; o++) {
if (marcas[o]) { //the instance will be evaluated
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
conjR[l][j] = realTrain[o][j];
conjN[l][j] = nominalTrain[o][j];
conjM[l][j] = nulosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
/*Evaluation of s set*/
bestAc = 0;
for (i=0; i<datosTrain.length; i++) {
claseObt = KNN.evaluacionKNN2(k, conjS, conjR, conjN, conjM, clasesS, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu);
if (claseObt == clasesTrain[i]) //correct clasification
bestAc++;
}
/*Body of the RMHC algorithm. Change only an element of the S set and see if it is
improves classification than previous. Is it is true, maintain the new set; if it
is not better, undo the change*/
for (i=0; i<n && eleS.length > 0; i++) {
pos = Randomize.Randint (0,eleS.length-1);
pos2 = Randomize.Randint (0, eleT.length-1);
/*Interchange of instances*/
tmp = eleS[pos];
eleS[pos] = eleT[pos2];
eleT[pos2] = tmp;
marcas[eleS[pos]] = true;
marcas[eleT[pos2]] = false;
/*Building of S set from the flags*/
for (o=0, l=0; o<datosTrain.length; o++) {
if (marcas[o]) { //the instance will evaluate
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
conjR[l][j] = realTrain[o][j];
conjN[l][j] = nominalTrain[o][j];
conjM[l][j] = nulosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
/*Evaluation of the S set*/
aciertos = 0;
for (j=0; j<datosTrain.length; j++) {
claseObt = KNN.evaluacionKNN2(k, conjS, conjR, conjN, conjM, clasesS, datosTrain[j], realTrain[j], nominalTrain[j], nulosTrain[j], nClases, distanceEu);
if (claseObt == clasesTrain[j]) //correct clasification
aciertos++;
}
if (aciertos > bestAc) { //maintain S
bestAc = aciertos;
} else { //undo changes
tmp = eleS[pos];
eleS[pos] = eleT[pos2];
eleT[pos2] = tmp;
marcas[eleS[pos]] = true;
marcas[eleT[pos2]] = false;
}
}
/*Building S s from the flags*/
for (o=0, l=0; o<datosTrain.length; o++) {
if (marcas[o]) { //the instance will evaluate
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[o][j];
conjR[l][j] = realTrain[o][j];
conjN[l][j] = nominalTrain[o][j];
conjM[l][j] = nulosTrain[o][j];
}
clasesS[l] = clasesTrain[o];
l++;
}
}
System.out.println("RMHC "+ 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 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 the 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 seed*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
semilla = Long.parseLong(tokens.nextToken().substring(1));
/*Getting the number of neighbors*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
k = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the percentage*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
porcentaje = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the number of iterations*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
n = 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;
}
}