/***********************************************************************
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/
**********************************************************************/
//
// Multiedit.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 13-7-2004.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Instance_Selection.Multiedit;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
public class Multiedit extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private int k;
private int B;
public Multiedit (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, l, m;
int nClases;
int claseObt;
boolean smarcas[][];
int snSel[];
double sconjS[][][];
double sconjR[][][];
int sconjN[][][];
boolean sconjM[][][];
int sclasesS[][];
double sconjS2[][][];
double sconjR2[][][];
int sconjN2[][][];
boolean sconjM2[][][];
int sclasesS2[][];
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel;
int baraje[][];
int pos, posi=0, posj=0, tmp;
boolean parar;
int fin=0;
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 (k > clasesTrain.length)
k = clasesTrain.length;
/*Shuffle the train set and divide it into subblocks*/
Randomize.setSeed (semilla);
sconjS = new double[B][][];
sconjR = new double[B][][];
sconjN = new int[B][][];
sconjM = new boolean[B][][];
sclasesS = new int[B][];
sconjS2 = new double[B][][];
sconjR2 = new double[B][][];
sconjN2 = new int[B][][];
sconjM2 = new boolean[B][][];
sclasesS2 = new int[B][];
baraje = new int[B][];
snSel = new int[B];
smarcas = new boolean[B][];
for (i=0; i<B; i++)
if (i < datosTrain.length % B)
baraje[i] = new int[datosTrain.length/B + 1];
else baraje[i] = new int[datosTrain.length/B];
for (i=0, l=0; i<B; i++)
for (j=0; j<baraje[i].length; j++, l++)
baraje[i][j] = l;
for (i=0; i<B; i++) {
for (l=0; l<baraje[i].length; l++) {
pos = Randomize.Randint(i*baraje[i].length + l, datosTrain.length - 1);
parar = false;
for (j = 0; !parar; j++) {
if (pos < baraje[j].length) {
posi = j;
posj = pos;
parar = true;
} else {
pos -= baraje[j].length;
}
}
tmp = baraje[i][l];
baraje[i][l] = baraje[posi][posj];
baraje[posi][posj] = tmp;
}
}
/*Building of all S subsets*/
for (i=0; i<B; i++) {
sconjS[i] = new double[baraje[i].length][datosTrain[0].length];
sconjR[i] = new double[baraje[i].length][datosTrain[0].length];
sconjN[i] = new int[baraje[i].length][datosTrain[0].length];
sconjM[i] = new boolean[baraje[i].length][datosTrain[0].length];
sclasesS[i] = new int[baraje[i].length];
for (m=0; m<baraje[i].length; m++) {
for (j=0; j<datosTrain[0].length; j++) {
sconjS[i][m][j] = datosTrain[baraje[i][m]][j];
sconjR[i][m][j] = realTrain[baraje[i][m]][j];
sconjN[i][m][j] = nominalTrain[baraje[i][m]][j];
sconjM[i][m][j] = nulosTrain[baraje[i][m]][j];
}
sclasesS[i][m] = clasesTrain[baraje[i][m]];
}
}
while (fin < B) {
fin = 0;
/*Inicialization of the flagged instances vector for a posterior copy*/
for (i=0; i<B; i++) {
smarcas[i] = new boolean[sconjS[i].length];
for (j=0; j<sconjS[i].length; j++)
smarcas[i][j] = false;
snSel[i] = 0;
}
/*Body of the algorithm. For each instance of the i-th block, do a KNN with the i-th+1(mod B) block.
If it is well classified, the instance is flagged for a later add. This process is repeated until
there is no changes.*/
for (i=0; i<B; i++) {
/*Apply KNN to the instances of Bi*/
for (j=0; j<sconjS[i].length; j++) {
/*Apply KNN to the instance*/
claseObt = KNN.evaluacionKNN2(k, sconjS[(i+1)%B], sconjR[(i+1)%B], sconjN[(i+1)%B], sconjM[(i+1)%B], sclasesS[(i+1)%B], sconjS[i][j], sconjR[i][j], sconjN[i][j], sconjM[i][j], nClases, distanceEu);
if (claseObt == sclasesS[i][j]) { //it is well classified, add to S
smarcas[i][j] = true;
snSel[i]++;
}
}
if (snSel[i] == sconjS[i].length)
fin++;
else {
sconjS2[i] = new double[snSel[i]][datosTrain[0].length];
sconjR2[i] = new double[snSel[i]][datosTrain[0].length];
sconjN2[i] = new int[snSel[i]][datosTrain[0].length];
sconjM2[i] = new boolean[snSel[i]][datosTrain[0].length];
sclasesS2[i] = new int[snSel[i]];
for (m=0, l=0; m<sconjS[i].length; m++) {
if (smarcas[i][m]) {
for (j=0; j<datosTrain[0].length; j++) {
sconjS2[i][l][j] = sconjS[i][m][j];
sconjR2[i][l][j] = sconjR[i][m][j];
sconjN2[i][l][j] = sconjN[i][m][j];
sconjM2[i][l][j] = sconjM[i][m][j];
}
sclasesS2[i][l] = sclasesS[i][m];
l++;
}
}
sconjS[i] = sconjS2[i];
sconjR[i] = sconjR2[i];
sconjN[i] = sconjN2[i];
sconjM[i] = sconjM2[i];
sclasesS[i] = sclasesS2[i];
}
}
}
/*Building of the final S set*/
nSel = 0;
for (i=0; i<B; i++)
nSel += snSel[i];
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 (i=0, l=0; i<B; i++) {
for (j=0; j<sconjS[i].length; j++, l++) {
for (m=0; m<sconjS[i][j].length; m++) {
conjS[l][m] = sconjS[i][j][m];
conjR[l][m] = sconjR[i][j][m];
conjN[l][m] = sconjN[i][j][m];
conjM[l][m] = sconjM[i][j][m];
}
clasesS[l] = sclasesS[i][j];
}
}
System.out.println("Multiedit "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
//Working on training
for ( i=0; i<datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i],conjS, nClases, clasesS, this.k);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
//Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
//Check time
for (i=0; i<realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0]= KNN.evaluate(datosTest[i],conjS, nClases, clasesS, this.k);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, 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++);
ficheroValidation = 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 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 number of subblocks*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
B = 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;
}
}