/***********************************************************************
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/
**********************************************************************/
//
// IB3.java
//
// Salvador Garc�a L�pez
//
// Created by Salvador Garc�a L�pez 14-7-2004.
// Copyright (c) 2004 __MyCompanyName__. All rights reserved.
//
package keel.Algorithms.Preprocess.Instance_Selection.IB3;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
public class IB3 extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private double nAcep;
private double nDrop;
/*Another data*/
int nClases;
public IB3 (String ficheroScript) {
super (ficheroScript);
}
public void ejecutar () {
int i, j, o;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int baraje[];
int pos, tmp;
int classRecord[][]; //store the number of rigths/fails
int classFrecuency[][]; //store the class frecuency for each instance
int CD[];
int nCD = 0;
double sim[];
boolean aceptable;
double bestsim;
int ymax, seleccionada;
long tiempo = System.currentTimeMillis();
/*Do a shuffle of training instances to a random evaluate*/
Randomize.setSeed (semilla);
baraje = new int[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
baraje[i] = i;
}
for (i=0; i<datosTrain.length; i++) {
pos = Randomize.Randint (i, datosTrain.length-1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
CD = new int[datosTrain.length];
for (i=0; i<datosTrain.length; i++)
CD[i] = -1;
sim = new double[datosTrain.length];
/*Getting the number of differents classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Inicialization of the clasification register*/
classRecord = new int[datosTrain.length][2];
for (i=0; i<datosTrain.length; i++) {
classRecord[i][0] = 0;
classRecord[i][1] = 0;
}
classFrecuency = new int[datosTrain.length+1][nClases];
for (i=0; i<datosTrain.length+1; i++)
for (j=0; j<nClases; j++)
classFrecuency[i][j] = 0;
/*Body of the IB3 algorithm. If an instance of the train set does not agree with the nearest
acceptable instance in S, it is added to S*/
for (o=0; o<datosTrain.length; o++) {
i = baraje[o]; //maintain the random order
if (nCD == 0) {//S set empty
CD[nCD] = i; //put an instance
nCD++;
/*Increment clasification*/
classFrecuency[i][clasesTrain[i]] ++;
classFrecuency[datosTrain.length][clasesTrain[i]] ++;
/*update register of this instance*/
classRecord[i][0] ++; //get right
} else {
/*Compute the distance from i to j, with j in CD*/
for (j=0; j<nCD; j++) {
sim[CD[j]] = KNN.distancia(datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], datosTrain[CD[j]], realTrain[CD[j]], nominalTrain[CD[j]], nulosTrain[CD[j]], distanceEu);
}
/*Check if there is any j that it is acceptable*/
aceptable = false;
bestsim = Double.POSITIVE_INFINITY;
seleccionada = ymax = -1;
for (j=0; j<nCD; j++) {
if (esAceptable(CD[j], classRecord, classFrecuency, i)) {
if (sim[CD[j]] < bestsim) {
aceptable = true;
ymax = CD[j];
bestsim = sim[CD[j]];
seleccionada = CD[j];
}
}
}
/*If there is not acceptables, take one of nCD*/
if (!aceptable) {
ymax = CD[Randomize.Randint(0,nCD-1)];
seleccionada = ymax;
}
if (clasesTrain[i] != clasesTrain[ymax]) {
CD[nCD] = i;
sim[CD[nCD]] = 0;
nCD++;
}
nCD = ActualizaRegistro (sim, CD, nCD, classRecord, clasesTrain[i], classFrecuency, i, seleccionada);
}
}
/*Building of the S set from CD*/
conjS = new double[nCD][datosTrain[0].length];
conjR = new double[nCD][datosTrain[0].length];
conjN = new int[nCD][datosTrain[0].length];
conjM = new boolean[nCD][datosTrain[0].length];
clasesS = new int[nCD];
for (i=0; i<nCD; i++) {
for (j=0; j<datosTrain[0].length; j++) {
conjS[i][j] = datosTrain[CD[i]][j];
conjR[i][j] = realTrain[CD[i]][j];
conjN[i][j] = nominalTrain[CD[i]][j];
conjM[i][j] = nulosTrain[CD[i]][j];
}
clasesS[i] = clasesTrain[CD[i]];
}
System.out.println("IB3 "+ 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);
}
/*Function that returns if an instance is acceptable or not*/
boolean esAceptable (int muest, int clRecord[][], int clasFrec[][], int total) {
double p, z, n;
double aux;
double factor;
double denominador, numeradorMax, numeradorMin;
double umbralMaxAc, umbralMinAc;
double umbralMaxFr, umbralMinFr;
int i;
boolean acept = false;
n = (double)(clRecord[muest][0]+clRecord[muest][1]);
aux = (double)clRecord[muest][0];
p = aux / n;
z = nAcep;
factor = ((p*(1-p))/n) + ((z*z)/(4*n*n));
factor = z*Math.sqrt(factor);
denominador = 1+(z*z/n);
numeradorMax = (p + (z*z/2*n)+factor);
numeradorMin = (p + (z*z/2*n)-factor);
umbralMaxAc = numeradorMax / denominador;
umbralMinAc = numeradorMin / denominador;
umbralMinAc = p * nAcep;
n = 0;
for (i=0; i<nClases; i++)
n = n + (double)clasFrec[muest][i];
aux = (double)clasFrec[muest][clasesTrain[muest]];
p = aux / n;
z = nAcep;
factor = (p*(1-p))/n + (z*z)/(4*n*n);
factor = z*Math.sqrt(factor);
denominador = 1+(z*z/n);
numeradorMax = (p + z*z/2*n+factor);
numeradorMin = (p + z*z/2*n-factor);
umbralMaxFr = numeradorMax / denominador;
umbralMinFr = numeradorMin / denominador;
umbralMaxFr = p;
if (umbralMinAc > umbralMaxFr)
acept = true;
return acept;
}
/*Function that updates the clasification registry*/
int ActualizaRegistro (double sim[], int CD[], int nCD, int classRecord[][], int xclase, int clasFrec[][], int total, int usada) {
int i, j;
/*Update of the meter of classes for each instance in CD*/
for (i=0; i<nCD; i++)
clasFrec[CD[i]][xclase] ++;
clasFrec[datosTrain.length][xclase] ++;
/*Update the register of all instances*/
for (i=0; i<nCD; i++) {
if (sim[CD[i]] <= sim[usada]) {
if (clasesTrain[CD[i]] == xclase)
classRecord[CD[i]][0] ++;
else
classRecord[CD[i]][1] ++;
/*Check if eliminate this instance from CD[i]*/
if (esDropable (CD[i], classRecord, clasFrec, total)) {
CD[i] = -1;
}
}
}
/*Eliminate them now*/
for (i=0; i<nCD; i++) {
if (CD[i] < 0) {
for (j=i; j<nCD; j++)
CD[j] = CD[j+1];
nCD--;
i--;
}
}
return nCD;
}
/*Function that returns if an instance is droppable or not*/
boolean esDropable (int muest, int clRecord[][], int clasFrec[][], int total) {
double p, z, n;
double aux;
double factor;
double denominador, numeradorMax, numeradorMin;
double umbralMaxAc, umbralMinAc;
double umbralMaxFr, umbralMinFr;
int i;
boolean acept = false;
n = (double)(clRecord[muest][0]+clRecord[muest][1]);
aux = (double)clRecord[muest][0];
p = aux / n;
z = nDrop;
factor = (p*(1-p))/n + (z*z)/(4*n*n);
factor = z*Math.sqrt(factor);
denominador = 1+(z*z/n);
numeradorMax = (p + z*z/2*n+factor);
numeradorMin = (p + z*z/2*n-factor);
umbralMaxAc = numeradorMax / denominador;
umbralMinAc = numeradorMin / denominador;
umbralMaxAc = p;
n = 0;
for (i=0; i<nClases; i++)
n = n + (double)clasFrec[muest][i];
aux = (double)clasFrec[muest][clasesTrain[muest]];
p = aux / n;
z = nDrop;
factor = (p*(1-p))/n + (z*z)/(4*n*n);
factor = z*Math.sqrt(factor);
denominador = 1+(z*z/n);
numeradorMax = (p + z*z/2*n+factor);
numeradorMin = (p + z*z/2*n-factor);
umbralMaxFr = numeradorMax / denominador;
umbralMinFr = numeradorMin / denominador;
umbralMinFr = p * nDrop;
if (umbralMaxAc < umbralMinFr)
acept = true;
return acept;
}
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 seed*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
semilla = Long.parseLong(tokens.nextToken().substring(1));
/*Getting the confianze level of acceptability and dropping*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
nAcep = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
nDrop = 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;
}
}