/***********************************************************************
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/
**********************************************************************/
/***********************************************************************
This file is part of the Fuzzy Instance Based Learning package, a
Java package implementing Fuzzy Nearest Neighbor Classifiers as
complementary material for the paper:
Fuzzy Nearest Neighbor Algorithms: Taxonomy, Experimental analysis and Prospects
Copyright (C) 2012
J. Derrac (jderrac@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
F. Herrera (herrera@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/
**********************************************************************/
/**
*
* File: PosIBL.java
*
* The PosIBL algorithm.
*
* @author Written by Joaqu�n Derrac (University of Granada) 13/11/2011
* @version 1.0
* @since JDK1.5
*
*/
package keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL;
import java.text.DecimalFormat;
import java.text.DecimalFormatSymbols;
import java.util.Arrays;
import java.util.StringTokenizer;
import org.core.Files;
import keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm;
import keel.Algorithms.Fuzzy_Instance_Based_Learning.ReportTool;
import keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer;
import keel.Algorithms.Fuzzy_Instance_Based_Learning.Util;
public class PosIBL extends FuzzyIBLAlgorithm {
private double BETA;
private double delta [];
/**
* Reads the parameters of the algorithm.
*
* @param script Configuration script
*
*/
@Override
protected void readParameters(String script) {
String file;
String line;
String type;
StringTokenizer fileLines, tokens;
file = Files.readFile (script);
fileLines = new StringTokenizer (file,"\n\r");
//Discard in/out files definition
fileLines.nextToken();
fileLines.nextToken();
fileLines.nextToken();
//Getting the M parameter
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
BETA = Double.parseDouble(tokens.nextToken().substring(1));
} //end-method
/**
* Main builder. Initializes the methods' structures
*
* @param script Configuration script
*/
public PosIBL(String script){
readDataFiles(script);
//Naming the algorithm
name="PosIBL";
delta = new double [trainData.length];
Arrays.fill(delta, 0.0);
//Initialization of Reporting tool
ReportTool.setOutputFile(outFile[2]);
} //end-method
/**
* Generates the model of the algorithm
*/
public void generateModel (){
//Start of model time
Timer.resetTime();
double minDist=0;
double dist;
//search for the nearest enemy
for(int i=0;i<trainData.length;i++){
minDist=Double.MAX_VALUE;
for(int j=0;j<trainData.length;j++){
if(trainOutput[i]!=trainOutput[j]){
dist=Util.euclideanDistance(trainData[i], trainData[j]);
if(minDist>dist){
minDist=dist;
}
}
}
delta[i]=minDist;
if(delta[i]<(1.0-BETA)){
delta[i]=1.0-BETA;
}
}
//End of model time
Timer.setModelTime();
//Showing results
System.out.println(name+" "+ relation + " Model " + Timer.getModelTime() + "s");
}
/**
* Classifies the training set (leave-one-out)
*/
public void classifyTrain(){
//Start of training time
Timer.resetTime();
classifyTrainSet();
//End of training time
Timer.setTrainingTime();
//Showing results
System.out.println(name+" "+ relation + " Training " + Timer.getTrainingTime() + "s");
} //end-method
/**
* Classifies the test set
*/
public void classifyTest(){
//Start of training time
Timer.resetTime();
classifyTestSet();
//End of test time
Timer.setTestTime();
//Showing results
System.out.println(name+" "+ relation + " Test " + Timer.getTestTime() + "s");
} //end-method
/**
* Classifies the training set
*/
public void classifyTrainSet(){
for(int i=0;i<trainData.length;i++){
trainPrediction[i]=computeClass(i);
}
} //end-method
/**
* Classifies the test set
*/
public void classifyTestSet(){
for(int i=0;i<testData.length;i++){
testPrediction[i]=computeTestClass(i);
}
} //end-method
/**
* Computes the class of a instance given its membership array
* @param pertenence Membership array
*
* @return Class assigned (crisp)
*/
private int computeClass(int index){
double maxSim=Double.MIN_VALUE;
double dist,sim;
int nearest=0;
int result;
for(int i=0;i<trainData.length;i++){
if(i!=index){
dist=Util.euclideanDistance(trainData[i], trainData[index]);
//kernel function
dist=1.0-dist;
dist=-1.0*delta[i]*(1.0-dist);
sim=Math.exp(dist);
if(maxSim<sim){
maxSim=sim;
nearest=i;
}
}
}
result=trainOutput[nearest];
return result;
} //end-method
/**
* Computes the class of a instance given its membership array
* @param pertenence Membership array
*
* @return Class assigned (crisp)
*/
private int computeTestClass(int index){
double maxSim=Double.MIN_VALUE;
double dist,sim;
int nearest=0;
int result;
for(int i=0;i<trainData.length;i++){
dist=Util.euclideanDistance(trainData[i], testData[index]);
//kernel function
dist=1.0-dist;
dist= -1.0*delta[i]*(1.0-dist);
sim=Math.exp(dist);
if(maxSim<sim){
maxSim=sim;
nearest=i;
}
}
result=trainOutput[nearest];
return result;
} //end-method
/**
* Reports the results obtained
*/
public void printReport(){
writeOutput(outFile[0], trainOutput, trainPrediction);
writeOutput(outFile[1], testOutput, testPrediction);
ReportTool.setResults(trainOutput,trainPrediction,testOutput,testPrediction,nClasses);
ReportTool.printReport();
} //end-method
} //end-class