/***********************************************************************
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: FRNN_FRS.java
*
* The FRNN_FRS 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.FRNN_FRS;
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 FRNN_FRS extends FuzzyIBLAlgorithm {
private int K;
/**
* Reads the parameters of the algorithm.
*
* @param script Configuration script
*
*/
@Override
protected void readParameters(String script) {
String file;
String line;
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 K parameter
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
K = Integer.parseInt(tokens.nextToken().substring(1));
} //end-method
/**
* Main builder. Initializes the methods' structures
*
* @param script Configuration script
*/
public FRNN_FRS(String script){
readDataFiles(script);
//Naming the algorithm
name="Fuzzy Rough nearest neighbor";
//Initialization of Reporting tool
ReportTool.setOutputFile(outFile[2]);
} //end-method
/**
* Generates the model of the algorithm
*/
public void generateModel (){
int index1,index2;
//Start of model time
Timer.resetTime();
//End of model time
Timer.setModelTime();
//Showing results
System.out.println(name+" "+ relation + " Model " + Timer.getModelTime() + "s");
} //end-method
private int classifyInstance(int index, boolean train){
double minDist[];
int nearestN[];
double dist;
boolean stop;
double min;
double quality;
double R[];
double lower[];
double upper[];
int outputClass;
nearestN = new int[K];
minDist = new double[K];
R = new double[K];
lower = new double[nClasses];
upper = new double[nClasses];
for (int i=0; i<K; i++) {
nearestN[i] = 0;
minDist[i] = Double.MAX_VALUE;
}
//KNN Method starts here
for (int i=0; i<trainData.length; i++) {
if(train){
if(i==index){
dist = Double.MAX_VALUE;
}else{
dist = Util.euclideanDistance(trainData[i],trainData[index]);
}
}
else{
dist = Util.euclideanDistance(trainData[i],testData[index]);
}
//see if it's nearer than our previous selected neighbors
stop=false;
for(int j=0;j<K && !stop;j++){
if (dist < minDist[j]) {
for (int l = K - 1; l >= j+1; l--) {
minDist[l] = minDist[l - 1];
nearestN[l] = nearestN[l - 1];
}
minDist[j] = dist;
nearestN[j] = i;
stop=true;
}
}
}
quality=0;
Arrays.fill(R, 0.0);
Arrays.fill(lower, 1.0);
Arrays.fill(upper, 0.0);
min=Double.MAX_VALUE;
for (int l = 0; l< K; l++){
for(int j=0;j<inputAtt;j++){
if(train){
dist=1.0-Math.abs(trainData[nearestN[l]][j]-trainData[index][j]);
}
else{
dist=1.0-Math.abs(trainData[nearestN[l]][j]-testData[index][j]);
}
if(min>dist){
min=dist;
}
}
R[l]=min;
//A(x) is 1 for the training output class... 0 for the rest
for(int c=0; c<nClasses; c++){
if(c==trainOutput[nearestN[l]]){
//lower approximation
lower[c]=Math.min(lower[c], Math.max(1.0-R[l],1.0));
//upper approximation
upper[c]=Math.max(upper[c], Math.min(R[l],1.0));
}
else{
lower[c]=Math.min(lower[c], Math.max(1.0-R[l],0.0));
//upper approximation
upper[c]=Math.max(upper[c], Math.min(R[l],0.0));
}
}
}
outputClass=-1;
for(int c=0; c<nClasses; c++){
if(quality<(lower[c]+upper[c])/2.0){
quality=(lower[c]+upper[c])/2.0;
outputClass=c;
}
}
return outputClass;
}
/**
* 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]=classifyInstance(i, true);
}
} //end-method
/**
* Classifies the test set
*/
public void classifyTestSet(){
for(int i=0;i<testData.length;i++){
testPrediction[i]=classifyInstance(i, false);
}
} //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