/***********************************************************************
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: FuzzyKNN.java
*
* The FuzzyKNN 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.FuzzyKNN;
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 FuzzyKNN extends FuzzyIBLAlgorithm {
private static final double MAX_NORM = 100000000;
private int K; //K value for Fuzzy K-NN
private double M; //M value for Fuzzy K-NN norm
private int k; //k value for K-NN in membership assignment
private double membership [][];
private double referenceMembership [][];
private double testMembership [][];
private int initialization;
private static final int CRISP = 1;
private static final int CLASS_MEAN = 2;
private static final int KNN = 3 ;
/**
* 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 K parameter
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
K = Integer.parseInt(tokens.nextToken().substring(1));
//Getting the M parameter
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
M = Double.parseDouble(tokens.nextToken().substring(1));
//Getting the membership initialization method
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
type = tokens.nextToken().substring(1);
initialization=CRISP;
if(type.equalsIgnoreCase("CLASS-MEAN")){
initialization=CLASS_MEAN;
}
if(type.equalsIgnoreCase("KNN")){
initialization=KNN;
//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 FuzzyKNN(String script){
readDataFiles(script);
//Naming the algorithm
name="FuzzyKNN";
membership = new double [trainData.length][nClasses];
for(int i=0;i<trainData.length;i++){
Arrays.fill(membership[i], -1.0);
}
referenceMembership = new double [referenceData.length][nClasses];
testMembership = new double [testData.length][nClasses];
//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();
assignMembership();
//End of model time
Timer.setModelTime();
//Showing results
System.out.println(name+" "+ relation + " Model " + Timer.getModelTime() + "s");
}
/**
* Assign class membership to each instance of the training set
*/
private void assignMembership(){
switch(initialization){
case CLASS_MEAN:
double meanInstances [][]= new double [nClasses][inputAtt];
for(int i=0;i<nClasses;i++){
Arrays.fill(meanInstances[i],0.0);
}
for(int i=0;i<trainData.length;i++){
for(int j=0;j<trainData[0].length;j++){
meanInstances[trainOutput[i]][j]+=trainData[i][j];
}
}
for(int i=0;i<nClasses;i++){
for(int j=0;j<trainData[0].length;j++){
if(nInstances[i]>0){
meanInstances[i][j]/=(double)nInstances[i];
}
}
}
//compute membership
double distan[];
double sum,sum2;
distan=new double [nClasses];
for(int i=0;i<trainData.length;i++){
sum=0.0;
for(int j=0;j<nClasses;j++){
distan[j]=Util.euclideanDistance(trainData[i], meanInstances[j]);
sum+=distan[j];
}
sum2=0.0;
for(int j=0;j<nClasses;j++){
if(nInstances[j]>0){
membership[i][j]=1.0-(distan[j]/sum);
sum2+=membership[i][j];
}
else{
membership[i][j]=0.0;
}
}
//normalize memberships
for(int j=0;j<nClasses;j++){
membership[i][j]/=sum2;
}
}
break;
case KNN:
for(int instance=0;instance<trainData.length;instance++){
double minDist[];
int nearestN[];
int selectedClasses[];
double dist;
boolean stop;
nearestN = new int[k];
minDist = new double[k];
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++) {
dist = Util.euclideanDistance(trainData[i],trainData[instance]);
if (i != instance){ //leave-one-out
//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;
}
}
}
}
//we have check all the instances... see what is the most present class
selectedClasses= new int[nClasses];
Arrays.fill(selectedClasses, 0);
for (int i=0; i<k; i++) {
selectedClasses[trainOutput[nearestN[i]]]++;
}
Arrays.fill(membership[instance], 0.0);
double term;
for (int i=0; i<nClasses; i++) {
term = ((double)selectedClasses[i]/(double)k);
if(trainOutput[instance]==i){
membership[instance][i]=0.51+0.49*term;
}else{
membership[instance][i]=0.49*term;
}
}
}
break;
case CRISP:
default:
for(int i=0;i<trainData.length;i++){
Arrays.fill(membership[i], 0.0);
membership[i][trainOutput[i]]=1.0;
}
break;
}
} //end-method
/**
* 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++){
computeTrainMembership(i,referenceData[i]);
trainPrediction[i]=computeClass(referenceMembership[i]);
}
} //end-method
/**
* Classifies the test set
*/
public void classifyTestSet(){
for(int i=0;i<testData.length;i++){
computeTestMembership(i,testData[i]);
testPrediction[i]=computeClass(testMembership[i]);
}
} //end-method
/**
* Computes the class of a instance given its membership array
* @param pertenence Membership array
*
* @return Class assigned (crisp)
*/
private int computeClass(double pertenence[]){
double max = Double.MIN_VALUE;
int output=-1;
for(int i=0; i< pertenence.length;i++){
if(max<pertenence[i]){
max=pertenence[i];
output=i;
}
}
return output;
} //end-method
/**
* Evaluates a instance to predict its class membership
*
* @param index Index of the instance in the test set
* @param example Instance evaluated
*
*/
private void computeTrainMembership(int index, double example[]) {
double minDist[];
int nearestN[];
double dist;
boolean stop;
nearestN = new int[K];
minDist = new double[K];
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(i!=index){ //leave-one-out
dist = Util.euclideanDistance(trainData[i],example);
//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;
}
}
}
}
//compute membership
double norm[];
double sum;
norm = new double [K];
sum = 0.0;
for(int i = 0;i<K;i++){
if(minDist[i]==0.0){
norm[i]=MAX_NORM;
}
norm[i] = 1.0/ Math.pow(minDist[i],(2.0/(M-1.0)));
norm[i]=Math.min(norm[i],MAX_NORM);
sum+=norm[i];
}
for(int i = 0;i<K;i++){
for(int c=0;c<nClasses;c++){
referenceMembership [index][c]+= membership[nearestN[i]][c]*(norm[i]/sum);
}
}
} //end-method
/**
* Evaluates a instance to predict its class membership
*
* @param index Index of the instance in the test set
* @param example Instance evaluated
*
*/
private void computeTestMembership(int index, double example[]) {
double minDist[];
int nearestN[];
double dist;
boolean stop;
nearestN = new int[K];
minDist = new double[K];
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++) {
dist = Util.euclideanDistance(trainData[i],example);
//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;
}
}
}
//compute membership
double norm[];
double sum;
norm = new double [K];
sum = 0.0;
for(int i = 0;i<K;i++){
if(minDist[i]==0.0){
norm[i]=MAX_NORM;
}
norm[i] = 1.0/ Math.pow(minDist[i],(2.0/(M-1.0)));
norm[i]=Math.min(norm[i],MAX_NORM);
sum+=norm[i];
}
for(int i = 0;i<K;i++){
for(int c=0;c<nClasses;c++){
testMembership [index][c]+= membership[nearestN[i]][c]*(norm[i]/sum);
}
}
} //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