/***********************************************************************
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: CFKNN.java
*
* The CFKNN 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.CFKNN;
import java.text.DecimalFormat;
import java.text.DecimalFormatSymbols;
import java.util.Arrays;
import java.util.StringTokenizer;
import org.core.Files;
import org.core.Randomize;
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 CFKNN extends FuzzyIBLAlgorithm {
private int K;
private double alpha;
private double referenceMembership [][];
private double testMembership [][];
private double membership [][];
private int selected [];
/**
* 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 seed
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
seed = Long.parseLong(tokens.nextToken().substring(1));
//Getting the K parameter
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
K = Integer.parseInt(tokens.nextToken().substring(1));
//Getting the Alpha parameter
line = fileLines.nextToken();
tokens = new StringTokenizer (line, "=");
tokens.nextToken();
alpha = Double.parseDouble(tokens.nextToken().substring(1));
} //end-method
/**
* Main builder. Initializes the methods' structures
*
* @param script Configuration script
*/
public CFKNN(String script){
readDataFiles(script);
//Naming the algorithm
name="CFKNN";
membership=new double[trainData.length][nClasses];
selected=new int[trainData.length];
referenceMembership = new double [referenceData.length][nClasses];
testMembership = new double [testData.length][nClasses];
//Initialization of Reporting tool
ReportTool.setOutputFile(outFile[2]);
} //end-method
private void editTrainingSet(){
int pos;
int count;
Arrays.fill(selected, 0);
//select initial prototypes
for (int i=0; i<nClasses; i++) {
pos = Randomize.Randint (0, trainOutput.length);
count=0;
while (trainOutput[pos] != i && count < trainOutput.length) {
pos = (pos + 1) % trainOutput.length;
count++;
}
if (count < trainOutput.length) {
selected[pos]=1;
}
}
//set a random order for the instances
int order [] = new int [trainData.length];
for (int i=0; i<trainData.length; i++) {
order[i]=i;
}
shuffleVector(order);
//test the inclusion of instances
int index;
double minDist[];
int nearestN[];
double dist;
boolean stop;
double classMembership[];
classMembership=new double[nClasses];
for (int i=0; i<trainData.length; i++) {
index=order[i];
Arrays.fill(classMembership, 0.0);
if(selected[index]==0){
//find its K nearest neighbors
nearestN = new int[K];
minDist = new double[K];
for (int i2=0; i2<K; i2++) {
nearestN[i2] = -1;
minDist[i2] = Double.MAX_VALUE;
}
//KNN Method starts here
for (int i2=0; i2<trainData.length; i2++) {
dist = Util.euclideanDistance(trainData[i2],trainData[index]);
if (i2 != index){ //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] = i2;
stop=true;
}
}
}
}
//compute its class membership
double norm[];
double sum;
double MAX_NORM = 100000000;
norm = new double [K];
sum = 0.0;
for(int n = 0;n<K;n++){
if(nearestN[n]!=-1){
if(minDist[n]==0.0){
norm[n]=MAX_NORM;
}
norm[n] = 1.0/ Math.pow(minDist[n],(2.0/(2.0-1.0)));
norm[n]=Math.min(norm[n],MAX_NORM);
sum+=norm[n];
}
}
for(int n = 0;n<K;n++){
if(nearestN[n]!=-1){
for(int c=0;c<nClasses;c++){
classMembership[c]+= membership[nearestN[n]][c]*(norm[n]/sum);
}
}
}
//compute entropy
double entropy= 0.0;
double term;
for(int c=0;c<nClasses;c++){
term=classMembership[c]*(Math.log(classMembership[c]) / Math.log(2));
entropy+=term;
}
entropy=-entropy;
//select instance
if(entropy>alpha){
selected[index]=1;
}
}
}
}
private void shuffleVector(int vector []){
int pos,tmp;
for (int i=0; i<vector.length; i++) {
pos = Randomize.Randint (0, vector.length);
tmp = vector[i];
vector[i] = vector[pos];
vector[pos] = tmp;
}
}
private void assignMemberships(){
double prototypes [][];
double dist;
double distances[];
double sum;
//compute prototypes
prototypes = new double [nClasses][inputAtt];
for(int i=0;i<nClasses;i++){
Arrays.fill(prototypes[i],0.0);
}
for(int i=0;i<trainData.length;i++){
for(int j=0;j<trainData[0].length;j++){
prototypes[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){
prototypes[i][j]/=(double)nInstances[i];
}
}
}
distances=new double [nClasses];
for(int i=0;i<trainData.length;i++){
sum=0.0;
for(int j=0;j<nClasses;j++){
dist=Util.euclideanDistance(trainData[i], prototypes[j]);
dist=1.0/(dist*dist);
distances[j]=dist;
sum+=dist;
}
for(int j=0;j<nClasses;j++){
membership[i][j]=distances[j]/sum;
}
}
}
/**
* 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]=classifyTrain(i,trainData[i]);
}
} //end-method
/**
* Classifies the test set
*/
public void classifyTestSet(){
for(int i=0;i<testData.length;i++){
testPrediction[i]=classifyTest(i,testData[i]);
}
} //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 int classifyTrain(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(selected[i]==1 && index!=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;
double MAX_NORM = 100000000;
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/(2.0-1.0)));
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);
}
}
return computeClass(referenceMembership [index]);
}
/**
* Evaluates a instance to predict its class membership
*
* @param index Index of the instance in the test set
* @param example Instance evaluated
*
*/
private int classifyTest(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(selected[i]==1){
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;
double MAX_NORM = 100000000;
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/(2.0-1.0)));
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);
}
}
return computeClass(testMembership [index]);
}
/**
* 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
/**
* Generates the model of the algorithm
*/
public void generateModel(){
//Start of model time
Timer.resetTime();
assignMemberships();
editTrainingSet();
//End of model time
Timer.setModelTime();
//Showing results
System.out.println(name+" "+ relation + " Model " + Timer.getModelTime() + "s");
}
/**
* 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