/***********************************************************************
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/
**********************************************************************/
/**
* <p>
* @author Written by Juli�n Luengo Mart�n 04/12/2006
* @version 0.1
* @since JDK 1.5
* </p>
*/
package keel.Algorithms.Preprocess.Missing_Values.fkmeans;
import java.io.*;
import java.util.*;
import keel.Dataset.*;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
/**
* <p>
* This class imputes the missing values by means of the Fuzzy K-means clustering algorithm. It creates a set of K fuzzy-clusters, and the missing values
* are filled in with the all the centroids, weighting the values with the membership degree of the instance to each cluster (based on the distance).
* </p>
*/
public class fkmeans {
double [] mean = null;
double [] std_dev = null;
double tempData = 0;
String[][] X = null; //matrix of transformed data
FreqList[] timesSeen = null; //matrix with frequences of attribute values
String[] mostCommon;
int ndatos = 0;
int nentradas = 0;
int tipo = 0;
int direccion = 0;
int nvariables = 0;
int nsalidas = 0;
int K = 1; //number of clusters
long semilla = 12345678;
double minError = 1;
int maxIter = 1000;
double fuzzifier;
InstanceSet IS,IStest;
String input_train_name = new String();
String input_test_name = new String();
String output_train_name = new String();
String output_test_name = new String();
String temp = new String();
String data_out = new String("");
/** Creates a new instance of fkmeans
* @param fileParam The path to the configuration file with all the parameters in KEEL format
*/
public fkmeans(String fileParam) {
config_read(fileParam);
IS = new InstanceSet();
IStest = new InstanceSet();
}
//Write data matrix X to disk, in KEEL format
private void write_results(String output){
//File OutputFile = new File(output_train_name.substring(1, output_train_name.length()-1));
try {
FileWriter file_write = new FileWriter(output);
file_write.write(IS.getHeader());
//now, print the normalized data
file_write.write("@data\n");
for(int i=0;i<ndatos;i++){
file_write.write(X[i][0]);
for(int j=1;j<nvariables;j++){
file_write.write(","+X[i][j]);
}
file_write.write("\n");
}
file_write.close();
} catch (IOException e) {
System.out.println("IO exception = " + e );
System.exit(-1);
}
}
//Read the pattern file, and parse data into strings
private void config_read(String fileParam){
File inputFile = new File(fileParam);
if (inputFile == null || !inputFile.exists()) {
System.out.println("parameter "+fileParam+" file doesn't exists!");
System.exit(-1);
}
//begin the configuration read from file
try {
FileReader file_reader = new FileReader(inputFile);
BufferedReader buf_reader = new BufferedReader(file_reader);
//FileWriter file_write = new FileWriter(outputFile);
String line;
do{
line = buf_reader.readLine();
}while(line.length()==0); //avoid empty lines for processing -> produce exec failure
String out[]= line.split("algorithm = ");
//alg_name = new String(out[1]); //catch the algorithm name
//input & output filenames
do{
line = buf_reader.readLine();
}while(line.length()==0);
out= line.split("inputData = ");
out = out[1].split("\\s\"");
input_train_name = new String(out[0].substring(1, out[0].length()-1));
input_test_name = new String(out[1].substring(0, out[1].length()-1));
if(input_test_name.charAt(input_test_name.length()-1)=='"')
input_test_name = input_test_name.substring(0,input_test_name.length()-1);
do{
line = buf_reader.readLine();
}while(line.length()==0);
out = line.split("outputData = ");
out = out[1].split("\\s\"");
output_train_name = new String(out[0].substring(1, out[0].length()-1));
output_test_name = new String(out[1].substring(0, out[1].length()-1));
if(output_test_name.charAt(output_test_name.length()-1)=='"')
output_test_name = output_test_name.substring(0,output_test_name.length()-1);
//parameters
do{
line = buf_reader.readLine();
}while(line.length()==0);
out = line.split("seed = ");
semilla = (new Long(out[1])).longValue(); //parse the string into a integer
do{
line = buf_reader.readLine();
}while(line.length()==0);
out = line.split("k = ");
K = (new Integer(out[1])).intValue(); //parse the string into a integer
do{
line = buf_reader.readLine();
}while(line.length()==0);
out = line.split("error = ");
minError = (new Double(out[1])).doubleValue(); //parse the string into a double
do{
line = buf_reader.readLine();
}while(line.length()==0);
out = line.split("iterations = ");
maxIter = (new Integer(out[1])).intValue(); //parse the string into a double
do{
line = buf_reader.readLine();
}while(line.length()==0);
out = line.split("m = ");
fuzzifier = (new Double(out[1])).doubleValue(); //parse the string into a integer
file_reader.close();
} catch (IOException e) {
System.out.println("IO exception = " + e );
e.printStackTrace();
System.exit(-1);
}
}
/**
* <p>
* Computes the distance between two instances (without previous normalization)
* </p>
* @param i First instance
* @param j Second instance
* @return The Euclidean distance between i and j
*/
private double distance(Instance i,Instance j){
double dist = 0;
int in = 0;
int out = 0;
for(int l = 0; l < nvariables;l++){
Attribute a = Attributes.getAttribute(l);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if(direccion == Attribute.INPUT){
if(tipo != Attribute.NOMINAL && !i.getInputMissingValues(in)){
//real value, apply euclidean distance
dist += (i.getInputRealValues(in)-j.getInputRealValues(in))*(i.getInputRealValues(in)-j.getInputRealValues(in));
} else{
if(!i.getInputMissingValues(in) && i.getInputNominalValues(in)!=j.getInputNominalValues(in))
dist += 1;
}
in++;
}else{
if(direccion == Attribute.OUTPUT){
if(tipo != Attribute.NOMINAL && !i.getOutputMissingValues(out)){
dist += (i.getOutputRealValues(out)-j.getOutputRealValues(out))*(i.getOutputRealValues(out)-j.getOutputRealValues(out));
} else{
if(!i.getOutputMissingValues(out) && i.getOutputNominalValues(out)!=j.getOutputNominalValues(out))
dist += 1;
}
out++;
}
}
}
return dist;
}
/**
* <p>
* Process the training and test files provided in the parameters file to the constructor.
* </p>
*/
public void process(){
//declarations
double []outputs;
double []outputs2;
Instance neighbor;
double dist,mean,tmp;
int actual;
Randomize rnd = new Randomize();
Instance ex;
fuzzygCenter kmeans = null;
int iterations = 0;
double E;
double prevE;
int totalMissing = 0;
boolean allMissing = true;
rnd.setSeed(semilla);
//PROCESS
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name,true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables];//matrix with transformed data
kmeans = new fuzzygCenter(K,ndatos,nvariables,fuzzifier);
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
//first, we choose k 'means' randomly from all
//instances
totalMissing = 0;
for(int i = 0;i < ndatos;i++){
Instance inst = IS.getInstance(i);
if(inst.existsAnyMissingValue())
totalMissing++;
}
if(totalMissing == ndatos)
allMissing = true;
else
allMissing = false;
for(int numMeans = 0;numMeans<K;numMeans++){
do{
actual = (int) (ndatos*rnd.Rand());
ex = IS.getInstance(actual);
}while(ex.existsAnyMissingValue() && !allMissing);
kmeans.copyCenter(ex,numMeans);
}
//now, iterate adjusting clusters' centers and
//instances to them
prevE = 0;
iterations = 0;
do{
for(int i = 0;i < ndatos;i++){
Instance inst = IS.getInstance(i);
kmeans.setMembershipOf(inst,i);
}
//set new centers
kmeans.recalculateCenters(IS);
//compute RMSE
E = 0;
for(int i = 0;i < ndatos;i++){
Instance inst = IS.getInstance(i);
for(int k=0;k<K;k++){
E += (kmeans.distance(inst,k)*kmeans.getMembershipOf(i,k));
}
}
iterations++;
//System.out.println(iterations+"\t"+E);
if(Math.abs(prevE - E ) == 0)
iterations = maxIter;
else
prevE = E;
}while(E>minError && iterations < maxIter);
for(int i = 0;i < ndatos;i++){
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
for(int j = 0; j < nvariables;j++){
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if(direccion == Attribute.INPUT){
if(tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)){
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else{
if(!inst.getInputMissingValues(in))
X[i][j] = inst.getInputNominalValues(in);
else{
if(tipo != Attribute.NOMINAL){
tmp = -1.0;
for(int k=0;k<K;k++){
if(kmeans.valueAt(k,j).compareTo("<null>")!=0){
if(tmp==-1.0)
tmp = 0.0;
tmp += kmeans.getMembershipOf(i,k)*new Double(kmeans.valueAt(k,j)).doubleValue();
if(tmp < a.getMinAttribute())
tmp = a.getMinAttribute();
if(tmp > a.getMaxAttribute())
tmp = a.getMaxAttribute();
}
}
if(tmp!=-1.0){
if(tipo==Attribute.INTEGER)
tmp = (int) tmp;
X[i][j] = new String(String.valueOf(tmp));
}
else
X[i][j] = "<null>";
}else{
actual = kmeans.getClusterOf(inst);
X[i][j] = new String(kmeans.valueAt(actual,j));
}
}
}
in++;
} else{
if(direccion == Attribute.OUTPUT){
if(tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)){
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else{
if(!inst.getOutputMissingValues(out))
X[i][j] = inst.getOutputNominalValues(out);
else{
if(tipo != Attribute.NOMINAL){
tmp = -1.0;
for(int k=0;k<K;k++){
if(kmeans.valueAt(k,j).compareTo("<null>")!=0){
if(tmp==-1.0)
tmp = 0.0;
tmp += kmeans.getMembershipOf(i,k)*new Double(kmeans.valueAt(k,j)).doubleValue();
if(tmp < a.getMinAttribute())
tmp = a.getMinAttribute();
if(tmp > a.getMaxAttribute())
tmp = a.getMaxAttribute();
}
}
if(tmp!=-1.0){
if(tipo==Attribute.INTEGER)
tmp = (int) tmp;
X[i][j] = new String(String.valueOf(tmp));
}
else
X[i][j] = "<null>";
}else{
actual = kmeans.getClusterOf(inst);
X[i][j] = new String(kmeans.valueAt(actual,j));
}
}
}
out++;
}
}
}
}
}catch (Exception e){
System.out.println("Dataset exception = " + e );
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/***************************************************************************************/
//does a test file associated exist?
if(input_train_name.compareTo(input_test_name)!=0){
try {
// Load in memory a dataset that contains a classification problem
IStest.readSet(input_test_name,false);
int in = 0;
int out = 0;
ndatos = IStest.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables];//matrix with transformed data
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
for(int i = 0;i < ndatos;i++){
Instance inst = IStest.getInstance(i);
in = 0;
out = 0;
for(int j = 0; j < nvariables;j++){
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if(direccion == Attribute.INPUT){
if(tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)){
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else{
if(!inst.getInputMissingValues(in))
X[i][j] = inst.getInputNominalValues(in);
else{
if(tipo != Attribute.NOMINAL){
tmp = -1.0;
for(int k=0;k<K;k++){
if(kmeans.valueAt(k,j).compareTo("<null>")!=0){
if(tmp==-1.0)
tmp = 0.0;
tmp += kmeans.getMembershipOf(i,k)*new Double(kmeans.valueAt(k,j)).doubleValue();
if(tmp < a.getMinAttribute())
tmp = a.getMinAttribute();
if(tmp > a.getMaxAttribute())
tmp = a.getMaxAttribute();
}
}
if(tmp!=-1.0){
if(tipo==Attribute.INTEGER)
tmp = (int) tmp;
X[i][j] = new String(String.valueOf(tmp));
}
else
X[i][j] = "<null>";
}else{
actual = kmeans.getClusterOf(inst);
X[i][j] = new String(kmeans.valueAt(actual,j));
}
}
}
in++;
} else{
if(direccion == Attribute.OUTPUT){
if(tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)){
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else{
if(!inst.getOutputMissingValues(out))
X[i][j] = inst.getOutputNominalValues(out);
else{
if(tipo != Attribute.NOMINAL){
tmp = -1.0;
for(int k=0;k<K;k++){
if(kmeans.valueAt(k,j).compareTo("<null>")!=0){
if(tmp==-1.0)
tmp = 0.0;
tmp += kmeans.getMembershipOf(i,k)*new Double(kmeans.valueAt(k,j)).doubleValue();
if(tmp < a.getMinAttribute())
tmp = a.getMinAttribute();
if(tmp > a.getMaxAttribute())
tmp = a.getMaxAttribute();
}
}
if(tmp!=-1.0){
if(tipo==Attribute.INTEGER)
tmp = (int) tmp;
X[i][j] = new String(String.valueOf(tmp));
}
else
X[i][j] = "<null>";
}else{
actual = kmeans.getClusterOf(inst);
X[i][j] = new String(kmeans.valueAt(actual,j));
}
}
}
out++;
}
}
}
}
}catch (Exception e){
System.out.println("Dataset exception = " + e );
e.printStackTrace();
System.exit(-1);
}
write_results(output_test_name);
}
}
}