/***********************************************************************
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/
**********************************************************************/
package keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW;
import java.io.IOException;
import org.core.*;
/**
* <p>It contains the implementation of the Chi algorithm</p>
*
* @author Written by Alberto Fern�ndez (University of Granada) 02/11/2007
* @version 1.0
* @since JDK1.5
*/
public class Fuzzy_Chi {
myDataset train, val, test;
String outputTr, outputTst, fileDB, fileRB;
int nClasses, nLabels, combinationType, inferenceType, ruleWeight;
DataBase dataBase;
RuleBase ruleBase;
public static final int MINIMUM = 0;
public static final int PRODUCT = 1;
public static final int CF = 0;
public static final int PCF_IV = 1;
public static final int MCF = 2;
public static final int NO_RW = 3;
public static final int PCF_II = 3;
public static final int WINNING_RULE = 0;
public static final int ADDITIVE_COMBINATION = 1;
//We may declare here the algorithm's parameters
private boolean somethingWrong = false; //to check if everything is correct.
/**
* Default constructor
*/
public Fuzzy_Chi() {
}
/**
* It reads the data from the input files (training, validation and test) and parse all the parameters
* from the parameters array.
* @param parameters parseParameters It contains the input files, output files and parameters
*/
public Fuzzy_Chi(parseParameters parameters) {
train = new myDataset();
val = new myDataset();
test = new myDataset();
try {
System.out.println("\nReading the training set: " +
parameters.getTrainingInputFile());
train.readClassificationSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " +
parameters.getValidationInputFile());
val.readClassificationSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " +
parameters.getTestInputFile());
test.readClassificationSet(parameters.getTestInputFile(), false);
} catch (IOException e) {
System.err.println(
"There was a problem while reading the input data-sets: " +
e);
somethingWrong = true;
}
//We may check if there are some numerical attributes, because our algorithm may not handle them:
//somethingWrong = somethingWrong || train.hasNumericalAttributes();
somethingWrong = somethingWrong || train.hasMissingAttributes();
outputTr = parameters.getTrainingOutputFile();
outputTst = parameters.getTestOutputFile();
fileDB = parameters.getOutputFile(0);
fileRB = parameters.getOutputFile(1);
//Now we parse the parameters
nLabels = Integer.parseInt(parameters.getParameter(0));
String aux = parameters.getParameter(1); //Computation of the compatibility degree
combinationType = PRODUCT;
if (aux.compareToIgnoreCase("minimum") == 0) {
combinationType = MINIMUM;
}
aux = parameters.getParameter(2);
ruleWeight = PCF_IV;
if (aux.compareToIgnoreCase("Certainty_Factor") == 0) {
ruleWeight = CF;
}
else if (aux.compareToIgnoreCase("Average_Penalized_Certainty_Factor") == 0) {
ruleWeight = PCF_II;
}
else if (aux.compareToIgnoreCase("No_Weights") == 0){
ruleWeight = NO_RW;
}
aux = parameters.getParameter(3);
inferenceType = WINNING_RULE;
if (aux.compareToIgnoreCase("Additive_Combination") == 0) {
inferenceType = ADDITIVE_COMBINATION;
}
}
/**
* It launches the algorithm
*/
public void execute() {
if (somethingWrong) { //We do not execute the program
System.err.println("An error was found, the data-set have missing values");
System.err.println("Please remove those values before the execution");
System.err.println("Aborting the program");
//We should not use the statement: System.exit(-1);
}
else {
//We do here the algorithm's operations
nClasses = train.getnClasses();
dataBase = new DataBase(train.getnInputs(), nLabels,
train.getRanges(),train.getNames());
ruleBase = new RuleBase(dataBase, inferenceType, combinationType,
ruleWeight, train.getNames(), train.getClasses());
System.out.println("Data Base:\n"+dataBase.printString());
ruleBase.Generation(train);
dataBase.writeFile(this.fileDB);
ruleBase.writeFile(this.fileRB);
//Finally we should fill the training and test output files
double accTra = doOutput(this.val, this.outputTr);
double accTst = doOutput(this.test, this.outputTst);
System.out.println("Accuracy obtained in training: "+accTra);
System.out.println("Accuracy obtained in test: "+accTst);
System.out.println("Algorithm Finished");
}
}
/**
* It generates the output file from a given dataset and stores it in a file
* @param dataset myDataset input dataset
* @param filename String the name of the file
*
* @return The classification accuracy
*/
private double doOutput(myDataset dataset, String filename) {
String output = new String("");
int hits = 0;
output = dataset.copyHeader(); //we insert the header in the output file
//We write the output for each example
for (int i = 0; i < dataset.getnData(); i++) {
//for classification:
String classOut = this.classificationOutput(dataset.getExample(i));
output += dataset.getOutputAsString(i) + " " + classOut + "\n";
if (dataset.getOutputAsString(i).equalsIgnoreCase(classOut)){
hits++;
}
}
Files.writeFile(filename, output);
return (1.0*hits/dataset.size());
}
/**
* It returns the algorithm classification output given an input example
* @param example double[] The input example
* @return String the output generated by the algorithm
*/
private String classificationOutput(double[] example) {
String output = new String("?");
/**
Here we should include the algorithm directives to generate the
classification output from the input example
*/
int classOut = ruleBase.FRM(example);
if (classOut >= 0) {
output = train.getOutputValue(classOut);
}
return output;
}
}