/***********************************************************************
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/
**********************************************************************/
/*
* To change this template, choose Tools | Templates
* and open the template in the editor.
*/
package keel.Algorithms.Semi_Supervised_Learning.Basic;
//import keel.Algorithms.Preprocess.Instance_Generation.Basic.*;
import keel.Algorithms.Semi_Supervised_Learning.utilities.*;
import keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.*;
import keel.Dataset.*;
import java.util.*;
import org.core.*;
/**
* Measures classification accuracy of a reduced set.
* That is, compute the percentage of instances well-classificated using as training set
* the reduced set.
* @author diegoj
*/
public class AccuracyMeter
{
/** Posible yes variants of the command line. */
protected static final String[] yes ={ "yes", "Yes", "YES", "Y", "yeah", "oh yeah" };
/**
* Informs if a string is equal to someone in an array
* @param s String to be tested.
* @param posibilities Posible values of s.
* @return TRUE if s is in posibilities, FALSE in other chase.
*/
protected static boolean isIn(String s, String[] posibilities )
{
boolean value = false;
for(int i=0; !value && i<posibilities.length; ++i)
value = value || s.equals(posibilities[i]);
return value;
}
/**
* Informs if a string is yes.
* @param s String to be tested.
* @return TRUE if s is some kind of yes, FALSE in other chase.
*/
protected static boolean isYes(String s)
{
return isIn(s, yes);
}
/**
* Main. Measures the accuracy of a reduced set and saves results in a file.
* @param args Command line arguments.
*/
public static void main(String[] args) throws Exception
{
//Parameters.setUse("PrototypeGenerator", "<algorithmName used> <accuracy_output_FILE> [append]");
Parameters.assertBasicArgs(args);
Debug.endsIf(args.length<4, "Use: <training data set> <reduced set file> <test set file> <algorithmName used in reduction> <output file> [append]");
String algoName = args[3];
String outputFile = args[4];
boolean append = false;
if(args.length==6)
append = isYes(args[5]);
//Debug.errorln(args[0] + " tiene el conjunto de datos original");
//Debug.errorln(args[1] + " tiene el conjunto de datos reducidos");
//Debug.errorln(args[2] + " tiene el conjunto de datos test");
PrototypeSet trainingSet = PrototypeGenerationAlgorithm.readPrototypeSet(args[0]);
PrototypeSet reducedSet = PrototypeGenerationAlgorithm.readPrototypeSet(args[1]);
PrototypeSet test = PrototypeGenerationAlgorithm.readPrototypeSet(args[2]);
PrototypeGenerator gen = new PrototypeGenerator(reducedSet);
gen.execute();
//Debug.errorln(args[1] + " executed");
int accuracy1NN = KNN.classficationAccuracy1NN(reducedSet, test);
/* String data = gen.getResults(Parameters.getFileName(), algoName, accuracy1NN, trainingSet.size(), test);
if(append)
KeelFile.append(outputFile, data);
else
KeelFile.write(outputFile, data);
System.out.flush();
*///PrototypeGenerator.saveResultsOfAccuracyIn(Parameters.getFileName(), algoName, reducedSet, test, outputFile, append);
}
}