Main.java

/***********************************************************************

	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.Discretizers.Bayesian_Discretizer;

import keel.Dataset.*;
import keel.Algorithms.Genetic_Rule_Learning.Globals.*;
import keel.Algorithms.Discretizers.Basic.*;


/**
 * <p>
 * This is the main class of the algorithm.
 * It gets the configuration script, finds the discretization accordingly to the model, and
 * applies it to the data.
 * 
 * @author Written by Victoria Lopez Morales (University of Granada) 17/12/2009 
 * @version 1.0
 * @since JDK1.5
 * </p>
 */
public class Main {

	
	/** Creates a new instance of Main */
	public Main() {
	}
	
	/**
	 * It runs the algorithm
	 * 
	 * @param args the command line arguments
	 */
	public static void main(String[] args) {
		ParserParameters.doParse(args[0]);
		LogManager.initLogManager();

		InstanceSet is=new InstanceSet();
		try {	
			is.readSet(Parameters.trainInputFile,true);
                } catch(Exception e) {
                        LogManager.printErr(e.toString());
                        System.exit(1);
                }
		checkDataset();

		Discretizer dis;
		String name=Parameters.algorithmName;
		dis=new BayesianDiscretizer();
		dis.buildCutPoints(is);
		dis.applyDiscretization(Parameters.trainInputFile,Parameters.trainOutputFile);
		dis.applyDiscretization(Parameters.testInputFile,Parameters.testOutputFile);
		LogManager.closeLog();
	}

        static void checkDataset() {
                Attribute []outputs=Attributes.getOutputAttributes();
                if(outputs.length!=1) {
                        LogManager.printErr("Only datasets with one output are supported");
                        System.exit(1);
                }
                if(outputs[0].getType()!=Attribute.NOMINAL) {
                        LogManager.printErr("Output attribute should be nominal");
                        System.exit(1);
                }
                Parameters.numClasses=outputs[0].getNumNominalValues();
                Parameters.numAttributes=Attributes.getInputAttributes().length;
        }
        
}