selectionexamplereader.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  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 2 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, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
 *  USA.
 */
package edu.udo.cs.yale.example;

import java.util.Iterator;
import edu.udo.cs.yale.MethodNotSupportedException;

/** This reader takes the selection of attributes into account and returns selection examples. 
 *  @author ingo, simon
 *  @version $Id: SelectionExampleReader.java,v 2.3 2003/04/25 10:29:34 fischer Exp $
 */
public class SelectionExampleReader implements ExampleReader {

    /** The parent exmaple reader. */
    private DataRowReader dataRowReader;

    /** The selection of the attributes. */
    private Attribute selectedAttributes[];
    
    private Attribute label, predictedLabel, weight, cluster, idAttribute;


    /** Creates the selection example reader. */
    public SelectionExampleReader(DataRowReader drr,
				  ExampleSet exampleSet) {
	this.dataRowReader      = drr;
	this.label              = exampleSet.getLabel();
	this.predictedLabel     = exampleSet.getPredictedLabel();
	this.weight             = exampleSet.getWeight();
	this.cluster            = exampleSet.getCluster();
	this.idAttribute        = exampleSet.getIdAttribute();

	this.selectedAttributes = new Attribute[exampleSet.getNumberOfAttributes()];
	for (int i = 0; i < selectedAttributes.length; i++)
	    selectedAttributes[i] = exampleSet.getAttribute(i);
    }

    public boolean hasNext() { 
	return dataRowReader.hasNext(); 
    }

    public Example next() {
	if (!hasNext()) return null;
	DataRow data = dataRowReader.next();
	if (data == null) return null;
	return new Example(data, selectedAttributes, label, predictedLabel, weight, cluster, idAttribute);
    }
}