wekamodel.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.operator.learner;

import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.tools.WekaTools;
import edu.udo.cs.yale.operator.OperatorException;

import weka.core.Instance;
import weka.core.Instances;

/** Abstract Superclass for all Weka models. Subclasses are 
 *  {@link edu.udo.cs.yale.operator.clusterer.WekaCluster}
 *  and {@link WekaClassifier}, which can cluster or classify a weka instance 
 *  and set the result as the predicted label of single {@link Example}s.
 *
 *  @author ingo
 *  @version $Id: WekaModel.java,v 2.3 2003/05/06 20:02:10 fischer Exp $
 */
public abstract class WekaModel extends SerializableModel {

    public void apply(ExampleSet exampleSet) throws OperatorException {
	LogService.logMessage("Converting to Weka instances.", LogService.MINIMUM);
	Instances instances = WekaTools.toWekaInstances(exampleSet, "ApplierInstances");
	LogService.logMessage("Applying Weka classifier.", LogService.MINIMUM);
	int i = 0;
	ExampleReader r = exampleSet.getExampleReader();
	while (r.hasNext()) {
	    Example e = r.next();
	    Instance instance = instances.instance(i++);
	    applyModelForInstance(instance, e);
	}
    }

    /** Applies the model for a single instance. The result should be set to the example.
     */
    public abstract void applyModelForInstance(Instance instance, Example e) throws OperatorException;
}