wekaclusterer.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.clusterer;

import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.operator.UserError;
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.operator.learner.Learner;
import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.tools.Ontology;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.tools.WekaTools;

import weka.core.Instances;

import java.util.List;
import java.util.Iterator;

/** This operator can build all clusterers from the 
 *  <a href="http://www.cs.waikato.ac.nz/~ml/weka/">Weka</a> package.
 *  The clusterer type can be selected by a parameter. See the weka javadoc
 *  for descriptions.<br/>
 *  This operator will create an additional special integer attribute named
 *  &quot;cluster&quot;. The values of this attribute specify the index of the
 *  cluster an example was assigned to.
 *
 *  @yale.xmlclass WekaClusterer
 *  @version $Id: WekaClusterer.java,v 2.6 2003/08/14 10:24:57 fischer Exp $
 */
public class WekaClusterer extends Clusterer {

    public static final String[] WEKA_CLUSTERERS = WekaTools.getWekaClasses(weka.clusterers.Clusterer.class, 
									    "weka.clusterers");



    public Model cluster(ExampleSet exampleSet) throws OperatorException {
	String operatorName = getParameterAsString("weka_clusterer_name");
	List wekaParameters = getParameterList("weka_parameters");

	String[] parameters = WekaTools.getWekaParameters(wekaParameters);
	weka.clusterers.Clusterer clusterer = null;
	try {
	    clusterer = weka.clusterers.Clusterer.forName(operatorName, parameters);
	} catch (Exception e) {
	    throw new UserError(this, e, 904, new Object[] { operatorName, e });
	}

	Instances instances = WekaTools.toWekaInstances(exampleSet, "TempInstances");
	WekaCluster wekaCluster = null;
	try {
	    clusterer.buildClusterer(instances);
	    wekaCluster = new WekaCluster(clusterer);
	    exampleSet.createClusterAttribute();
	    wekaCluster.apply(exampleSet);
	} catch (Exception e) {
	    throw new UserError(this, e, 905, new Object[] { operatorName, e });
	}
	return wekaCluster;
    }


    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeStringCategory("weka_clusterer_name", "The fully qualified classname of the weka clusterer.", WEKA_CLUSTERERS));	
	//types.add(new ParameterTypeString("weka_clusterer", "Fully qualified name of the Weka class to use for clustering.", null));
	types.add(new ParameterTypeList("weka_parameters", "Parameters for the Weka classifier as described in the Weka manual.", new ParameterTypeString(null, null)));
	return types;
    }
}