wekaassociationlearner.java

一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2001-2004
 *      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.weka;

import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.operator.Operator;
import edu.udo.cs.yale.operator.IOObject;
import edu.udo.cs.yale.operator.ResultObjectAdapter;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.UserError;
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.Ontology;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.tools.WekaTools;

import weka.associations.Associator;
import weka.core.Instances;

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

/** This class builds a Weka Associator. The operator returns a result object
 *  containing the rules found by the association learner. In contrast to models
 *  generated by normal learners, the association rules cannot be applied to
 *  an example set. Hence, there is no way to evaluate the performance of association
 *  rules yet.
 *
 *  @yale.xmlclass WekaAssociator
 *  @version $Id: WekaAssociationLearner.java,v 1.2 2004/08/27 11:57:42 ingomierswa Exp $
 */
public class WekaAssociationLearner extends Operator {

    public static final String[] WEKA_CLASSIFIERS = WekaTools.getWekaClasses(weka.associations.Associator.class);

    public Class[] getInputClasses() { return new Class[] { ExampleSet.class }; }
    public Class[] getOutputClasses() { return new Class[] { WekaAssociator.class }; }

    public static class WekaAssociator extends ResultObjectAdapter {
	private Associator associator;
	public WekaAssociator(Associator associator) {
	    this.associator = associator;
	}
	public String toString() {
	    return associator.toString();
	}
    }

    public IOObject[] apply() throws OperatorException {
	ExampleSet exampleSet = (ExampleSet)getInput(ExampleSet.class);

	String operatorName = getParameterAsString("weka_associator_name");
	List wekaParameters = getParameterList("weka_parameters");
	String[] parameters = WekaTools.getWekaParameters(wekaParameters);
	Associator associator = null;
	try {
	    associator = Associator.forName(operatorName, parameters);
	} catch (Exception e) {
	    throw new UserError(this, e, 904, new Object[] { operatorName, e});
	}

	LogService.logMessage(getName() + ": Converting to Weka instances.", LogService.MINIMUM);
	Instances instances = WekaTools.toWekaInstances(exampleSet, "TempInstances", null, false);
	try {
	    LogService.logMessage(getName() + ": Building Weka associator.", LogService.MINIMUM);
	    associator.buildAssociations(instances);
	} catch (Exception e) {
	    throw new UserError(this, e, 905, new Object[] {operatorName, e});
	}
	return new IOObject[] { new WekaAssociator(associator) };
    }


    public List getParameterTypes() {
	List types = super.getParameterTypes();
	ParameterType type = new ParameterTypeStringCategory("weka_associator_name", "The fully qualified classname of the weka associator.", WEKA_CLASSIFIERS);
	type.setExpert(false);
	types.add(type);
	types.add(new ParameterTypeList("weka_parameters", "Parameters for the Weka associator as described in the Weka manual.", new ParameterTypeString(null, null)));
	return types;
    }
}