parametersetter.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;

import edu.udo.cs.yale.operator.parameter.*;
import java.util.List;
import java.util.Iterator;
import java.util.Map;
import java.util.HashMap;

/** Sets a set of parameters. These parameters can either be generated by a
 *  {@link ParameterOptimizationOperator} or read by a {@link edu.udo.cs.yale.operator.io.ParameterSetLoader}. 
 *  This operator is useful, e.g. in the following scenario. If one wants to 
 *  find the best parameters for a certain learning scheme, one usually is also interested 
 *  in the model generated with this parameters. While the first is easily possible using a
 *  {@link ParameterOptimizationOperator}, the latter is not possible because the
 *  {@link ParameterOptimizationOperator} does not return the IOObjects produced
 *  within, but only a parameter set. This is, because the parameter optimization 
 *  operator knows nothing about models, but only about the performance vectors
 *  produced within. Producing performance vectors does not necessarily require a 
 *  model.
 *  <br/> 
 *  To solve this problem, one can use a <code>ParameterSetter</code>.
 *  Usually, an experiment with a <code>ParameterSetter</code> contains at least 
 *  two operators of the same type, typically a learner. One learner may be 
 *  an inner operator of the {@link ParameterOptimizationOperator} and may be 
 *  named &quot;Learner&quot;, whereas a second learner of the same type 
 *  named &quot;OptimalLearner&quot; follows the parameter optimization and
 *  should use the optimal parameter set found by the optimization.
 *  In order to make the <code>ParameterSetter</code> set the optimal 
 *  parameters of the right operator, one must specify its name.
 *  Therefore, the parameter list <var>name_map</var> was introduced. Each parameter
 *  in this list maps the name of an operator that was used during optimization
 *  (in our case this is &quot;Learner&quot;) to an operator that should now use
 *  these parameters (in our case this is &quot;OptimalLearner&quot;).
 *
 *  @version $Id: ParameterSetter.java,v 2.7 2004/08/27 11:57:34 ingomierswa Exp $
 */
public class ParameterSetter extends Operator {

    private static final Class[] INPUT_CLASSES = new Class[] { ParameterSet.class };

    public Class[] getInputClasses() { return INPUT_CLASSES; }
    public Class[] getOutputClasses() { return new Class[0]; }

    public IOObject[] apply() throws OperatorException {
	ParameterSet parameterSet = (ParameterSet)getInput(ParameterSet.class);

	Map nameMap = new HashMap();
	List nameList = getParameterList("name_map");
	Iterator i = nameList.iterator();
	while (i.hasNext()) {
	    Object[] keyValue = (Object[])i.next();
	    nameMap.put(keyValue[0], keyValue[1]);
	}
	parameterSet.applyAll(getExperiment(), nameMap);
	
	return new IOObject[0];
    }

    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeList("name_map", "A list mapping operator names from the set to operator names in the experiment.",
					new ParameterTypeString("operator_name", "The keys are the operator names in the parameter set, the values are names of the operators in the experiment.")));
	return types;
    }


}