modelapplier.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;

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

import java.util.*;

/** This operator applies a {@link Model} generated by a subclass of {@link Learner}.
 *  All parameters of the training process are stored within the model. Hence, this
 *  operator does not take any parameters. Models can also be read from a file
 *  by using a {@link edu.udo.cs.yale.operator.io.ModelLoader}.
 *
 *  @yale.xmlclass ModelApplier
 *  @author ingo
 *  @version $Id: ModelApplier.java,v 2.14 2004/09/09 12:00:53 ingomierswa Exp $
 */
public class ModelApplier extends Operator {

    private static final Class[] INPUT_CLASSES = { Model.class, ExampleSet.class };
    private static final Class[] OUTPUT_CLASSES = { ExampleSet.class };

    /** Applies the operator and labels the {@link ExampleSet}. The example set in the input
     *  is not consumed. */
    public IOObject[] apply() throws OperatorException {
	ExampleSet inputExampleSet = (ExampleSet)getInput(ExampleSet.class, false);
	Model model = (Model)getInput(Model.class);
	
	LogService.logMessage("Applying " + model.getClass().getName(), LogService.MINIMUM);
  	Attribute predictedLabel = model.createPredictedLabel(inputExampleSet);
	model.apply(inputExampleSet);

	inputExampleSet.recalculateAttributeStatistics(predictedLabel);
	
	return new IOObject[0];
    }


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