averagemodel.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.lazy;

import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.ExampleReader;

import java.io.ObjectOutputStream;
import java.util.Iterator;

/** Average model simply calculates the average of the attributes as prediction. For classification problems 
 *  the class value closest to the prediction is returned. 
 *
 *  @version $Id: AverageModel.java,v 1.2 2004/08/27 11:57:40 ingomierswa Exp $
 */
public class AverageModel extends Model {

    public AverageModel(Attribute label) {
	super(label);
    }

    public void writeData(ObjectOutputStream out) {}

    public void apply(ExampleSet exampleSet) {
	ExampleReader reader = exampleSet.getExampleReader();
	while (reader.hasNext()) {
	    Example example = reader.next();

	    // calc average
	    double average = 0.0d;
	    for (int i = 0; i < example.getNumberOfAttributes(); i++)
		average += example.getValue(example.getAttribute(i));
	    average /= example.getNumberOfAttributes();

	    // set prediction for regression and determine class for classification tasks
	    if (exampleSet.getPredictedLabel().isNominal()) {
		Attribute label = exampleSet.getPredictedLabel();
		double minDistance = Double.POSITIVE_INFINITY;
		int prediction = -1;
		Iterator i = label.getValues().iterator();
		while (i.hasNext()) {
		    String classValue = (String)i.next();
		    int classIndex = label.mapString(classValue);
		    double distance = Math.abs((double)classIndex - average);
		    if (distance < minDistance) {
			minDistance = distance;
			prediction = classIndex;
		    }
		}
		example.setPredictedLabel(prediction);
	    } else {
		example.setPredictedLabel(average);
	    }
	}
    }
}