jmysvmmodel.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.kernel;

import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.operator.OperatorException;

import edu.udo.cs.mySVM.SVM.*;
import edu.udo.cs.mySVM.Kernel.*;

import java.io.ObjectOutputStream;
import java.io.ObjectInputStream;

/** The implementation for the mySVM model (Java version) by Stefan Rueping. 
 *
 *  @version $Id: JMySVMModel.java,v 1.3 2004/08/27 11:57:40 ingomierswa Exp $
 */
public class JMySVMModel extends AbstractMySVMModel {

    public JMySVMModel(Attribute labelAttribute) {
	super(labelAttribute);
    }

    public JMySVMModel(Attribute labelAttribute, 
		       edu.udo.cs.mySVM.Examples.ExampleSet model, 
		       Kernel kernel, 
		       int kernelType) {
	super(labelAttribute, model, kernel, kernelType);
    }

    public SVMInterface createSVM() {
	if (getLabel().isNominal()) return new SVMpattern();
	else return new SVMregression();
    }

    public void setPrediction(Example example, double prediction) {
	Attribute predLabel = example.getPredictedLabelAttribute();
	if (predLabel.isNominal()) {
	    example.setPredictedLabel(prediction > 0 ? predLabel.getPositiveIndex() : predLabel.getNegativeIndex());
	} else {
	    example.setPredictedLabel(prediction);
	}
    }
}