myklrlearner.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.Attribute;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.UserError;

import edu.udo.cs.myKLR.KLR;
import edu.udo.cs.mySVM.SVM.SVMInterface;
import edu.udo.cs.mySVM.Kernel.Kernel;
import edu.udo.cs.mySVM.Examples.ExampleSet;

/** This is the Java implementation of <em>myKLR</em> by Stefan R&uuml;ping. myKLR is a tool for 
 *  large scale kernel logistic regression based on the algorithm of Keerthi/etal/2003 and the 
 *  code of mySVM.
 *
 *  @yale.index KLR
 *  
 *  @version $Id: MyKLRLearner.java,v 1.2 2004/08/27 11:57:40 ingomierswa Exp $
 */
public class MyKLRLearner extends AbstractMySVMLearner {

    public AbstractMySVMModel createSVMModel(Attribute label, ExampleSet exampleSet, Kernel kernel, int kernelType) {
	//System.out.println("MODEL:\n" + exampleSet);
	return new MyKLRModel(label, exampleSet, kernel, kernelType);
    }

    public SVMInterface createSVM(Attribute label, Kernel kernel, ExampleSet exampleSet) throws OperatorException {
	if (!label.isNominal())
	    throw new UserError(this, 101, new Object[] {"MyKLR", label.getName() });
	return new KLR(this);
    }
}