jmysvmlearner.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.parameter.*;
import edu.udo.cs.yale.operator.performance.PerformanceVector;
import edu.udo.cs.yale.operator.performance.EstimatedPerformance;

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

import java.util.List;

/** This learner uses the Java implementation of the support vector machine <em>mySVM</em> 
 *  by Stefan R&uuml;ping. This learning method can be used for both regression and classification
 *  and provides a fast algorithm and good results for many learning tasks.
 *
 *  @yale.reference Rueping/2000a
 *  @yale.reference Vapnik/98a
 *  @yale.index SVM
 *  
 *  @version $Id: JMySVMLearner.java,v 1.3 2004/08/27 11:57:40 ingomierswa Exp $
 */
public class JMySVMLearner extends AbstractMySVMLearner {

    /** Indicates if the SVM is used for classification learning. */
    private boolean pattern = true;

    /** Returns true iff the learner can generate a performance vector during training. */
    public boolean canEstimatePerformance() { 
	return pattern && getParameterAsBoolean("xi_alpha"); 
    }
    
    /** Returns the estimated performances of this SVM. Does only work for classification tasks. */
    public PerformanceVector getEstimatedPerformance() {
	if (!pattern)
	    throw new RuntimeException("JMySVM-Learner: Cannot calculate leave one out estimation of error!");
	double[] estVector = ((SVMpattern)getSVM()).getXiAlphaEstimation(getKernel());
	PerformanceVector pv = new PerformanceVector();
	pv.addCriterion(new EstimatedPerformance("xialpha_error", estVector[0], 1, true));
	pv.addCriterion(new EstimatedPerformance("xialpha_precision", estVector[1], 1, false));
	pv.addCriterion(new EstimatedPerformance("xialpha_recall", estVector[2], 1, false));
	pv.setMainCriterionName("xialpha_error");
	return pv;
    }

    public AbstractMySVMModel createSVMModel(Attribute label, ExampleSet exampleSet, Kernel kernel, int kernelType) {
	return new JMySVMModel(label, exampleSet, kernel, kernelType);
    }
    
    public SVMInterface createSVM(Attribute label, Kernel kernel, ExampleSet exampleSet) {
	if (label.isNominal()) {
	    this.pattern = true;
	    return new SVMpattern(this, kernel, exampleSet);
	} else {
	    this.pattern = false;
	    return new SVMregression(this, kernel, exampleSet);
	}
    }
    
    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeDouble("L_pos", "A factor for the SVM complexity constant for positive examples", 0, 
					  Double.POSITIVE_INFINITY, 1.0d));
	types.add(new ParameterTypeDouble("L_neg", "A factor for the SVM complexity constant for negative examples", 0, 
					  Double.POSITIVE_INFINITY, 1.0d));
	types.add(new ParameterTypeDouble("epsilon", 
					  "Insensitivity constant. No loss if prediction lies this close to true value",
					  0.0d, Double.POSITIVE_INFINITY, 0.0d));
	types.add(new ParameterTypeDouble("epsilon+", "Epsilon for positive deviation only", 
					  0.0d, Double.POSITIVE_INFINITY, 0.0d));
	types.add(new ParameterTypeDouble("epsilon-", "Epsilon for negative deviation only", 
					  0.0d, Double.POSITIVE_INFINITY, 0.0d));
	types.add(new ParameterTypeBoolean("balance_cost", 
					   "Adapts Cpos and Cneg to the relative size of the classes", false));
	types.add(new ParameterTypeBoolean("quadratic_loss_pos", "Use quadratic loss for positive deviation", false));
	types.add(new ParameterTypeBoolean("quadratic_loss_neg", "Use quadratic loss for negative deviation", false));
	types.add(new ParameterTypeBoolean("xi_alpha", "Calculate xi alpha estimation for classification task.", false));
	return types;
    }
}