learner.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.OperatorException;
import edu.udo.cs.yale.operator.performance.PerformanceVector;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.AttributeWeights;

/**
 *  A <tt>Learner</tt> is an operator that encapsulates the learning step of a machine learning 
 *  method. Some Learners may be capable of estimating the performance of the generated model.
 *  In that case, they additionally return a 
 *  {@link edu.udo.cs.yale.operator.performance.PerformanceVector}.
 *  Furthermore some learner can calculate weights of the used attributes which can also be delivered.
 *
 *  @author Ingo
 *  @version $Id: Learner.java,v 2.9 2004/08/27 11:57:38 ingomierswa Exp $
 */
public interface Learner {

    /** Trains a model. This method should be called by apply() and is implemented by subclasses. */
    public Model learn(ExampleSet exampleSet) throws OperatorException;

    /** Returns the name of the learner. */
    public String getName();

    /** Returns true iff the learner can generate a performance vector during training. */
    public boolean canEstimatePerformance();
    
    /** Returns the estimated performance. */
    public PerformanceVector getEstimatedPerformance();

    /** Returns true iff the learner can generate a attribute weight vector. */
    public boolean canCalculateWeights();

    /** Returns the calculated weight vectors. */
    public AttributeWeights getWeights(ExampleSet exampleSet);

}