learningcurveoperator.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.validation;

import edu.udo.cs.yale.operator.OperatorChain;
import edu.udo.cs.yale.operator.IOObject;
import edu.udo.cs.yale.operator.IOContainer;
import edu.udo.cs.yale.operator.IODescription;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.IllegalInputException;
import edu.udo.cs.yale.operator.Value;
import edu.udo.cs.yale.operator.performance.PerformanceVector;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.SplittedExampleSet;

import java.util.List;


/** Simple sampling operator.
 *
 *  @version $Id: LearningCurveOperator.java,v 1.4 2004/09/12 11:09:52 ingomierswa Exp $
 */
public class LearningCurveOperator extends OperatorChain {

    private double lastFraction    = Double.NaN;
    private double lastPerformance = Double.NaN;
    private double lastDeviation   = Double.NaN;

    public LearningCurveOperator() {
	addValue(new Value("fraction", "The used fraction of data..") {
		public double getValue() {
		    return lastFraction;
		}
	    });
	addValue(new Value("performance", "The last performance (main criterion).") {
		public double getValue() {
		    return lastPerformance;
		}
	    });
	addValue(new Value("deviation", "The variance of the last performance (main criterion).") {
		public double getValue() {
		    return lastDeviation;
		}
	    });
    }

    public IOObject[] apply() throws OperatorException {
	ExampleSet exampleSet = (ExampleSet)getInput(ExampleSet.class);
	double stepFraction = getParameterAsDouble("step_fraction");
	this.lastFraction = stepFraction; 
	while (lastFraction <= 1.0d) {
	    SplittedExampleSet splitted = new SplittedExampleSet(exampleSet, lastFraction);
	    splitted.selectSingleSubset(0);
	    splitted.recalculateAllAttributeStatistics();
	    IOContainer result = getOperator(0).apply(new IOContainer(new IOObject[] { splitted }));
	    PerformanceVector performance = (PerformanceVector)result.getInput(PerformanceVector.class);
	    this.lastPerformance = performance.getMainCriterion().getValue();
	    this.lastDeviation   = performance.getMainCriterion().getStandardDeviation();
	    this.lastFraction    += stepFraction;
	}
	return new IOObject[0];
    }


    public Class[] getInputClasses() {
	return new Class[] { ExampleSet.class };
    }

    public Class[] getOutputClasses() {
	return new Class[0];
    }

    public int getNumberOfSteps() {
	return (int)(1.0d / getParameterAsDouble("step_fraction")) * getNumberOfChildrensSteps();
    }

    public int getMinNumberOfInnerOperators() { return 1; }
    public int getMaxNumberOfInnerOperators() { return 1; }

    public Class[] checkIO(Class[] input) throws IllegalInputException {
	input = getOperator(0).checkIO(input);
	if (!IODescription.containsClass(PerformanceVector.class, input))
	    throw new IllegalInputException(this, getOperator(0), PerformanceVector.class);
	return getOutputClasses();
    }

    public List getParameterTypes() {
	List types = super.getParameterTypes();
	ParameterType type = new ParameterTypeDouble("step_fraction", "The fraction of examples which would be additionally used in each step.", 0.0d, 1.0d, 0.05);
	type.setExpert(false);
	types.add(type);
	
	return types;
    }
}