predictionaverage.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      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.performance;

import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;

/** Returns the average value of the prediction. This criterion can be used
 *  to detect whether a learning scheme predicts nonsense, e.g. always the
 *  same error. This criterion is not suitable for evaluating the performance
 *  and should never be used as main criterion. 
 *  The {@link #getFitness()} method alsways returns 0.
 *
 *  @version $Id: PredictionAverage.java,v 2.1 2003/07/10 10:40:25 fischer Exp $
 */
public class PredictionAverage extends MeasuredPerformance {

    private double sum;
    private double squaredSum;
    private int count;
    
    public void countExample(Example example) {
	count++;
	double v = example.getPredictedLabel();
	if (!Double.isNaN(v)) {
	    sum += v;
	    squaredSum += v*v;
	}
    }

    public double getValue() {
	return sum / count;
    }

    public double getVariance() {
	double avg = getValue();
	return (squaredSum / count) - avg*avg;
    }

    public void startCounting(ExampleSet set) {
	count = 0;
	sum   = 0.0;
    }


    public String getName() {
	return "prediction";
    }

    /** Returns 0. */
    public double getFitness() { return 0.0; }


    void clonePerformanceCriterion(PerformanceCriterion newPC) {
	super.clonePerformanceCriterion(newPC);
	PredictionAverage pa = (PredictionAverage)newPC;
	this.sum          = pa.sum;
	this.squaredSum   = pa.squaredSum;
	this.count        = pa.count;
    }

    public void buildAverage(PerformanceCriterion performance) {
	super.buildAverage(performance);
	PredictionAverage other = (PredictionAverage)performance;
	this.sum          += other.sum;
	this.squaredSum   += other.squaredSum;
	this.count        += other.count;
    }

}