moa.evaluation.BasicRegressionPerformanceEvaluator.java Source code

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Here is the source code for moa.evaluation.BasicRegressionPerformanceEvaluator.java

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/*
 *    BasicRegressionPerformanceEvaluator.java
 *    Copyright (C) 2011 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 *
 *    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 3 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, see <http://www.gnu.org/licenses/>.
 *    
 */
package moa.evaluation;

import moa.AbstractMOAObject;
import moa.core.Measurement;
import weka.core.Instance;

/**
 * Regression evaluator that performs basic incremental evaluation.
 *
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class BasicRegressionPerformanceEvaluator extends AbstractMOAObject
        implements RegressionPerformanceEvaluator {

    private static final long serialVersionUID = 1L;

    protected double weightObserved;

    protected double squareError;

    protected double averageError;

    @Override
    public void reset() {
        this.weightObserved = 0.0;
        this.squareError = 0.0;
        this.averageError = 0.0;
    }

    @Override
    public void addResult(Instance inst, double[] prediction) {
        if (inst.weight() > 0.0) {
            this.weightObserved += inst.weight();
            if (prediction.length > 0) {
                this.squareError += (inst.classValue() - prediction[0]) * (inst.classValue() - prediction[0]);
                this.averageError += Math.abs(inst.classValue() - prediction[0]);
            }
        }
    }

    @Override
    public Measurement[] getPerformanceMeasurements() {
        return new Measurement[] { new Measurement("classified instances", getTotalWeightObserved()),
                new Measurement("mean absolute error", getMeanError()),
                new Measurement("root mean squared error", getSquareError()) };
    }

    public double getTotalWeightObserved() {
        return this.weightObserved;
    }

    public double getMeanError() {
        return this.weightObserved > 0.0 ? this.averageError / this.weightObserved : 0.0;
    }

    public double getSquareError() {
        return Math.sqrt(this.weightObserved > 0.0 ? this.squareError / this.weightObserved : 0.0);
    }

    @Override
    public void getDescription(StringBuilder sb, int indent) {
        Measurement.getMeasurementsDescription(getPerformanceMeasurements(), sb, indent);
    }
}