moa.classifiers.rules.functions.TargetMean.java Source code

Introduction

Here is the source code for moa.classifiers.rules.functions.TargetMean.java

Source

/*
 *    TargetMean.java
 *    Copyright (C) 2014 University of Porto, Portugal
 *    @author  J. Duarte, A. Bifet, J. Gama
 *
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 *    
 *    
 */
package moa.classifiers.rules.functions;

/**
 * TargetMean - Returns the mean of the target variable of the training instances
 * 
 * @author Joo Duarte 
 * 
 *  */
import weka.core.Instance;
import moa.classifiers.AbstractClassifier;
import moa.classifiers.Regressor;
import moa.core.Measurement;
import moa.core.StringUtils;
import moa.options.FloatOption;

public class TargetMean extends AbstractClassifier implements Regressor {

    /**
     * 
     */
    protected long n;
    protected double sum;
    protected double errorSum;
    protected double nError;
    private double fadingErrorFactor;

    private static final long serialVersionUID = 7152547322803559115L;

    public FloatOption fadingErrorFactorOption = new FloatOption("fadingErrorFactor", 'e',
            "Fading error factor for the TargetMean accumulated error", 0.99, 0, 1);

    @Override
    public boolean isRandomizable() {
        return false;
    }

    @Override
    public double[] getVotesForInstance(Instance inst) {
        double[] currentMean = new double[1];
        if (n > 0)
            currentMean[0] = sum / n;
        else
            currentMean[0] = 0;
        return currentMean;
    }

    @Override
    public void resetLearningImpl() {
        sum = 0;
        n = 0;
        errorSum = Double.MAX_VALUE;
        nError = 0;
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        updateAccumulatedError(inst);
        ++this.n;
        this.sum += inst.classValue();
    }

    protected void updateAccumulatedError(Instance inst) {
        double mean = 0;
        nError = 1 + fadingErrorFactor * nError;
        if (n > 0)
            mean = sum / n;
        errorSum = Math.abs(inst.classValue() - mean) + fadingErrorFactor * errorSum;
    }

    @Override
    protected Measurement[] getModelMeasurementsImpl() {
        return null;
    }

    @Override
    public void getModelDescription(StringBuilder out, int indent) {
        StringUtils.appendIndented(out, indent, "Current Mean: " + this.sum / this.n);
        StringUtils.appendNewline(out);

    }

    /* JD
     * Resets the learner but initializes with a starting point 
     * */
    public void reset(double currentMean, long numberOfInstances) {
        this.sum = currentMean * numberOfInstances;
        this.n = numberOfInstances;
        this.resetError();
    }

    /* JD
     * Resets the learner but initializes with a starting point 
     * */
    public double getCurrentError() {
        if (this.nError > 0)
            return this.errorSum / this.nError;
        else
            return Double.MAX_VALUE;
    }

    public TargetMean(TargetMean t) {
        super();
        this.n = t.n;
        this.sum = t.sum;
        this.errorSum = t.errorSum;
        this.nError = t.nError;
        this.fadingErrorFactor = t.fadingErrorFactor;
        this.fadingErrorFactorOption = t.fadingErrorFactorOption;
    }

    public TargetMean() {
        super();
        fadingErrorFactor = fadingErrorFactorOption.getValue();
    }

    public void resetError() {
        this.errorSum = 0;
        this.nError = 0;
    }

}