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

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

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/*
 *    FadingTargetMean.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;

import moa.options.FloatOption;
import weka.core.Instance;

public class FadingTargetMean extends TargetMean {

    /**
     * 
     */
    private static final long serialVersionUID = -1383391769242905972L;

    public FloatOption fadingFactorOption = new FloatOption("fadingFactor", 'f',
            "Fading factor for the FadingTargetMean accumulated error", 0.99, 0, 1);

    private double nD;
    private double fadingFactor;

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        updateAccumulatedError(inst);
        nD = 1 + fadingFactor * nD;
        sum = inst.classValue() + fadingFactor * sum;
    }

    @Override
    public void resetLearningImpl() {
        super.resetLearningImpl();
        this.fadingFactor = fadingFactorOption.getValue();
    }

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

}