moa.classifiers.core.driftdetection.ADWINChangeDetector Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of moa Show documentation

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

There is a newer version: 2024.07.0

Show newest version

/*
 *    ADWINChangeDetector.java
 *    Copyright (C) 2008 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 .
 */
package moa.classifiers.core.driftdetection;

import com.github.javacliparser.FloatOption;
import moa.core.ObjectRepository;
import moa.tasks.TaskMonitor;

/**
 * Drift detection method based in ADWIN. ADaptive sliding WINdow is a change
 * detector and estimator. It keeps a variable-length window of recently seen
 * items, with the property that the window has the maximal length statistically
 * consistent with the hypothesis "there has been no change in the average value
 * inside the window".
 *
 *
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class ADWINChangeDetector extends AbstractChangeDetector {

    protected ADWIN adwin;

    public FloatOption deltaAdwinOption = new FloatOption("deltaAdwin", 'a',
            "Delta of Adwin change detection", 0.002, 0.0, 1.0);

    @Override
    public void input(double inputValue) {
        if (this.adwin == null) {
            resetLearning();
        }
        double ErrEstim = this.adwin.getEstimation();
        if(adwin.setInput(inputValue)) {
            if (this.adwin.getEstimation() > ErrEstim) {
                this.isChangeDetected = true;
            }
        }
        this.isWarningZone = false;
        this.delay = 0.0;
        this.estimation = adwin.getEstimation();
    }

    @Override
    public void resetLearning() {
        adwin = new ADWIN((double) this.deltaAdwinOption.getValue());
        super.resetLearning();
    }

    @Override
    public void getDescription(StringBuilder sb, int indent) {
        // TODO Auto-generated method stub
    }

    @Override
    protected void prepareForUseImpl(TaskMonitor monitor,
            ObjectRepository repository) {
        // TODO Auto-generated method stub
    }
}