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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.

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/*
 *    WindowClassificationPerformanceEvaluator.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet ([email protected])
 *
 *    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.evaluation;

import moa.core.Example;
import moa.core.Measurement;
import moa.core.ObjectRepository;
import moa.options.AbstractOptionHandler;

import com.github.javacliparser.IntOption;

import moa.tasks.TaskMonitor;
import moa.core.Utils;

import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.instances.InstanceData;
import com.yahoo.labs.samoa.instances.Prediction;

import java.util.LinkedList;

/**
 * Classification evaluator that updates evaluation results using a sliding
 * window.
 *
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @author Jean Paul Barddal ([email protected])
 * @version $Revision: 8 $
 *
 *
 */
public class WindowClassificationPerformanceEvaluator extends BasicClassificationPerformanceEvaluator {

    private static final long serialVersionUID = 1L;

    public IntOption widthOption = new IntOption("width",
            'w', "Size of Window", 1000);

    @Override
    protected Estimator newEstimator() {
        return new WindowEstimator(this.widthOption.getValue());
    }

    public class WindowEstimator implements Estimator {

        protected double[] window;

        protected int posWindow;

        protected int lenWindow;

        protected int SizeWindow;

        protected double sum;

        protected double qtyNaNs;

        public WindowEstimator(int sizeWindow) {
            window = new double[sizeWindow];
            SizeWindow = sizeWindow;
            posWindow = 0;
            lenWindow = 0;
        }

        public void add(double value) {
            double forget = window[posWindow];
            if(!Double.isNaN(forget)){
                sum -= forget;
            }else qtyNaNs--;
            if(!Double.isNaN(value)) {
                sum += value;
            }else qtyNaNs++;
            window[posWindow] = value;
            posWindow++;
            if (posWindow == SizeWindow) {
                posWindow = 0;
            }
            if (lenWindow < SizeWindow) {
                lenWindow++;
            }
        }

        public double estimation(){
            return sum / (lenWindow - qtyNaNs);
        }

    }

}