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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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/*
 *    WaveformGeneratorDrift.java
 *    Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 *    @author Christophe Salperwyck
 *
 *    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.streams.generators;

import weka.core.DenseInstance;
import weka.core.Instance;

import moa.core.InstancesHeader;
import moa.core.ObjectRepository;
import moa.options.IntOption;
import moa.tasks.TaskMonitor;

/**
 * Stream generator for the problem of predicting one of three waveform types with drift.
 *
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class WaveformGeneratorDrift extends WaveformGenerator {

    private static final long serialVersionUID = 1L;

    public IntOption numberAttributesDriftOption = new IntOption("numberAttributesDrift",
            'd', "Number of attributes with drift.", 0, 0, TOTAL_ATTRIBUTES_INCLUDING_NOISE);

    protected int[] numberAttribute;

    @Override
    public String getPurposeString() {
        return "Generates a problem of predicting one of three waveform types with drift.";
    }

    @Override
    protected void prepareForUseImpl(TaskMonitor monitor,
            ObjectRepository repository) {
        super.prepareForUseImpl(monitor, repository);
        int numAtts = this.addNoiseOption.isSet() ? TOTAL_ATTRIBUTES_INCLUDING_NOISE
                : NUM_BASE_ATTRIBUTES;
        this.numberAttribute = new int[numAtts];
        for (int i = 0; i < numAtts; i++) {
            this.numberAttribute[i] = i;
        }
        //Change atributes
        int randomInt = this.instanceRandom.nextInt(numAtts);
        int offset = this.instanceRandom.nextInt(numAtts);
        int swap;
        for (int i = 0; i < this.numberAttributesDriftOption.getValue(); i++) {
            swap = this.numberAttribute[(i + randomInt) % numAtts];
            this.numberAttribute[(i + randomInt) % numAtts] = this.numberAttribute[(i + offset) % numAtts];
            this.numberAttribute[(i + offset) % numAtts] = swap;
        }
    }

    @Override
    public Instance nextInstance() {
        InstancesHeader header = getHeader();
        Instance inst = new DenseInstance(header.numAttributes());
        inst.setDataset(header);
        int waveform = this.instanceRandom.nextInt(NUM_CLASSES);
        int choiceA = 0, choiceB = 0;
        switch (waveform) {
            case 0:
                choiceA = 0;
                choiceB = 1;
                break;
            case 1:
                choiceA = 0;
                choiceB = 2;
                break;
            case 2:
                choiceA = 1;
                choiceB = 2;
                break;

        }
        double multiplierA = this.instanceRandom.nextDouble();
        double multiplierB = 1.0 - multiplierA;
        for (int i = 0; i < NUM_BASE_ATTRIBUTES; i++) {
            inst.setValue(this.numberAttribute[i], (multiplierA * hFunctions[choiceA][i])
                    + (multiplierB * hFunctions[choiceB][i])
                    + this.instanceRandom.nextGaussian());
        }
        if (this.addNoiseOption.isSet()) {
            for (int i = NUM_BASE_ATTRIBUTES; i < TOTAL_ATTRIBUTES_INCLUDING_NOISE; i++) {
                inst.setValue(this.numberAttribute[i], this.instanceRandom.nextGaussian());
            }
        }
        inst.setClassValue(waveform);
        return inst;
    }

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