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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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/*
 *    SDRSplitCriterionAMRules.java
 *    Copyright (C) 2014 University of Porto, Portugal
 *    @author A. Bifet, J. Duarte, 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.driftdetection;

import java.io.Serializable;

public class PageHinkleyTest implements Serializable {

    private static final long serialVersionUID = 1L;
    protected double cumulativeSum;
    
    public double getCumulativeSum() {
		return cumulativeSum;
	}

	public double getMinimumValue() {
		return minimumValue;
	}


	protected double minimumValue;
    protected double sumAbsolutError;
    protected long phinstancesSeen;
    protected double threshold;
    protected double alpha;

    public PageHinkleyTest(double threshold, double alpha) {
        this.threshold = threshold;
        this.alpha = alpha;
        this.reset();
    }

    public void reset() {
        this.cumulativeSum = 0.0;
        this.minimumValue = Double.MAX_VALUE;
        this.sumAbsolutError = 0.0;
        this.phinstancesSeen = 0;
    }

    //Compute Page-Hinkley test
    public boolean update(double error) {

        this.phinstancesSeen++;
        double absolutError = Math.abs(error);
        this.sumAbsolutError = this.sumAbsolutError + absolutError;
        if (this.phinstancesSeen > 30) {
        	double mT = absolutError - (this.sumAbsolutError / this.phinstancesSeen) - this.alpha;
        	this.cumulativeSum = this.cumulativeSum + mT; // Update the cumulative mT sum
        	if (this.cumulativeSum < this.minimumValue) { // Update the minimum mT value if the new mT is smaller than the current minimum
        		this.minimumValue = this.cumulativeSum;
        	}
        	return (((this.cumulativeSum - this.minimumValue) > this.threshold));
        }
        return false;
    }

}