
moa.classifiers.core.driftdetection.EWMAChartDM Maven / Gradle / Ivy
/*
* EWMAChartDM.java
* Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
* @author Manuel Baena ([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.classifiers.core.driftdetection;
import com.github.javacliparser.FloatOption;
import com.github.javacliparser.IntOption;
import moa.core.ObjectRepository;
import moa.tasks.TaskMonitor;
/**
* Drift detection method based in EWMA Charts of Ross, Adams, Tasoulis and Hand
* 2012
*
*
* @author Manuel Baena ([email protected])
* @version $Revision: 7 $
*/
public class EWMAChartDM extends AbstractChangeDetector {
private static final long serialVersionUID = -3518369648142099719L;
//private static final int DDM_MINNUMINST = 30;
public IntOption minNumInstancesOption = new IntOption(
"minNumInstances",
'n',
"The minimum number of instances before permitting detecting change.",
30, 0, Integer.MAX_VALUE);
public FloatOption lambdaOption = new FloatOption("lambda", 'l',
"Lambda parameter of the EWMA Chart Method", 0.2, 0.0, Float.MAX_VALUE);
private double m_n;
private double m_sum;
private double m_p;
private double m_s;
private double lambda;
private double z_t;
public EWMAChartDM() {
resetLearning();
}
@Override
public void resetLearning() {
m_n = 1.0;
m_sum = 0.0;
m_p = 0.0;
m_s = 0.0;
z_t = 0.0;
lambda = this.lambdaOption.getValue();
}
@Override
public void input(double prediction) {
// prediction must be 1 or 0
// It monitors the error rate
if (this.isChangeDetected == true || this.isInitialized == false) {
resetLearning();
this.isInitialized = true;
}
m_sum += prediction;
m_p = m_sum/m_n; // m_p + (prediction - m_p) / (double) (m_n+1);
m_s = Math.sqrt( m_p * (1.0 - m_p)* lambda * (1.0 - Math.pow(1.0 - lambda, 2.0 * m_n)) / (2.0 - lambda));
m_n++;
z_t += lambda * (prediction - z_t);
//double L_t = 2.76 - 6.23 * m_p + 18.12 * Math.pow(m_p, 3) - 312.45 * Math.pow(m_p, 5) + 1002.18 * Math.pow(m_p, 7); //%1 FP
double L_t = 3.97 - 6.56 * m_p + 48.73 * Math.pow(m_p, 3) - 330.13 * Math.pow(m_p, 5) + 848.18 * Math.pow(m_p, 7); //%1 FP
//double L_t = 1.17 + 7.56 * m_p - 21.24 * Math.pow(m_p, 3) + 112.12 * Math.pow(m_p, 5) - 987.23 * Math.pow(m_p, 7); //%1 FP
// System.out.print(prediction + " " + m_n + " " + (m_p+m_s) + " ");
this.estimation = m_p;
this.isChangeDetected = false;
this.isWarningZone = false;
this.delay = 0;
if (m_n < this.minNumInstancesOption.getValue()) {
return;
}
if (m_n > this.minNumInstancesOption.getValue() && z_t > m_p + L_t * m_s) {
//System.out.println(m_p + ",D");
this.isChangeDetected = true;
//resetLearning();
} else if (z_t > m_p + 0.5 * L_t * m_s) {
//System.out.println(m_p + ",W");
this.isWarningZone = true;
} else {
this.isWarningZone = false;
//System.out.println(m_p + ",N");
}
}
@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
}
}
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