| package com.yahoo.labs.samoa.moa.classifiers.core.driftdetection; |
| |
| /* |
| * #%L |
| * SAMOA |
| * %% |
| * Copyright (C) 2013 University of Waikato, Hamilton, New Zealand |
| * %% |
| * 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. |
| * #L% |
| */ |
| |
| import com.github.javacliparser.FloatOption; |
| import com.github.javacliparser.IntOption; |
| import com.yahoo.labs.samoa.moa.core.ObjectRepository; |
| import com.yahoo.labs.samoa.moa.tasks.TaskMonitor; |
| |
| /** |
| * Drift detection method based in EWMA Charts of Ross, Adams, Tasoulis and Hand 2012 |
| * |
| * |
| * @author Manuel Baena (mbaena@lcc.uma.es) |
| * @version $Revision: 7 $ |
| */ |
| public class EWMAChartDM extends AbstractChangeDetector { |
| |
| private static final long serialVersionUID = -3518369648142099719L; |
| |
| // private static final int DDM_MIN_NUM_INST = 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) { |
| resetLearning(); |
| } |
| |
| 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 = 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 |
| 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) { |
| this.isChangeDetected = true; |
| // resetLearning(); |
| } else { |
| this.isWarningZone = z_t > m_p + 0.5 * L_t * m_s; |
| } |
| } |
| |
| @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 |
| } |
| } |