package com.yahoo.labs.samoa.moa.classifiers.core.driftdetection;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * 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
  }
}