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.IntOption;
import com.yahoo.labs.samoa.moa.core.ObjectRepository;
import com.yahoo.labs.samoa.moa.tasks.TaskMonitor;

/**
 * Drift detection method based in DDM method of Joao Gama SBIA 2004.
 * 
 * <p>
 * João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295
 * </p>
 * 
 * @author Manuel Baena (mbaena@lcc.uma.es)
 * @version $Revision: 7 $
 */
public class DDM 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);
  private int m_n;

  private double m_p;

  private double m_s;

  private double m_psmin;

  private double m_pmin;

  private double m_smin;

  public DDM() {
    resetLearning();
  }

  @Override
  public void resetLearning() {
    m_n = 1;
    m_p = 1;
    m_s = 0;
    m_psmin = Double.MAX_VALUE;
    m_pmin = Double.MAX_VALUE;
    m_smin = Double.MAX_VALUE;
  }

  @Override
  public void input(double prediction) {
    // prediction must be 1 or 0
    // It monitors the error rate
    if (this.isChangeDetected) {
      resetLearning();
    }
    m_p = m_p + (prediction - m_p) / (double) m_n;
    m_s = Math.sqrt(m_p * (1 - m_p) / (double) m_n);

    m_n++;

    // 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_p + m_s <= m_psmin) {
      m_pmin = m_p;
      m_smin = m_s;
      m_psmin = m_p + m_s;
    }

    if (m_n > this.minNumInstancesOption.getValue() && m_p + m_s > m_pmin + 3 * m_smin) {
      this.isChangeDetected = true;
      // resetLearning();
    } else
      this.isWarningZone = m_p + m_s > m_pmin + 2 * m_smin;
  }

  @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
  }
}