package com.yahoo.labs.samoa.moa.cluster;

/*
 * #%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.
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 */
import java.util.Arrays;
import com.yahoo.labs.samoa.instances.Instance;

/* micro cluster, as defined by Aggarwal et al, On Clustering Massive Data Streams: A Summarization Praradigm 
 * in the book Data streams : models and algorithms, by Charu C Aggarwal
 *  @article{
 title = {Data Streams: Models and Algorithms},
 author = {Aggarwal, Charu C.},
 year = {2007},
 publisher = {Springer Science+Business Media, LLC},
 url = {http://ebooks.ulb.tu-darmstadt.de/11157/},
 institution = {eBooks [http://ebooks.ulb.tu-darmstadt.de/perl/oai2] (Germany)},
 }

 DEFINITION A micro-clusterfor a set of d-dimensionalpoints Xi,. .Xi,
 with t i m e s t a m p s ~. . .T,, is the (2-d+3)tuple (CF2", CFlX CF2t, CFlt, n),
 wherein CF2" and CFlX each correspond to a vector of d entries. The definition of each of these entries is as follows:

 o For each dimension, the sum of the squares of the data values is maintained
 in CF2". Thus, CF2" contains d values. The p-th entry of CF2" is equal to
 \sum_j=1^n(x_i_j)^2

 o For each dimension, the sum of the data values is maintained in C F l X .
 Thus, CFIX contains d values. The p-th entry of CFIX is equal to
 \sum_j=1^n x_i_j

 o The sum of the squares of the time stamps Ti,. .Tin maintained in CF2t

 o The sum of the time stamps Ti, . . .Tin maintained in CFlt.

 o The number of data points is maintained in n.

 */
public abstract class CFCluster extends SphereCluster {

  private static final long serialVersionUID = 1L;

  protected double radiusFactor = 1.8;

  /**
   * Number of points in the cluster.
   */
  protected double N;
  /**
   * Linear sum of all the points added to the cluster.
   */
  public double[] LS;
  /**
   * Squared sum of all the points added to the cluster.
   */
  public double[] SS;

  /**
   * Instantiates an empty kernel with the given dimensionality.
   * 
   * @param dimensions
   *          The number of dimensions of the points that can be in this kernel.
   */
  public CFCluster(Instance instance, int dimensions) {
    this(instance.toDoubleArray(), dimensions);
  }

  protected CFCluster(int dimensions) {
    this.N = 0;
    this.LS = new double[dimensions];
    this.SS = new double[dimensions];
    Arrays.fill(this.LS, 0.0);
    Arrays.fill(this.SS, 0.0);
  }

  public CFCluster(double[] center, int dimensions) {
    this.N = 1;
    this.LS = center;
    this.SS = new double[dimensions];
    for (int i = 0; i < SS.length; i++) {
      SS[i] = Math.pow(center[i], 2);
    }
  }

  public CFCluster(CFCluster cluster) {
    this.N = cluster.N;
    this.LS = Arrays.copyOf(cluster.LS, cluster.LS.length);
    this.SS = Arrays.copyOf(cluster.SS, cluster.SS.length);
  }

  public void add(CFCluster cluster) {
    this.N += cluster.N;
    addVectors(this.LS, cluster.LS);
    addVectors(this.SS, cluster.SS);
  }

  public abstract CFCluster getCF();

  /**
   * @return this kernels' center
   */
  @Override
  public double[] getCenter() {
    assert (this.N > 0);
    double res[] = new double[this.LS.length];
    for (int i = 0; i < res.length; i++) {
      res[i] = this.LS[i] / N;
    }
    return res;
  }

  @Override
  public abstract double getInclusionProbability(Instance instance);

  /**
   * See interface <code>Cluster</code>
   * 
   * @return The radius of the cluster.
   */
  @Override
  public abstract double getRadius();

  /**
   * See interface <code>Cluster</code>
   * 
   * @return The weight.
   * @see Cluster#getWeight()
   */
  @Override
  public double getWeight() {
    return N;
  }

  public void setN(double N) {
    this.N = N;
  }

  public double getN() {
    return N;
  }

  /**
   * Adds the second array to the first array element by element. The arrays must have the same length.
   * 
   * @param a1
   *          Vector to which the second vector is added.
   * @param a2
   *          Vector to be added. This vector does not change.
   */
  public static void addVectors(double[] a1, double[] a2) {
    assert (a1 != null);
    assert (a2 != null);
    assert (a1.length == a2.length) : "Adding two arrays of different "
        + "length";

    for (int i = 0; i < a1.length; i++) {
      a1[i] += a2[i];
    }
  }
}