package de.lmu.ifi.dbs.elki.math.statistics.dependence;

/*
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures

 Copyright (C) 2015
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team

 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero 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 Affero General Public License for more details.

 You should have received a copy of the GNU Affero General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberArrayAdapter;
import de.lmu.ifi.dbs.elki.utilities.datastructures.heap.DoubleMinHeap;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Compute the similarity of dimensions using the SURFING score. The parameter k
 * for the k nearest neighbors is currently hard-coded to 10% of the set size.
 *
 * Note that the complexity is roughly O(n n k), so this is a rather slow
 * method, and with k at 10% of n, is actually cubic: O(0.1 * n^3).
 *
 * This version cannot use index support, as the API operates without database
 * attachment. However, it should be possible to implement some trivial
 * sorted-list indexes to get a reasonable speedup!
 *
 * Reference:
 * <p>
 * Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:<br />
 * Interactive Data Mining with 3D-Parallel-Coordinate-Trees.<br />
 * Proceedings of the 2013 ACM International Conference on Management of Data
 * (SIGMOD), New York City, NY, 2013.
 * </p>
 *
 * Based on:
 * <p>
 * Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and
 * Peer Kröger<br />
 * Subspace Selection for Clustering High-Dimensional Data<br />
 * In IEEE International Conference on Data Mining, 2004.
 * </p>
 *
 * TODO: make the subspace distance function and k parameterizable.
 *
 * @author Robert Rödler
 * @author Erich Schubert
 */
@Reference(authors = "Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek", //
title = "Interactive Data Mining with 3D-Parallel-Coordinate-Trees", //
booktitle = "Proc. of the 2013 ACM International Conference on Management of Data (SIGMOD)", //
url = "http://dx.doi.org/10.1145/2463676.2463696")
public class SURFINGDependenceMeasure extends AbstractDependenceMeasure {
  /**
   * Static instance.
   */
  public static final SURFINGDependenceMeasure STATIC = new SURFINGDependenceMeasure();

  /**
   * Constructor. Use static instance instead!
   */
  protected SURFINGDependenceMeasure() {
    super();
  }

  @Reference(authors = "Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and Peer Kröger", //
  title = "Subspace Selection for Clustering High-Dimensional Data", //
  booktitle = "IEEE International Conference on Data Mining, 2004", //
  url = "http://dx.doi.org/10.1109/ICDM.2004.10112")
  @Override
  public <A, B> double dependence(NumberArrayAdapter<?, A> adapter1, A data1, NumberArrayAdapter<?, B> adapter2, B data2) {
    final int len = size(adapter1, data1, adapter2, data2);
    final int k = Math.max(1, len / 10);

    double[] knns = new double[len];

    DoubleMinHeap heap = new DoubleMinHeap(k);
    double kdistmean = 0.;
    for(int i = 0; i < len; ++i) {
      double ix = adapter1.getDouble(data1, i), iy = adapter2.getDouble(data2, i);
      heap.clear();
      for(int j = 0; j < len; ++j) {
        double jx = adapter1.getDouble(data1, j), jy = adapter2.getDouble(data2, j);
        double dx = ix - jx, dy = iy - jy;
        heap.add(dx * dx + dy * dy); // Squared Euclidean.
      }
      double kdist = Math.sqrt(heap.peek()); // Euclidean
      knns[i] = kdist;
      kdistmean += kdist;
    }
    kdistmean /= len;
    // Deviation from mean:
    double diff = 0.;
    int below = 0;
    for(int l = 0; l < knns.length; l++) {
      diff += Math.abs(kdistmean - knns[l]);
      if(knns[l] < kdistmean) {
        below++;
      }
    }
    return (below > 0) ? diff / (2. * kdistmean * below) : 0;
  }

  /**
   * Parameterization class.
   *
   * @author Erich Schubert
   *
   * @apiviz.exclude
   */
  public static class Parameterizer extends AbstractParameterizer {
    @Override
    protected SURFINGDependenceMeasure makeInstance() {
      return STATIC;
    }
  }
}