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) 2014
 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.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Arrange dimensions based on the entropy of the slope spectrum.
 * 
 * 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>
 *
 * TODO: shouldn't this be normalized by the single-dimension entropies or so?
 * 
 * @author Erich Schubert
 * @author Robert Rödler
 */
@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 SlopeDependenceMeasure extends AbstractDependenceMeasure {
  /**
   * Static instance.
   */
  public static final SlopeDependenceMeasure STATIC = new SlopeDependenceMeasure();

  /**
   * Full precision.
   */
  protected final static int PRECISION = 40;

  /**
   * Precision for entropy normalization.
   */
  protected final static double LOG_PRECISION = Math.log(PRECISION);

  /**
   * Scaling factor.
   */
  protected final static double RESCALE = PRECISION * .5;

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

  @Override
  public <A, B> double dependence(NumberArrayAdapter<?, A> adapter1, A data1, NumberArrayAdapter<?, B> adapter2, B data2) {
    final int len = size(adapter1, data1, adapter2, data2);

    // Get attribute value range:
    final double off1, scale1, off2, scale2;
    {
      double mi = adapter1.getDouble(data1, 0), ma = mi;
      for(int i = 1; i < len; ++i) {
        double v = adapter1.getDouble(data1, i);
        if(v < mi) {
          mi = v;
        }
        else if(v > ma) {
          ma = v;
        }
      }
      off1 = mi;
      scale1 = (ma > mi) ? (1. / (ma - mi)) : 1.;
      // Second data
      mi = adapter2.getDouble(data2, 0);
      ma = mi;
      for(int i = 1; i < len; ++i) {
        double v = adapter2.getDouble(data2, i);
        if(v < mi) {
          mi = v;
        }
        else if(v > ma) {
          ma = v;
        }
      }
      off2 = mi;
      scale2 = (ma > mi) ? (1. / (ma - mi)) : 1.;
    }

    // Collect angular histograms.
    // Note, we only fill half of the matrix
    int[] angles = new int[PRECISION];

    // Scratch buffer
    for(int i = 0; i < len; i++) {
      double x = adapter1.getDouble(data1, i), y = adapter2.getDouble(data2, i);
      x = (x - off1) * scale1;
      y = (y - off2) * scale2;
      final double delta = x - y + 1;
      int div = (int) Math.round(delta * RESCALE);
      // TODO: do we really need this check?
      div = (div < 0) ? 0 : (div >= PRECISION) ? PRECISION - 1 : div;
      angles[div] += 1;
    }

    // Compute entropy:
    double entropy = 0.;
    for(int l = 0; l < PRECISION; l++) {
      if(angles[l] > 0) {
        final double p = angles[l] / (double) len;
        entropy += p * Math.log(p);
      }
    }
    return 1 + entropy / LOG_PRECISION;
  }

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