package de.lmu.ifi.dbs.elki.distance.distancefunction.histogram;

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

 Copyright (C) 2011
 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.data.NumberVector;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Distance function based on histogram matching, i.e. Manhattan distance on the
 * cumulative density function.
 * 
 * This distance function assumes there exist a natural order in the vectors,
 * i.e. they should be some 1-dimensional histogram.
 * 
 * @author Erich Schubert
 */
public class HistogramMatchDistanceFunction extends AbstractNumberVectorDistanceFunction {
  /**
   * Static instance. Use this!
   */
  public static final HistogramMatchDistanceFunction STATIC = new HistogramMatchDistanceFunction();

  /**
   * Constructor for the Histogram match distance function.
   * 
   * @deprecated Use static instance!
   */
  @Deprecated
  public HistogramMatchDistanceFunction() {
    super();
  }

  @Override
  public double distance(NumberVector v1, NumberVector v2) {
    final int dim = dimensionality(v1, v2);
    double xs = 0., ys = 0., agg = 0.;
    for(int i = 0; i < dim; i++) {
      xs += v1.doubleValue(i);
      ys += v2.doubleValue(i);
      agg += Math.abs(xs - ys);
    }
    return agg;
  }

  @Override
  public String toString() {
    return "HistogramMatchDistanceFunction";
  }

  @Override
  public boolean equals(Object obj) {
    if(obj == null) {
      return false;
    }
    if(obj == this) {
      return true;
    }
    if(this.getClass().equals(obj.getClass())) {
      return true;
    }
    return super.equals(obj);
  }

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

@@ -1085,16 +1085,78 @@
 nction;%0A
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;%0A
 import d
@@ -1486,34 +1486,817 @@
 %0A * 
-@author Erich Schubert%0A */
+This is also known as Earth Movers Distance (EMD), 1st Mallows distance or%0A * 1st Wasserstein metric (also Vasershtein metric), for the special case of a%0A * one-dimensional histogram, where the cost is linear in the number of bins to%0A * transport.%0A * %0A * Reference:%0A * %3Cp%3E%0A * L.N. Vaserstein%3Cbr /%3E%0A * Markov processes over denumerable products of spaces describing large systems%0A * of automata %3Cbr /%3E%0A * Problemy Peredachi Informatsii 5.3 / Problems of Information Transmission 5:3%0A * %3C/p%3E%0A * %0A * @author Erich Schubert%0A */%0A@Reference(authors = %22L.N. Vaserstein%22, //%0Atitle = %22Markov processes over denumerable products of spaces describing large systems of automata%22, //%0Abooktitle = %22Problemy Peredachi Informatsii 5.3 / Problems of Information Transmission, 5:3%22, //%0Aurl = %22http://mi.mathnet.ru/eng/ppi1811%22)
 %0Apub
@@ -3028,24 +3028,88 @@
 n agg;%0A  %7D%0A%0A
+  @Override%0A  public boolean isMetric() %7B%0A    return true;%0A  %7D%0A%0A
   @Override%0A

Add reference, flag as metric.