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

/*
 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.utilities.optionhandling.AbstractParameterizer;

/**
 * Jensen-Shannon Divergence is essentially the same as Jeffrey divergence, only
 * scaled by half.
 * 
 * @author Erich Schubert
 */
public class JensenShannonDivergenceDistanceFunction extends JeffreyDivergenceDistanceFunction {
  /**
   * Static instance. Use this!
   */
  public static final JensenShannonDivergenceDistanceFunction STATIC = new JensenShannonDivergenceDistanceFunction();

  /**
   * Constructor for the Jensen-Shannon divergence.
   *
   * @deprecated Use static instance!
   */
  @Deprecated
  public JensenShannonDivergenceDistanceFunction() {
    super();
  }

  @Override
  public double distance(NumberVector v1, NumberVector v2) {
    return .5 * super.distance(v1, v2);
  }

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

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

@@ -998,16 +998,75 @@
 Vector;%0A
+import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;%0A
 import d
@@ -1833,32 +1833,168 @@
 e(v1, v2);%0A  %7D%0A%0A
+  @Override%0A  public double minDist(SpatialComparable mbr1, SpatialComparable mbr2) %7B%0A    return .5 * super.minDist(mbr1, mbr2);%0A  %7D%0A  %0A
   @Override%0A  pu

Add missing override for minDist.