/*
 * This file is part of ELKI:
 * Environment for Developing KDD-Applications Supported by Index-Structures
 *
 * Copyright (C) 2022
 * 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/>.
 */
package elki.distance.probabilistic;

import elki.data.NumberVector;
import elki.data.spatial.SpatialComparable;
import elki.data.type.SimpleTypeInformation;
import elki.distance.AbstractNumberVectorDistance;
import elki.distance.SpatialPrimitiveDistance;
import elki.utilities.Alias;
import elki.utilities.Priority;
import elki.utilities.documentation.Reference;
import elki.utilities.optionhandling.Parameterizer;

/**
 * χ² distance function, symmetric version.
 * <p>
 * This implementation assumes \(\sum_i x_i=\sum_i y_i\), and is defined as:
 * \[\chi^2(\vec{x},\vec{y}):=2\sum\nolimits_i \tfrac{(x_i-x_i)^2}{x_i+y_i}\]
 * <p>
 * Reference:
 * <p>
 * J. Puzicha, J. M. Buhmann, Y. Rubner, C. Tomasi<br>
 * Empirical evaluation of dissimilarity measures for color and texture<br>
 * Proc. 7th IEEE International Conference on Computer Vision
 *
 * @author Erich Schubert
 * @since 0.6.0
 */
@Alias("chisq")
@Priority(Priority.IMPORTANT)
@Reference(authors = "J. Puzicha, J. M. Buhmann, Y. Rubner, C. Tomasi", //
    title = "Empirical evaluation of dissimilarity measures for color and texture", //
    booktitle = "Proc. 7th IEEE International Conference on Computer Vision", //
    url = "https://doi.org/10.1109/ICCV.1999.790412", //
    bibkey = "DBLP:conf/iccv/PuzichaRTB99")
public class ChiSquaredDistance extends AbstractNumberVectorDistance implements SpatialPrimitiveDistance<NumberVector> {
  /**
   * Static instance. Use this!
   */
  public static final ChiSquaredDistance STATIC = new ChiSquaredDistance();

  /**
   * Constructor for the Chi-Squared distance function.
   * 
   * @deprecated Use static instance!
   */
  @Deprecated
  public ChiSquaredDistance() {
    super();
  }

  @Override
  public double distance(NumberVector v1, NumberVector v2) {
    final int dim1 = v1.getDimensionality(), dim2 = v2.getDimensionality();
    final int mindim = (dim1 < dim2) ? dim1 : dim2;
    double agg = 0.;
    for(int d = 0; d < mindim; d++) {
      final double xd = v1.doubleValue(d), yd = v2.doubleValue(d);
      final double di = xd - yd;
      if(di > 0 || di < 0) {
        final double si = xd + yd;
        if(!(si > 0)) {
          continue;
        }
        agg += di * di / si;
      }
    }
    for(int d = mindim; d < dim1; d++) {
      final double xd = v1.doubleValue(d);
      agg += xd == xd ? xd : 0; // NaN safe
    }
    for(int d = mindim; d < dim2; d++) {
      final double yd = v2.doubleValue(d);
      agg += yd == yd ? yd : 0; // NaN safe
    }
    return 2. * agg;
  }

  @Override
  public double minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
    final int dim1 = mbr1.getDimensionality(), dim2 = mbr2.getDimensionality();
    final int mindim = (dim1 < dim2) ? dim1 : dim2;
    double agg = 0.;
    for(int d = 0; d < mindim; d++) {
      final double min1 = mbr1.getMin(d), max1 = mbr1.getMax(d);
      final double min2 = mbr2.getMin(d), max2 = mbr2.getMax(d);
      final double diff = min2 > max1 ? min2 - max1 : min1 > max2 ? min1 - max2 : 0;
      if(diff > 0) {
        final double si = max1 + max2; // Maximum sum
        if(!(si > 0)) {
          continue;
        }
        agg += diff * diff / si;
      }
    }
    for(int d = mindim; d < dim1; d++) {
      final double min1 = mbr1.getMin(d);
      if(min1 > 0.) {
        agg += min1;
      }
      else {
        final double max1 = mbr1.getMax(d);
        if(max1 < 0.) { // Should never happen.
          agg += max1;
        }
      }
    }
    for(int d = mindim; d < dim2; d++) {
      final double min2 = mbr2.getMin(d);
      if(min2 > 0.) {
        agg += min2;
      }
      else {
        final double max2 = mbr2.getMax(d);
        if(max2 < 0.) { // Should never happen.
          agg += max2;
        }
      }
    }
    return 2. * agg;
  }

  @Override
  public String toString() {
    return "ChiSquaredDistance";
  }
  
  @Override
  public boolean isSquared() {
    return true;
  }

  @Override
  public boolean equals(Object obj) {
    return obj == this || (obj != null && this.getClass().equals(obj.getClass()));
  }

  @Override
  public int hashCode() {
    return getClass().hashCode();
  }

  @Override
  public SimpleTypeInformation<? super NumberVector> getInputTypeRestriction() {
    return NumberVector.VARIABLE_LENGTH;
  }

  /**
   * Parameterization class, using the static instance.
   * 
   * @author Erich Schubert
   */
  public static class Par implements Parameterizer {
    @Override
    public ChiSquaredDistance make() {
      return STATIC;
    }
  }
}
