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

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

 Copyright (C) 2013
 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.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.utilities.Alias;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Manhattan distance function to compute the Manhattan distance for a pair of
 * FeatureVectors.
 * 
 * @author Arthur Zimek
 */
@Alias({ "taxicab", "cityblock", "l1", "ManhattanDistanceFunction", "de.lmu.ifi.dbs.elki.distance.distancefunction.ManhattanDistanceFunction" })
public class ManhattanDistanceFunction extends LPIntegerNormDistanceFunction {
  /**
   * The static instance to use.
   */
  public static final ManhattanDistanceFunction STATIC = new ManhattanDistanceFunction();

  /**
   * Provides a Manhattan distance function that can compute the Manhattan
   * distance (that is a DoubleDistance) for FeatureVectors.
   * 
   * @deprecated Use static instance!
   */
  @Deprecated
  public ManhattanDistanceFunction() {
    super(1);
  }

  private final double doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg) {
    for (int d = start; d < end; d++) {
      final double xd = v1.doubleValue(d), yd = v2.doubleValue(d);
      final double delta = (xd >= yd) ? xd - yd : yd - xd;
      agg += delta;
    }
    return agg;
  }

  private final double doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg) {
    for (int d = start; d < end; d++) {
      final double value = v.doubleValue(d), min = mbr.getMin(d);
      double delta = min - value;
      if (delta < 0.) {
        delta = value - mbr.getMax(d);
      }
      if (delta > 0.) {
        agg += delta;
      }
    }
    return agg;
  }

  private final double doublePreDistanceMBR(SpatialComparable mbr1, SpatialComparable mbr2, int start, int end, double agg) {
    for (int d = start; d < end; d++) {
      double delta = mbr2.getMin(d) - mbr1.getMax(d);
      if (delta < 0.) {
        delta = mbr1.getMin(d) - mbr2.getMax(d);
      }
      if (delta > 0.) {
        agg += delta;
      }
    }
    return agg;
  }

  private final double doublePreNorm(NumberVector<?> v, int start, int end, double agg) {
    for (int d = start; d < end; d++) {
      final double xd = v.doubleValue(d);
      final double delta = (xd >= 0.) ? xd : -xd;
      agg += delta;
    }
    return agg;
  }

  private final double doublePreNormMBR(SpatialComparable mbr, int start, int end, double agg) {
    for (int d = start; d < end; d++) {
      double delta = mbr.getMin(d);
      if (delta < 0.) {
        delta = -mbr.getMax(d);
      }
      if (delta > 0.) {
        agg += delta;
      }
    }
    return agg;
  }

  @Override
  public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
    final int dim1 = v1.getDimensionality(), dim2 = v2.getDimensionality();
    final int mindim = (dim1 < dim2) ? dim1 : dim2;
    double agg = doublePreDistance(v1, v2, 0, mindim, 0.);
    if (dim1 > mindim) {
      agg = doublePreNorm(v1, mindim, dim1, agg);
    } else if (dim2 > mindim) {
      agg = doublePreNorm(v2, mindim, dim2, agg);
    }
    return agg;
  }

  @Override
  public double doubleNorm(NumberVector<?> v) {
    return doublePreNorm(v, 0, v.getDimensionality(), 0.);
  }

  @Override
  public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
    final int dim1 = mbr1.getDimensionality(), dim2 = mbr2.getDimensionality();
    final int mindim = (dim1 < dim2) ? dim1 : dim2;

    final NumberVector<?> v1 = (mbr1 instanceof NumberVector) ? (NumberVector<?>) mbr1 : null;
    final NumberVector<?> v2 = (mbr2 instanceof NumberVector) ? (NumberVector<?>) mbr2 : null;

    double agg = 0.;
    if (v1 != null) {
      if (v2 != null) {
        agg = doublePreDistance(v1, v2, 0, mindim, agg);
      } else {
        agg = doublePreDistanceVM(v1, mbr2, 0, mindim, agg);
      }
    } else {
      if (v2 != null) {
        agg = doublePreDistanceVM(v2, mbr1, 0, mindim, agg);
      } else {
        agg = doublePreDistanceMBR(mbr1, mbr2, 0, mindim, agg);
      }
    }
    // first object has more dimensions.
    if (dim1 > mindim) {
      if (v1 != null) {
        agg = doublePreNorm(v1, mindim, dim1, agg);
      } else {
        agg = doublePreNormMBR(v1, mindim, dim1, agg);
      }
    }
    // second object has more dimensions.
    if (dim2 > mindim) {
      if (v2 != null) {
        agg = doublePreNorm(v2, mindim, dim2, agg);
      } else {
        agg = doublePreNormMBR(mbr2, mindim, dim2, agg);
      }
    }
    return agg;
  }

  @Override
  public boolean isMetric() {
    return true;
  }

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

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