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) 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.data.SparseNumberVector;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.Norm;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;

/**
 * LP-Norm, optimized for {@link SparseNumberVector}s.
 * 
 * @author Erich Schubert
 */
// TODO: implement SpatialDistanceFunction
public class SparseLPNormDistanceFunction extends AbstractPrimitiveDistanceFunction<SparseNumberVector> implements Norm<SparseNumberVector> {
  /**
   * P parameter and its inverse.
   */
  private double p, invp;

  /**
   * Constructor.
   */
  public SparseLPNormDistanceFunction(double p) {
    super();
    this.p = p;
    this.invp = 1. / p;
  }

  @Override
  public double distance(SparseNumberVector v1, SparseNumberVector v2) {
    // Get the bit masks
    double accu = 0.;
    int i1 = v1.iter(), i2 = v2.iter();
    while(v1.iterValid(i1) && v2.iterValid(i2)) {
      final int d1 = v1.iterDim(i1), d2 = v2.iterDim(i2);
      if(d1 < d2) {
        // In first only
        final double val = Math.abs(v1.iterDoubleValue(i1));
        accu += Math.pow(val, p);
        i1 = v1.iterAdvance(i1);
      }
      else if(d2 < d1) {
        // In second only
        final double val = Math.abs(v2.iterDoubleValue(i2));
        accu += Math.pow(val, p);
        i2 = v2.iterAdvance(i2);
      }
      else {
        // Both vectors have a value.
        final double val = Math.abs(v1.iterDoubleValue(i1) - v2.iterDoubleValue(i2));
        accu += Math.pow(val, p);
        i1 = v1.iterAdvance(i1);
        i2 = v2.iterAdvance(i2);
      }
    }
    while(v1.iterValid(i1)) {
      // In first only
      final double val = Math.abs(v1.iterDoubleValue(i1));
      accu += Math.pow(val, p);
      i1 = v1.iterAdvance(i1);
    }
    while(v2.iterValid(i2)) {
      // In second only
      final double val = Math.abs(v2.iterDoubleValue(i2));
      accu += Math.pow(val, p);
      i2 = v2.iterAdvance(i2);
    }
    return Math.pow(accu, invp);
  }

  @Override
  public double norm(SparseNumberVector v1) {
    double accu = 0.;
    for(int it = v1.iter(); v1.iterValid(it); it = v1.iterAdvance(it)) {
      final double val = Math.abs(v1.iterDoubleValue(it));
      accu += Math.pow(val, p);
    }
    return Math.pow(accu, invp);
  }

  @Override
  public SimpleTypeInformation<? super SparseNumberVector> getInputTypeRestriction() {
    return TypeUtil.SPARSE_VECTOR_VARIABLE_LENGTH;
  }

  @Override
  public boolean isMetric() {
    return (p >= 1.);
  }

  /**
   * Parameterizer
   * 
   * @author Erich Schubert
   * 
   * @apiviz.exclude
   */
  public static class Parameterizer extends AbstractParameterizer {
    /**
     * Value for p
     */
    double p = 2.;

    @Override
    protected void makeOptions(Parameterization config) {
      super.makeOptions(config);
      DoubleParameter pP = new DoubleParameter(LPNormDistanceFunction.Parameterizer.P_ID);
      pP.addConstraint(CommonConstraints.GREATER_THAN_ZERO_DOUBLE);
      if(config.grab(pP)) {
        p = pP.getValue();
      }
    }

    @Override
    protected SparseLPNormDistanceFunction makeInstance() {
      if(p == 2.) {
        return SparseEuclideanDistanceFunction.STATIC;
      }
      if(p == 1.) {
        return SparseManhattanDistanceFunction.STATIC;
      }
      if(p == Double.POSITIVE_INFINITY) {
        return SparseMaximumDistanceFunction.STATIC;
      }
      return new SparseLPNormDistanceFunction(p);
    }
  }
}