// Copyright (c) 2002 Utrecht University (The Netherlands).
// All rights reserved.
//
// This file is part of CGAL (www.cgal.org); you may redistribute it under
// the terms of the Q Public License version 1.0.
// See the file LICENSE.QPL distributed with CGAL.
//
// Licensees holding a valid commercial license may use this file in
// accordance with the commercial license agreement provided with the software.
//
// This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
// WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
//
// $URL: svn+ssh://scm.gforge.inria.fr/svn/cgal/branches/CGAL-3.2-branch/Spatial_searching/include/CGAL/Orthogonal_k_neighbor_search.h $
// $Id: Orthogonal_k_neighbor_search.h 28567 2006-02-16 14:30:13Z lsaboret $
// 
//
// Author(s)     : Hans Tangelder (<hanst@cs.uu.nl>)

#ifndef  ORTHOGONAL_K_NEIGHBOR_SEARCH_H
#define  ORTHOGONAL_K_NEIGHBOR_SEARCH_H
#include <cstring>
#include <list>
#include <queue>
#include <memory>
#include <CGAL/Kd_tree_node.h>
#include <CGAL/Kd_tree.h>
#include <CGAL/Euclidean_distance.h>
#include <CGAL/Splitters.h>

namespace CGAL {

  template <class SearchTraits, 
            class Distance_= Euclidean_distance<SearchTraits>,
            class Splitter_= Sliding_midpoint<SearchTraits> ,
            class Tree_= Kd_tree<SearchTraits, Splitter_, Tag_true> >
  class Orthogonal_k_neighbor_search {

  public:

    typedef Splitter_ Splitter;
    typedef Tree_  Tree;
    typedef Distance_ Distance;
    typedef typename SearchTraits::Point_d Point_d;
    typedef typename Distance::Query_item Query_item;

    typedef typename SearchTraits::FT FT;
    typedef std::pair<Point_d,FT> Point_with_transformed_distance;
  
    typedef typename Tree::Node_handle Node_handle;
  
    typedef typename Tree::Point_d_iterator Point_d_iterator;

  private:

    int number_of_internal_nodes_visited;
    int number_of_leaf_nodes_visited;
    int number_of_items_visited;

    bool search_nearest;

    FT multiplication_factor;
    Query_item query_object;
    int total_item_number;
    FT distance_to_root;   

    typedef std::list<Point_with_transformed_distance> NN_list;

  public:

    typedef typename NN_list::const_iterator iterator;

  private:

    NN_list l;
    int max_k;
    int actual_k;


    Distance distance_instance;

    bool 
    branch(FT distance) 
    {
      if (actual_k<max_k) return true;
      else 
	if (search_nearest) return 
			      ( distance * multiplication_factor < l.rbegin()->second);
	else return 
	       ( distance > 
		 l.begin()->second * multiplication_factor);
    }

    void 
    insert(Point_d* I, FT dist) 
    {
      bool insert;
      if (actual_k<max_k) insert=true;
      else 
	if (search_nearest) insert=
			      ( dist < l.rbegin()->second ); 
	else insert=(dist > l.rbegin()->second);
      if (insert) {
	actual_k++;	 	
	typename NN_list::iterator it=l.begin();
        if (search_nearest)
	for (; (it != l.end()); ++it) 
	  { if (dist < it->second) break;}
        else 
        for (; (it != l.end()); ++it) 
	  { if (dist > it->second) break;}
	Point_with_transformed_distance NN_Candidate(*I,dist);
	l.insert(it,NN_Candidate);
	if (actual_k > max_k) {
	  actual_k--;
	  l.pop_back();
	}
      }

    }

	
  public:
  
    iterator 
    begin() const
    {
      return l.begin();
    }

    iterator 
    end() const
    {
      return l.end();
    }


    // constructor
    Orthogonal_k_neighbor_search(Tree& tree, const Query_item& q,  
				 int k=1, FT Eps=FT(0.0), bool Search_nearest=true, const Distance& d=Distance())
      : number_of_internal_nodes_visited(0), number_of_leaf_nodes_visited(0), number_of_items_visited(0), 
      search_nearest(Search_nearest), multiplication_factor(d.transformed_distance(1.0+Eps)), query_object(q), 
      total_item_number(tree.size()), max_k(k), actual_k(0), distance_instance(d) 
	
    {
      if (search_nearest) 
      distance_to_root = d.min_distance_to_rectangle(q, tree.bounding_box());
       else 
      	distance_to_root = d.max_distance_to_rectangle(q, tree.bounding_box());

      compute_neighbors_orthogonally(tree.root(), distance_to_root);
       
    }

    
    // Print statistics of the k_neighbor search process.
    std::ostream& 
    statistics (std::ostream& s) 
    {
      s << "K_Neighbor search statistics:" << std::endl;
      s << "Number of internal nodes visited:" 
	<< number_of_internal_nodes_visited << std::endl;
      s << "Number of leaf nodes visited:" 
	<< number_of_leaf_nodes_visited << std::endl;
      s << "Number of items visited:" 
	<< number_of_items_visited << std::endl;
      return s;
    }



  private:
   
    void 
    compute_neighbors_orthogonally(Node_handle N, FT rd)
    {
      typename SearchTraits::Construct_cartesian_const_iterator_d construct_it;
      typename SearchTraits::Cartesian_const_iterator_d query_object_it = construct_it(query_object);
      if (!(N->is_leaf())) {
	number_of_internal_nodes_visited++;
	int new_cut_dim=N->cutting_dimension();
	FT old_off, new_rd;
	FT new_off =
	  *(query_object_it + new_cut_dim) - 
	  N->cutting_value();
	if ( ((new_off < FT(0.0)) && (search_nearest)) ||
	     (( new_off >= FT(0.0)) && (!search_nearest))  ) {
	  compute_neighbors_orthogonally(N->lower(),rd);
	  if (search_nearest) {
	    old_off= *(query_object_it + new_cut_dim)-
	      N->low_value();
	    if (old_off>FT(0.0)) old_off=FT(0.0);
	  }
	  else 
	    {	
	      old_off= *(query_object_it + new_cut_dim) 
		- N->high_value();
	      if (old_off<FT(0.0)) old_off=FT(0.0);
	    }
	  new_rd=
	    distance_instance.new_distance(rd,old_off,
					   new_off,
					   new_cut_dim);
	  if (branch(new_rd)) 
	    compute_neighbors_orthogonally(N->upper(),
					   new_rd);                               
	}
	else { // compute new distance
	  compute_neighbors_orthogonally(N->upper(),rd); 
	  if (search_nearest) {
	    old_off= N->high_value() - 
	      *(query_object_it + new_cut_dim);
	    if (old_off>FT(0.0)) old_off=FT(0.0);
	  }
	  else 
	    {       
	      old_off= N->low_value() - 
		*(query_object_it + new_cut_dim);
	      if (old_off<FT(0.0)) old_off=FT(0.0);
	    }  
	  new_rd=
	    distance_instance. new_distance(rd,old_off,
					    new_off,
					    new_cut_dim);
	  if (branch(new_rd)) 
	    compute_neighbors_orthogonally(N->lower(),
					   new_rd);       
	}
      }
      else
	{
	  // n is a leaf
	  number_of_leaf_nodes_visited++;
	  if (N->size() > 0)
	    for (Point_d_iterator it=N->begin(); it != N->end(); it++) {
	      number_of_items_visited++;
	      FT distance_to_query_object=
		distance_instance.transformed_distance(query_object,**it);
	      insert(*it,distance_to_query_object);
	    }
	}
    }

    
    
   

  }; // class 



} // namespace CGAL


#endif  // ORTHOGONAL_K_NEIGHBOR_SEARCH