\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)

CGAL 4.8 - dD Spatial Searching

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Spatial_searching/user_defined_point_and_distance.cpp
#include <CGAL/Search_traits.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/Orthogonal_k_neighbor_search.h>
#include "Point.h" // defines types Point, Construct_coord_iterator
#include "Distance.h"
typedef CGAL::Random_points_in_cube_3<Point> Random_points_iterator;
typedef CGAL::Counting_iterator<Random_points_iterator> N_Random_points_iterator;
typedef CGAL::Dimension_tag<3> D;
typedef CGAL::Search_traits<double, Point, const double*, Construct_coord_iterator, D> Traits;
typedef CGAL::Orthogonal_k_neighbor_search<Traits, Distance> K_neighbor_search;
typedef K_neighbor_search::Tree Tree;
int main() {
const int N = 1000;
const unsigned int K = 5;
// generator for random data points in the cube ( (-1,-1,-1), (1,1,1) )
Random_points_iterator rpit( 1.0);
// Insert number_of_data_points in the tree
Tree tree(N_Random_points_iterator(rpit,0),
N_Random_points_iterator(N));
Point query(0.0, 0.0, 0.0);
Distance tr_dist;
// search K nearest neighbours
K_neighbor_search search(tree, query, K);
for(K_neighbor_search::iterator it = search.begin(); it != search.end(); it++){
std::cout << " d(q, nearest neighbor)= "
<< tr_dist.inverse_of_transformed_distance(it->second) << std::endl;
}
// search K furthest neighbour searching, with eps=0, search_nearest=false
K_neighbor_search search2(tree, query, K, 0.0, false);
for(K_neighbor_search::iterator it = search2.begin(); it != search2.end(); it++){
std::cout << " d(q, furthest neighbor)= "
<< tr_dist.inverse_of_transformed_distance(it->second) << std::endl;
}
return 0;
}