\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL - dD Spatial Searching
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Spatial_searching/general_neighbor_searching.cpp
#include <CGAL/Epick_d.h>
#include <CGAL/point_generators_d.h>
#include <CGAL/Manhattan_distance_iso_box_point.h>
#include <CGAL/K_neighbor_search.h>
#include <CGAL/Search_traits_d.h>
typedef Kernel::Point_d Point_d;
typedef CGAL::Random_points_in_cube_d<Point_d> Random_points_iterator;
typedef Kernel::Iso_box_d Iso_box_d;
typedef Kernel TreeTraits;
typedef Neighbor_search::Tree Tree;
int main() {
const int N = 1000;
const unsigned int K = 10;
Tree tree;
Random_points_iterator rpit(4,1000.0);
for(int i = 0; i < N; i++){
tree.insert(*rpit++);
}
Point_d pp(0.1,0.1,0.1,0.1);
Point_d qq(0.2,0.2,0.2,0.2);
Iso_box_d query(pp,qq);
Distance tr_dist;
Neighbor_search N1(tree, query, 5, 10.0, false); // eps=10.0, nearest=false
std::cout << "For query rectangle = [0.1, 0.2]^4 " << std::endl
<< "the " << K << " approximate furthest neighbors are: " << std::endl;
for (Neighbor_search::iterator it = N1.begin();it != N1.end();it++) {
std::cout << " Point " << it->first << " at distance " << tr_dist.inverse_of_transformed_distance(it->second) << std::endl;
}
return 0;
}