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example_spatial_searching.cpp

# Bottleneck_distance

#include <gudhi/Kd_tree_search.h>
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
#include <vector>
namespace gss = Gudhi::spatial_searching;
int main(void) {
typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
typedef typename K::Point_d Point;
typedef std::vector<Point> Points;
typedef gss::Kd_tree_search<K, Points> Points_ds;
CGAL::Random rd;
Points points;
for (int i = 0; i < 500; ++i)
points.push_back(Point(rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1)));
Points_ds points_ds(points);
// 10-nearest neighbor query
std::clog << "10 nearest neighbors from points[20]:\n";
auto knn_range = points_ds.k_nearest_neighbors(points[20], 10, true);
for (auto const nghb : knn_range)
std::clog << nghb.first << " (sq. dist. = " << nghb.second << ")\n";
// Incremental nearest neighbor query
std::clog << "Incremental nearest neighbors:\n";
auto inn_range = points_ds.incremental_nearest_neighbors(points[45]);
// Get the neighbors in distance order until we hit the first point
for (auto ins_iterator = inn_range.begin(); ins_iterator->first != 0; ++ins_iterator)
std::clog << ins_iterator->first << " (sq. dist. = " << ins_iterator->second << ")\n";
// 10-furthest neighbor query
std::clog << "10 furthest neighbors from points[20]:\n";
auto kfn_range = points_ds.k_furthest_neighbors(points[20], 10, true);
for (auto const nghb : kfn_range)
std::clog << nghb.first << " (sq. dist. = " << nghb.second << ")\n";
// Incremental furthest neighbor query
std::clog << "Incremental furthest neighbors:\n";
auto ifn_range = points_ds.incremental_furthest_neighbors(points[45]);
// Get the neighbors in distance reverse order until we hit the first point
for (auto ifs_iterator = ifn_range.begin(); ifs_iterator->first != 0; ++ifs_iterator)
std::clog << ifs_iterator->first << " (sq. dist. = " << ifs_iterator->second << ")\n";
// All-near-neighbors search
std::clog << "All-near-neighbors search:\n";
std::vector<std::size_t> rs_result;
points_ds.all_near_neighbors(points[45], 0.5, std::back_inserter(rs_result));
K k;
for (auto const& p_idx : rs_result)
std::clog << p_idx << " (sq. dist. = " << k.squared_distance_d_object()(points[p_idx], points[45]) << ")\n";
return 0;
}
Spatial tree data structure to perform (approximate) nearest and furthest neighbor search.
Definition: Kd_tree_search.h:70