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/* This file is part of the Gudhi Library. The Gudhi library
* (Geometric Understanding in Higher Dimensions) is a generic C++
* library for computational topology.
*
* Author(s): Clement Jamin
*
* Copyright (C) 2016 Inria
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE Spatial_searching - test Kd_tree_search
#include <boost/test/unit_test.hpp>
#include <gudhi/Kd_tree_search.h>
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
#include <vector>
BOOST_AUTO_TEST_CASE(test_Kd_tree_search) {
typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
typedef K::FT FT;
typedef K::Point_d Point;
typedef std::vector<Point> Points;
typedef Gudhi::spatial_searching::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);
// Test k_nearest_neighbors
std::size_t closest_pt_index =
points_ds.k_nearest_neighbors(points[10], 1, false).begin()->first;
BOOST_CHECK(closest_pt_index == 10);
auto kns_range = points_ds.k_nearest_neighbors(points[20], 10, true);
std::vector<std::size_t> knn_result;
FT last_dist = -1.;
for (auto const& nghb : kns_range) {
BOOST_CHECK(nghb.second > last_dist);
knn_result.push_back(nghb.second);
last_dist = nghb.second;
}
// Test incremental_nearest_neighbors
closest_pt_index =
points_ds.incremental_nearest_neighbors(points[10]).begin()->first;
BOOST_CHECK(closest_pt_index == 10);
auto inn_range = points_ds.incremental_nearest_neighbors(points[20]);
std::vector<std::size_t> inn_result;
last_dist = -1.;
auto inn_it = inn_range.begin();
for (int i = 0; i < 10; ++inn_it, ++i) {
auto const& nghb = *inn_it;
BOOST_CHECK(nghb.second > last_dist);
inn_result.push_back(nghb.second);
last_dist = nghb.second;
}
// Same result for KNN and INN?
BOOST_CHECK(knn_result == inn_result);
// Test k_furthest_neighbors
auto kfn_range = points_ds.k_furthest_neighbors(points[20], 10, true);
std::vector<std::size_t> kfn_result;
last_dist = kfn_range.begin()->second;
for (auto const& nghb : kfn_range) {
BOOST_CHECK(nghb.second <= last_dist);
kfn_result.push_back(nghb.second);
last_dist = nghb.second;
}
// Test k_furthest_neighbors
auto ifn_range = points_ds.incremental_furthest_neighbors(points[20]);
std::vector<std::size_t> ifn_result;
last_dist = ifn_range.begin()->second;
auto ifn_it = ifn_range.begin();
for (int i = 0; i < 10; ++ifn_it, ++i) {
auto const& nghb = *ifn_it;
BOOST_CHECK(nghb.second <= last_dist);
ifn_result.push_back(nghb.second);
last_dist = nghb.second;
}
// Same result for KFN and IFN?
BOOST_CHECK(kfn_result == ifn_result);
// Test all_near_neighbors
Point rs_q(rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1));
std::vector<std::size_t> rs_result;
points_ds.all_near_neighbors(rs_q, 0.5, std::back_inserter(rs_result));
K k;
for (auto const& p_idx : rs_result)
BOOST_CHECK(k.squared_distance_d_object()(points[p_idx], rs_q) <= 0.5);
}
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