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Diffstat (limited to 'src/Spatial_searching/test/test_Kd_tree_search.cpp')
-rw-r--r-- | src/Spatial_searching/test/test_Kd_tree_search.cpp | 112 |
1 files changed, 112 insertions, 0 deletions
diff --git a/src/Spatial_searching/test/test_Kd_tree_search.cpp b/src/Spatial_searching/test/test_Kd_tree_search.cpp new file mode 100644 index 00000000..0ef22023 --- /dev/null +++ b/src/Spatial_searching/test/test_Kd_tree_search.cpp @@ -0,0 +1,112 @@ +/* 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 query_k_nearest_neighbors + std::size_t closest_pt_index = + points_ds.query_k_nearest_neighbors(points[10], 1, false).begin()->first; + BOOST_CHECK(closest_pt_index == 10); + + auto kns_range = points_ds.query_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 query_incremental_nearest_neighbors + closest_pt_index = + points_ds.query_incremental_nearest_neighbors(points[10]).begin()->first; + BOOST_CHECK(closest_pt_index == 10); + + auto inn_range = points_ds.query_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 query_k_farthest_neighbors + auto kfn_range = points_ds.query_k_farthest_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 query_k_farthest_neighbors + auto ifn_range = points_ds.query_incremental_farthest_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); +} |