diff options
9 files changed, 52 insertions, 54 deletions
diff --git a/src/Spatial_searching/doc/Intro_spatial_searching.h b/src/Spatial_searching/doc/Intro_spatial_searching.h index 22652ac4..1ee5e92e 100644 --- a/src/Spatial_searching/doc/Intro_spatial_searching.h +++ b/src/Spatial_searching/doc/Intro_spatial_searching.h @@ -46,7 +46,7 @@ namespace spatial_searching { * * \section spatial_searching_examples Example * - * This example generates 500 random points, then performs near search, and queries for nearest and farthest points using different methods. + * This example generates 500 random points, then performs all-near-neighbors searches, and queries for nearest and furthest neighbors using different methods. * * \include Spatial_searching/example_spatial_searching.cpp * diff --git a/src/Spatial_searching/example/example_spatial_searching.cpp b/src/Spatial_searching/example/example_spatial_searching.cpp index 201b589e..034ad24a 100644 --- a/src/Spatial_searching/example/example_spatial_searching.cpp +++ b/src/Spatial_searching/example/example_spatial_searching.cpp @@ -24,34 +24,34 @@ int main(void) { // 10-nearest neighbor query std::cout << "10 nearest neighbors from points[20]:\n"; - auto knn_range = points_ds.query_k_nearest_neighbors(points[20], 10, true); + auto knn_range = points_ds.k_nearest_neighbors(points[20], 10, true); for (auto const& nghb : knn_range) std::cout << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; // Incremental nearest neighbor query std::cout << "Incremental nearest neighbors:\n"; - auto inn_range = points_ds.query_incremental_nearest_neighbors(points[45]); + 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::cout << ins_iterator->first << " (sq. dist. = " << ins_iterator->second << ")\n"; - // 10-farthest neighbor query - std::cout << "10 farthest neighbors from points[20]:\n"; - auto kfn_range = points_ds.query_k_farthest_neighbors(points[20], 10, true); + // 10-furthest neighbor query + std::cout << "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::cout << nghb.first << " (sq. dist. = " << nghb.second << ")\n"; - // Incremental farthest neighbor query - std::cout << "Incremental farthest neighbors:\n"; - auto ifn_range = points_ds.query_incremental_farthest_neighbors(points[45]); + // Incremental furthest neighbor query + std::cout << "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::cout << ifs_iterator->first << " (sq. dist. = " << ifs_iterator->second << ")\n"; - // Near search - std::cout << "Near search:\n"; + // All-near-neighbors search + std::cout << "All-near-neighbors search:\n"; std::vector<std::size_t> rs_result; - points_ds.near_search(points[45], 0.5, std::back_inserter(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::cout << p_idx << " (sq. dist. = " << k.squared_distance_d_object()(points[p_idx], points[45]) << ")\n"; diff --git a/src/Spatial_searching/include/gudhi/Kd_tree_search.h b/src/Spatial_searching/include/gudhi/Kd_tree_search.h index af04736b..ef428002 100644 --- a/src/Spatial_searching/include/gudhi/Kd_tree_search.h +++ b/src/Spatial_searching/include/gudhi/Kd_tree_search.h @@ -42,19 +42,19 @@ namespace spatial_searching { /** * \class Kd_tree_search Kd_tree_search.h gudhi/Kd_tree_search.h - * \brief Spatial tree data structure to perform (approximate) nearest and farthest neighbor search. + * \brief Spatial tree data structure to perform (approximate) nearest and furthest neighbor search. * * \ingroup spatial_searching * * \details * The class Kd_tree_search is a tree-based data structure, based on * <a target="_blank" href="http://doc.cgal.org/latest/Spatial_searching/index.html">CGAL dD spatial searching data structures</a>. - * It provides a simplified API to perform (approximate) nearest and farthest neighbor searches. Contrary to CGAL default behavior, the tree + * It provides a simplified API to perform (approximate) nearest and furthest neighbor searches. Contrary to CGAL default behavior, the tree * does not store the points themselves, but stores indices. * - * There are two types of queries: the <i>k-nearest or k-farthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest - * or farthest points are computed right away, - * and the <i>incremental nearest or farthest neighbor query</i>, where no number of neighbors is provided during the call, as the + * There are two types of queries: the <i>k-nearest or k-furthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest + * or furthest points are computed right away, + * and the <i>incremental nearest or furthest neighbor query</i>, where no number of neighbors is provided during the call, as the * neighbors will be computed incrementally when the iterator on the range is incremented. * * \tparam Search_traits must be a model of the <a target="_blank" @@ -96,7 +96,7 @@ class Kd_tree_search { typedef CGAL::Orthogonal_k_neighbor_search<STraits> K_neighbor_search; typedef typename K_neighbor_search::Tree Tree; typedef typename K_neighbor_search::Distance Distance; - /// \brief The range returned by a k-nearest or k-farthest neighbor search. + /// \brief The range returned by a k-nearest or k-furthest neighbor search. /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index /// of a point P and `second` is the squared distance between P and the query point. typedef K_neighbor_search KNS_range; @@ -104,7 +104,7 @@ class Kd_tree_search { typedef CGAL::Orthogonal_incremental_neighbor_search< STraits, Distance, CGAL::Sliding_midpoint<STraits>, Tree> Incremental_neighbor_search; - /// \brief The range returned by an incremental nearest or farthest neighbor search. + /// \brief The range returned by an incremental nearest or furthest neighbor search. /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index /// of a point P and `second` is the squared distance between P and the query point. typedef Incremental_neighbor_search INS_range; @@ -171,7 +171,7 @@ class Kd_tree_search { /// @param[in] sorted Indicates if the computed sequence of k-nearest neighbors needs to be sorted. /// @param[in] eps Approximation factor. /// @return A range (whose `value_type` is `std::size_t`) containing the k-nearest neighbors. - KNS_range query_k_nearest_neighbors( + KNS_range k_nearest_neighbors( Point const& p, unsigned int k, bool sorted = true, @@ -197,7 +197,7 @@ class Kd_tree_search { /// neighbors sorted by their distance to p. /// All the neighbors are not computed by this function, but they will be /// computed incrementally when the iterator on the range is incremented. - INS_range query_incremental_nearest_neighbors(Point const& p, FT eps = FT(0)) const { + INS_range incremental_nearest_neighbors(Point const& p, FT eps = FT(0)) const { // Initialize the search structure, and search all N points // Note that we need to pass the Distance explicitly since it needs to // know the property map @@ -211,13 +211,13 @@ class Kd_tree_search { return search; } - /// \brief Search for the k-farthest points from a query point. + /// \brief Search for the k-furthest points from a query point. /// @param[in] p The query point. - /// @param[in] k Number of farthest points to search. - /// @param[in] sorted Indicates if the computed sequence of k-farthest neighbors needs to be sorted. + /// @param[in] k Number of furthest points to search. + /// @param[in] sorted Indicates if the computed sequence of k-furthest neighbors needs to be sorted. /// @param[in] eps Approximation factor. - /// @return A range (whose `value_type` is `std::size_t`) containing the k-farthest neighbors. - KNS_range query_k_farthest_neighbors( + /// @return A range (whose `value_type` is `std::size_t`) containing the k-furthest neighbors. + KNS_range k_furthest_neighbors( Point const& p, unsigned int k, bool sorted = true, @@ -236,14 +236,14 @@ class Kd_tree_search { return search; } - /// \brief Search incrementally for the farthest neighbors from a query point. + /// \brief Search incrementally for the furthest neighbors from a query point. /// @param[in] p The query point. /// @param[in] eps Approximation factor. /// @return A range (whose `value_type` is `std::size_t`) /// containing the neighbors sorted by their distance to p. /// All the neighbors are not computed by this function, but they will be /// computed incrementally when the iterator on the range is incremented. - INS_range query_incremental_farthest_neighbors(Point const& p, FT eps = FT(0)) const { + INS_range incremental_furthest_neighbors(Point const& p, FT eps = FT(0)) const { // Initialize the search structure, and search all N points // Note that we need to pass the Distance explicitly since it needs to // know the property map @@ -264,7 +264,7 @@ class Kd_tree_search { /// Note: `it` is used this way: `*it++ = each_point`. /// @param[in] eps Approximation factor. template <typename OutputIterator> - void near_search(Point const& p, + void all_near_neighbors(Point const& p, FT radius, OutputIterator it, FT eps = FT(0)) const { diff --git a/src/Spatial_searching/test/test_Kd_tree_search.cpp b/src/Spatial_searching/test/test_Kd_tree_search.cpp index 663a103a..8a8334c3 100644 --- a/src/Spatial_searching/test/test_Kd_tree_search.cpp +++ b/src/Spatial_searching/test/test_Kd_tree_search.cpp @@ -48,12 +48,12 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { Points_ds points_ds(points); - // Test query_k_nearest_neighbors + // Test k_nearest_neighbors std::size_t closest_pt_index = - points_ds.query_k_nearest_neighbors(points[10], 1, false).begin()->first; + points_ds.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); + auto kns_range = points_ds.k_nearest_neighbors(points[20], 10, true); std::vector<std::size_t> knn_result; FT last_dist = -1.; @@ -63,12 +63,12 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { last_dist = nghb.second; } - // Test query_incremental_nearest_neighbors + // Test incremental_nearest_neighbors closest_pt_index = - points_ds.query_incremental_nearest_neighbors(points[10]).begin()->first; + points_ds.incremental_nearest_neighbors(points[10]).begin()->first; BOOST_CHECK(closest_pt_index == 10); - auto inn_range = points_ds.query_incremental_nearest_neighbors(points[20]); + auto inn_range = points_ds.incremental_nearest_neighbors(points[20]); std::vector<std::size_t> inn_result; last_dist = -1.; @@ -83,8 +83,8 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { // 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); + // 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; @@ -94,8 +94,8 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { last_dist = nghb.second; } - // Test query_k_farthest_neighbors - auto ifn_range = points_ds.query_incremental_farthest_neighbors(points[20]); + // 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; @@ -110,10 +110,10 @@ BOOST_AUTO_TEST_CASE(test_Kd_tree_search) { // Same result for KFN and IFN? BOOST_CHECK(kfn_result == ifn_result); - // Test near search + // 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.near_search(rs_q, 0.5, std::back_inserter(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); diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h index 507f8c79..7d3b97fb 100644 --- a/src/Subsampling/include/gudhi/sparsify_point_set.h +++ b/src/Subsampling/include/gudhi/sparsify_point_set.h @@ -83,7 +83,7 @@ sparsify_point_set( *output_it++ = *it_pt; - auto ins_range = points_ds.query_incremental_nearest_neighbors(*it_pt); + auto ins_range = points_ds.incremental_nearest_neighbors(*it_pt); // If another point Q is closer that min_squared_dist, mark Q to be dropped for (auto const& neighbor : ins_range) { diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h index 9fa7c825..a5cefd6a 100644 --- a/src/Tangential_complex/include/gudhi/Tangential_complex.h +++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h @@ -1093,8 +1093,8 @@ class Tangential_complex { std::size_t num_inserted_points = 1; #endif // const int NUM_NEIGHBORS = 150; - // KNS_range ins_range = m_points_ds.query_k_nearest_neighbors(center_pt, NUM_NEIGHBORS); - INS_range ins_range = m_points_ds.query_incremental_nearest_neighbors(center_pt); + // KNS_range ins_range = m_points_ds.k_nearest_neighbors(center_pt, NUM_NEIGHBORS); + INS_range ins_range = m_points_ds.incremental_nearest_neighbors(center_pt); // While building the local triangulation, we keep the radius // of the sphere "star sphere" centered at "center_vertex" @@ -1203,7 +1203,7 @@ class Tangential_complex { Point center_point = compute_perturbed_point(i); // Among updated point, what is the closer from our center point? std::size_t closest_pt_index = - updated_pts_ds.query_k_nearest_neighbors(center_point, 1, false).begin()->first; + updated_pts_ds.k_nearest_neighbors(center_point, 1, false).begin()->first; typename K::Construct_weighted_point_d k_constr_wp = m_k.construct_weighted_point_d_object(); @@ -1315,11 +1315,10 @@ class Tangential_complex { m_k.compute_coordinate_d_object(); #ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM - KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors( - p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds_for_tse.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points_for_tse; #else - KNS_range kns_range = m_points_ds.query_k_nearest_neighbors(p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points; #endif @@ -1413,11 +1412,10 @@ class Tangential_complex { const Point &p = m_points[*it_index]; #ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM - KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors( - p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds_for_tse.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points_for_tse; #else - KNS_range kns_range = m_points_ds.query_k_nearest_neighbors(p, num_pts_for_pca, false); + KNS_range kns_range = m_points_ds.k_nearest_neighbors(p, num_pts_for_pca, false); const Points &points_for_pca = m_points; #endif diff --git a/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h b/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h index fb669ef8..4f3cef4f 100644 --- a/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h +++ b/src/Witness_complex/include/gudhi/Euclidean_strong_witness_complex.h @@ -84,7 +84,7 @@ class Euclidean_strong_witness_complex : landmarks_(std::begin(landmarks), std::end(landmarks)), landmark_tree_(landmarks_) { nearest_landmark_table_.reserve(boost::size(witnesses)); for (auto w : witnesses) - nearest_landmark_table_.push_back(landmark_tree_.query_incremental_nearest_neighbors(w)); + nearest_landmark_table_.push_back(landmark_tree_.incremental_nearest_neighbors(w)); } /** \brief Returns the point corresponding to the given vertex. diff --git a/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h b/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h index 6afe9a5d..ff8bb139 100644 --- a/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h +++ b/src/Witness_complex/include/gudhi/Euclidean_witness_complex.h @@ -86,7 +86,7 @@ class Euclidean_witness_complex : landmarks_(std::begin(landmarks), std::end(landmarks)), landmark_tree_(landmarks) { nearest_landmark_table_.reserve(boost::size(witnesses)); for (auto w : witnesses) - nearest_landmark_table_.push_back(landmark_tree_.query_incremental_nearest_neighbors(w)); + nearest_landmark_table_.push_back(landmark_tree_.incremental_nearest_neighbors(w)); } /** \brief Returns the point corresponding to the given vertex. diff --git a/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp b/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp index 62fd1157..4f718203 100644 --- a/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp +++ b/src/Witness_complex/test/test_euclidean_simple_witness_complex.cpp @@ -75,7 +75,7 @@ BOOST_AUTO_TEST_CASE(simple_witness_complex) { Kd_tree landmark_tree(landmarks); Nearest_landmark_table nearest_landmark_table; for (auto w: witnesses) - nearest_landmark_table.push_back(landmark_tree.query_incremental_nearest_neighbors(w)); + nearest_landmark_table.push_back(landmark_tree.incremental_nearest_neighbors(w)); // Weak witness complex: Euclidean version EuclideanWitnessComplex eucl_witness_complex(landmarks, |