From 624126c4af0a0b7d50a347968a24789ca69adbfe Mon Sep 17 00:00:00 2001 From: cjamin Date: Wed, 13 Sep 2017 13:42:16 +0000 Subject: radius search => near search git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/call_it_near_search@2669 636b058d-ea47-450e-bf9e-a15bfbe3eedb Former-commit-id: e77c8b7de12df9cf5d0a6ef8d79e4b14a28237b3 --- src/Spatial_searching/doc/Intro_spatial_searching.h | 2 +- src/Spatial_searching/example/example_spatial_searching.cpp | 6 +++--- src/Spatial_searching/include/gudhi/Kd_tree_search.h | 2 +- src/Spatial_searching/test/test_Kd_tree_search.cpp | 4 ++-- 4 files changed, 7 insertions(+), 7 deletions(-) (limited to 'src/Spatial_searching') diff --git a/src/Spatial_searching/doc/Intro_spatial_searching.h b/src/Spatial_searching/doc/Intro_spatial_searching.h index 9a3c1b65..22652ac4 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 radius search, and queries for nearest and farthest points using different methods. + * This example generates 500 random points, then performs near search, and queries for nearest and farthest points 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 9e6a8f32..201b589e 100644 --- a/src/Spatial_searching/example/example_spatial_searching.cpp +++ b/src/Spatial_searching/example/example_spatial_searching.cpp @@ -48,10 +48,10 @@ int main(void) { for (auto ifs_iterator = ifn_range.begin(); ifs_iterator->first != 0; ++ifs_iterator) std::cout << ifs_iterator->first << " (sq. dist. = " << ifs_iterator->second << ")\n"; - // Radius search - std::cout << "Radius search:\n"; + // Near search + std::cout << "Near search:\n"; std::vector rs_result; - points_ds.radius_search(points[45], 0.5, std::back_inserter(rs_result)); + points_ds.near_search(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 f13a98f7..a4385c84 100644 --- a/src/Spatial_searching/include/gudhi/Kd_tree_search.h +++ b/src/Spatial_searching/include/gudhi/Kd_tree_search.h @@ -264,7 +264,7 @@ class Kd_tree_search { /// Note: `it` is used this way: `*it++ = each_point`. /// @param[in] eps Approximation factor. template - void radius_search( + void near_search( Point const& p, FT radius, OutputIterator it, diff --git a/src/Spatial_searching/test/test_Kd_tree_search.cpp b/src/Spatial_searching/test/test_Kd_tree_search.cpp index f79114bc..663a103a 100644 --- a/src/Spatial_searching/test/test_Kd_tree_search.cpp +++ b/src/Spatial_searching/test/test_Kd_tree_search.cpp @@ -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 radius search + // Test near search 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 rs_result; - points_ds.radius_search(rs_q, 0.5, std::back_inserter(rs_result)); + points_ds.near_search(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); -- cgit v1.2.3