diff options
Diffstat (limited to 'src/Subsampling/include/gudhi/sparsify_point_set.h')
-rw-r--r-- | src/Subsampling/include/gudhi/sparsify_point_set.h | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h index edb9869b..7ff11b4c 100644 --- a/src/Subsampling/include/gudhi/sparsify_point_set.h +++ b/src/Subsampling/include/gudhi/sparsify_point_set.h @@ -32,6 +32,7 @@ #include <vector> namespace Gudhi { + namespace subsampling { /** @@ -54,15 +55,14 @@ namespace subsampling { * @param[in] min_squared_dist Minimum squared distance separating the output points. * @param[out] output_it The output iterator. */ - template <typename Kernel, typename Point_range, typename OutputIterator> void sparsify_point_set( - const Kernel &k, Point_range const& input_pts, - typename Kernel::FT min_squared_dist, - OutputIterator output_it) { + const Kernel &k, Point_range const& input_pts, + typename Kernel::FT min_squared_dist, + OutputIterator output_it) { typedef typename Gudhi::spatial_searching::Kd_tree_search< - Kernel, Point_range> Points_ds; + Kernel, Point_range> Points_ds; typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); @@ -77,9 +77,9 @@ sparsify_point_set( // Parse the input points, and add them if they are not too close to // the other points std::size_t pt_idx = 0; - for (typename Point_range::const_iterator it_pt = input_pts.begin() ; - it_pt != input_pts.end(); - ++it_pt, ++pt_idx) { + for (typename Point_range::const_iterator it_pt = input_pts.begin(); + it_pt != input_pts.end(); + ++it_pt, ++pt_idx) { if (dropped_points[pt_idx]) continue; @@ -105,7 +105,7 @@ sparsify_point_set( #ifdef GUDHI_SUBSAMPLING_PROFILING t.end(); std::cerr << "Point set sparsified in " << t.num_seconds() - << " seconds." << std::endl; + << " seconds." << std::endl; #endif } |