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-rw-r--r--src/Subsampling/include/gudhi/sparsify_point_set.h33
1 files changed, 13 insertions, 20 deletions
diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h
index b30cec80..4571b8f3 100644
--- a/src/Subsampling/include/gudhi/sparsify_point_set.h
+++ b/src/Subsampling/include/gudhi/sparsify_point_set.h
@@ -11,6 +11,13 @@
#ifndef SPARSIFY_POINT_SET_H_
#define SPARSIFY_POINT_SET_H_
+#include <boost/version.hpp>
+#if BOOST_VERSION < 106600
+# include <boost/function_output_iterator.hpp>
+#else
+# include <boost/iterator/function_output_iterator.hpp>
+#endif
+
#include <gudhi/Kd_tree_search.h>
#ifdef GUDHI_SUBSAMPLING_PROFILING
#include <gudhi/Clock.h>
@@ -27,7 +34,7 @@ namespace subsampling {
* \ingroup subsampling
* \brief Outputs a subset of the input points so that the
* squared distance between any two points
- * is greater than or equal to `min_squared_dist`.
+ * is greater than `min_squared_dist`.
*
* \tparam Kernel must be a model of the <a target="_blank"
* href="http://doc.cgal.org/latest/Spatial_searching/classSearchTraits.html">SearchTraits</a>
@@ -63,29 +70,15 @@ 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) {
- if (dropped_points[pt_idx])
+ for (auto const& pt : input_pts) {
+ if (dropped_points[pt_idx++])
continue;
- *output_it++ = *it_pt;
-
- auto ins_range = points_ds.incremental_nearest_neighbors(*it_pt);
+ *output_it++ = pt;
// If another point Q is closer that min_squared_dist, mark Q to be dropped
- for (auto const& neighbor : ins_range) {
- std::size_t neighbor_point_idx = neighbor.first;
- // If the neighbor is too close, we drop the neighbor
- if (neighbor.second < min_squared_dist) {
- // N.B.: If neighbor_point_idx < pt_idx,
- // dropped_points[neighbor_point_idx] is already true but adding a
- // test doesn't make things faster, so why bother?
- dropped_points[neighbor_point_idx] = true;
- } else {
- break;
- }
- }
+ auto drop = [&dropped_points] (std::ptrdiff_t neighbor_point_idx) { dropped_points[neighbor_point_idx] = true; };
+ points_ds.all_near_neighbors2(pt, min_squared_dist, min_squared_dist, boost::make_function_output_iterator(std::ref(drop)));
}
#ifdef GUDHI_SUBSAMPLING_PROFILING