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+/* 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/>.
+ */
+
+#ifndef SPARSIFY_POINT_SET_H_
+#define SPARSIFY_POINT_SET_H_
+
+#include <gudhi/Kd_tree_search.h>
+#ifdef GUDHI_SUBSAMPLING_PROFILING
+#include <gudhi/Clock.h>
+#endif
+
+#include <cstddef>
+#include <vector>
+
+namespace Gudhi {
+
+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`.
+ *
+ * \tparam Kernel must be a model of the <a target="_blank"
+ * href="http://doc.cgal.org/latest/Spatial_searching/classSearchTraits.html">SearchTraits</a>
+ * concept, such as the <a target="_blank"
+ * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class, which
+ * can be static if you know the ambiant dimension at compile-time, or dynamic if you don't.
+ * \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access
+ * via `operator[]` and the points should be stored contiguously in memory.
+ * \tparam OutputIterator Output iterator whose value type is Kernel::Point_d.
+ *
+ * @param[in] k A kernel object.
+ * @param[in] input_pts Const reference to the input points.
+ * @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) {
+ typedef typename Gudhi::spatial_searching::Kd_tree_search<
+ Kernel, Point_range> Points_ds;
+
+#ifdef GUDHI_SUBSAMPLING_PROFILING
+ Gudhi::Clock t;
+#endif
+
+ Points_ds points_ds(input_pts);
+
+ std::vector<bool> dropped_points(input_pts.size(), false);
+
+ // 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])
+ continue;
+
+ *output_it++ = *it_pt;
+
+ auto ins_range = points_ds.query_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) {
+ 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;
+ }
+ }
+ }
+
+#ifdef GUDHI_SUBSAMPLING_PROFILING
+ t.end();
+ std::cerr << "Point set sparsified in " << t.num_seconds()
+ << " seconds." << std::endl;
+#endif
+}
+
+} // namespace subsampling
+} // namespace Gudhi
+
+#endif // SPARSIFY_POINT_SET_H_