From 8d7329f3e5ad843e553c3c5503cecc28ef2eead6 Mon Sep 17 00:00:00 2001
From: Gard Spreemann <gspreemann@gmail.com>
Date: Thu, 20 Apr 2017 11:10:45 +0200
Subject: GUDHI 2.0.0 as released by upstream in a tarball.

---
 include/gudhi/sparsify_point_set.h | 113 +++++++++++++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 include/gudhi/sparsify_point_set.h

(limited to 'include/gudhi/sparsify_point_set.h')

diff --git a/include/gudhi/sparsify_point_set.h b/include/gudhi/sparsify_point_set.h
new file mode 100644
index 00000000..507f8c79
--- /dev/null
+++ b/include/gudhi/sparsify_point_set.h
@@ -0,0 +1,113 @@
+/*    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_
-- 
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