1 files changed, 44 insertions, 9 deletions

diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 40c7808d..5e908090 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -48,22 +48,40 @@ namespace subsampling {
  *  \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
  *  current chosen point set to the subsampling. 
  *  The iteration starts with the landmark `starting point`.
+ *  \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the 
+ *          concept <a target="_blank"
+ *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a>
+ *   concept.
+ *  It must also contain a public member 'squared_distance_d_object' of this type.
+ *  \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.
  *  \details It chooses `final_size` points from a random access range `input_pts` and
  *  outputs it in the output iterator `output_it`.
+ * @param[in] k A kernel object.
+ * @param[in] input_pts Const reference to the input points.
+ * @param[in] final_size The size of the subsample to compute.
+ * @param[in] starting_point The seed in the farthest point algorithm.
+ * @param[out] output_it The output iterator.
  *  
  */
 template < typename Kernel,
-typename Point_container,
+typename Point_range,
 typename OutputIterator>
 void choose_n_farthest_points(Kernel const &k,
-                              Point_container const &input_pts,
+                              Point_range const &input_pts,
                               std::size_t final_size,
                               std::size_t starting_point,
                               OutputIterator output_it) {
-  typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
-
   std::size_t nb_points = boost::size(input_pts);
-  assert(nb_points >= final_size);
+  if (final_size > nb_points)
+    final_size = nb_points;
+
+  // Tests to the limit
+  if (final_size < 1)
+    return;
+
+  typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
 
   std::size_t current_number_of_landmarks = 0;  // counter for landmarks
   const double infty = std::numeric_limits<double>::infinity();  // infinity (see next entry)
@@ -96,22 +114,39 @@ void choose_n_farthest_points(Kernel const &k,
  *  \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
  *  current chosen point set to the subsampling. 
  *  The iteration starts with a random landmark.
+ *  \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the 
+ *          concept <a target="_blank"
+ *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a>
+ *   concept.
+ *  It must also contain a public member 'squared_distance_d_object' of this type.
+ *  \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.
  *  \details It chooses `final_size` points from a random access range `input_pts` and
  *  outputs it in the output iterator `output_it`.
+ * @param[in] k A kernel object.
+ * @param[in] input_pts Const reference to the input points.
+ * @param[in] final_size The size of the subsample to compute.
+ * @param[out] output_it The output iterator.
  *  
  */
 template < typename Kernel,
-typename Point_container,
+typename Point_range,
 typename OutputIterator>
 void choose_n_farthest_points(Kernel const& k,
-                              Point_container const &input_pts,
+                              Point_range const &input_pts,
                               unsigned final_size,
                               OutputIterator output_it) {
+  // Tests to the limit
+  if ((final_size < 1) || (input_pts.size() == 0))
+    return;
+
   // Choose randomly the first landmark
   std::random_device rd;
   std::mt19937 gen(rd());
-  std::uniform_int_distribution<> dis(1, 6);
-  int starting_point = dis(gen);
+  std::uniform_int_distribution<> dis(0, (input_pts.size() - 1));
+  std::size_t starting_point = dis(gen);
+
   choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
 }