Improved the documentation for choose_farthest_points

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1869 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 08223b7d1788c73b0fb3fc7255a6386896b63626

1 files changed, 27 insertions, 4 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..43bf6402 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -48,15 +48,27 @@ 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.
+ *  \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) {
@@ -96,15 +108,26 @@ 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.
+ *  \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) {
   // Choose randomly the first landmark