Took into account Clément's remarks

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1328 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 6f1db13d0e8cec8e64c5bb30da53b72b00446de3

2 files changed, 28 insertions, 25 deletions

diff --git a/src/Subsampling/include/gudhi/choose_by_farthest_point.h b/src/Subsampling/include/gudhi/choose_by_farthest_point.h
index 2918983f..52647c16 100644
--- a/src/Subsampling/include/gudhi/choose_by_farthest_point.h
+++ b/src/Subsampling/include/gudhi/choose_by_farthest_point.h
@@ -31,32 +31,29 @@
 #include <random>
 
 namespace Gudhi {
-  
+
+namespace subsampling {
   /** 
-   *  \ingroup witness_complex
-   *  \brief Landmark choice strategy by iteratively adding the farthest witness from the
-   *  current landmark set as the new landmark. 
-   *  \details It chooses nbL landmarks from a random access range `points` and
-   *  writes {witness}*{closest landmarks} matrix in `knn`.
-   *
-   *  The type KNearestNeighbors can be seen as 
-   *  Witness_range<Closest_landmark_range<Vertex_handle>>, where
-   *  Witness_range and Closest_landmark_range are random access ranges 
+   *  \ingroup subsampling
+   *  \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
+   *  current chosen point set to the subsampling. 
+   *  \details It chooses `final_size` points from a random access range `points` and
+   *  outputs it in the output iterator `output_it`.
    *  
    */
 
   template < typename Kernel,
              typename Point_container,
              typename OutputIterator>
-  void choose_by_farthest_point(Kernel& k,
-                                Point_container const &points,
-                                int nbL,
-                                OutputIterator output_it)
+  void choose_by_farthest_point( Kernel& k,
+                                 Point_container const &points,
+                                 int final_size,
+                                 OutputIterator output_it)
   {
     typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
     
     int nb_points = boost::size(points);
-    assert(nb_points >= nbL);
+    assert(nb_points >= final_size);
 
     int current_number_of_landmarks = 0;  // counter for landmarks
     double curr_max_dist = 0;  // used for defining the furhest point from L
@@ -69,7 +66,7 @@ namespace Gudhi {
     std::uniform_int_distribution<> dis(1, 6);
     int curr_max_w = dis(gen);
 
-    for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
+    for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) {
       // curr_max_w at this point is the next landmark
       *output_it++ = points[curr_max_w];
       // std::cout << curr_max_w << "\n";
@@ -89,6 +86,8 @@ namespace Gudhi {
         }
     }
   }
+
+} // namespace subsampling
   
 }  // namespace Gudhi
 
diff --git a/src/Subsampling/include/gudhi/Pick_random_points.h b/src/Subsampling/include/gudhi/pick_random_points.h
index 98902264..732ae3f7 100644
--- a/src/Subsampling/include/gudhi/Pick_random_points.h
+++ b/src/Subsampling/include/gudhi/pick_random_points.h
@@ -34,26 +34,28 @@
 
 namespace Gudhi {
 
+namespace subsampling {
+  
   /**
-   *  \ingroup witness_complex
-   * \brief Landmark choice strategy by taking random vertices for landmarks.
+   *  \ingroup subsampling
+   * \brief Subsample a point set by picking random vertices.
    *
-   *  \details It chooses nbL distinct landmarks from a random access range `points`
-   *  and outputs them to an output iterator.
+   *  \details It chooses `final_size` distinct points from a random access range `points`
+   *  and outputs them to the output iterator `output_it`.
    *  Point_container::iterator should be ValueSwappable and RandomAccessIterator.
    */
 
   template <typename Point_container,
             typename OutputIterator>
   void pick_random_points(Point_container const &points,
-                          unsigned nbL,
+                          unsigned final_size,
                           OutputIterator output_it) {
-#ifdef GUDHI_LM_PROFILING
+#ifdef GUDHI_SUBS_PROFILING
     Gudhi::Clock t;
 #endif
 
     unsigned nbP = boost::size(points);
-    assert(nbP >= nbL);
+    assert(nbP >= final_size);
     std::vector<int> landmarks(nbP);
     std::iota(landmarks.begin(), landmarks.end(), 0);
 
@@ -61,18 +63,20 @@ namespace Gudhi {
     std::mt19937 g(rd());
  
     std::shuffle(landmarks.begin(), landmarks.end(), g);
-    landmarks.resize(nbL);
+    landmarks.resize(final_size);
 
     for (int l: landmarks)
       *output_it++ = points[l];
     
-#ifdef GUDHI_LM_PROFILING
+#ifdef GUDHI_SUBS_PROFILING
     t.end();
     std::cerr << "Random landmark choice took " << t.num_seconds()
       << " seconds." << std::endl;
 #endif
   }
 
+} // namesapce subsampling
+  
 }  // namespace Gudhi
 
 #endif  // PICK_RANDOM_POINTS_H_