Mark comment #1 + Vincent comment #1

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1472 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: e9a2fb8d8f9e11c60326203c7e0e46cdb2b12435

1 files changed, 14 insertions, 14 deletions

diff --git a/src/Subsampling/include/gudhi/choose_by_farthest_point.h b/src/Subsampling/include/gudhi/choose_by_farthest_point.h
index 8dea19be..b09192d9 100644
--- a/src/Subsampling/include/gudhi/choose_by_farthest_point.h
+++ b/src/Subsampling/include/gudhi/choose_by_farthest_point.h
@@ -46,7 +46,7 @@ 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`.
-   *  \details It chooses `final_size` points from a random access range `points` and
+   *  \details It chooses `final_size` points from a random access range `input_pts` and
    *  outputs it in the output iterator `output_it`.
    *  
    */
@@ -54,31 +54,31 @@ namespace subsampling {
   template < typename Kernel,
              typename Point_container,
              typename OutputIterator>
-  void choose_by_farthest_point( Kernel& k,
-                                 Point_container const &points,
-                                 int final_size,
-                                 int starting_point,
+  void choose_by_farthest_point( Kernel const &k,
+                                 Point_container const &input_pts,
+                                 unsigned final_size,
+                                 unsigned starting_point,
                                  OutputIterator output_it)
   {
     typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
     
-    int nb_points = boost::size(points);
+    int nb_points = boost::size(input_pts);
     assert(nb_points >= final_size);
 
-    int current_number_of_landmarks = 0;  // counter for landmarks
+    unsigned current_number_of_landmarks = 0;  // counter for landmarks
     double curr_max_dist = 0;  // used for defining the furhest point from L
     const double infty = std::numeric_limits<double>::infinity();  // infinity (see next entry)
-    std::vector< double > dist_to_L(nb_points, infty);  // vector of current distances to L from points
+    std::vector< double > dist_to_L(nb_points, infty);  // vector of current distances to L from input_pts
 
     int curr_max_w = starting_point;
 
     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];
+      *output_it++ = input_pts[curr_max_w];
       // std::cout << curr_max_w << "\n";
       unsigned i = 0;
-      for (auto& p : points) {
-        double curr_dist = sqdist(p, *(std::begin(points) + curr_max_w));
+      for (auto& p : input_pts) {
+        double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w));
         if (curr_dist < dist_to_L[i])
           dist_to_L[i] = curr_dist;
         ++i;
@@ -98,7 +98,7 @@ 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 a random landmark.
-   *  \details It chooses `final_size` points from a random access range `points` and
+   *  \details It chooses `final_size` points from a random access range `input_pts` and
    *  outputs it in the output iterator `output_it`.
    *  
    */
@@ -106,7 +106,7 @@ namespace subsampling {
              typename Point_container,
              typename OutputIterator>
   void choose_by_farthest_point( Kernel& k,
-                                 Point_container const &points,
+                                 Point_container const &input_pts,
                                  int final_size,
                                  OutputIterator output_it)
   {
@@ -115,7 +115,7 @@ namespace subsampling {
     std::mt19937 gen(rd());
     std::uniform_int_distribution<> dis(1, 6);
     int starting_point = dis(gen);
-    choose_by_farthest_point(k, points, final_size, starting_point, output_it);
+    choose_by_farthest_point(k, input_pts, final_size, starting_point, output_it);
   }
   
 } // namespace subsampling