Removed headers in the examples

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1339 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 835003033dc15ce8fd2d8efdc7bfdf3e0be7760d

1 files changed, 16 insertions, 86 deletions

diff --git a/src/Subsampling/include/gudhi/choose_by_farthest_point.h b/src/Subsampling/include/gudhi/choose_by_farthest_point.h
index d1db0d1a..8dea19be 100644
--- a/src/Subsampling/include/gudhi/choose_by_farthest_point.h
+++ b/src/Subsampling/include/gudhi/choose_by_farthest_point.h
@@ -45,6 +45,7 @@ namespace subsampling {
    *  \ingroup 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
    *  outputs it in the output iterator `output_it`.
    *  
@@ -53,11 +54,11 @@ namespace subsampling {
   template < typename Kernel,
              typename Point_container,
              typename OutputIterator>
-  void choose_by_farthest_point_old( Kernel& k,
-                                     Point_container const &points,
-                                     int final_size,
-                                     int starting_point,
-                                     OutputIterator output_it)
+  void choose_by_farthest_point( Kernel& k,
+                                 Point_container const &points,
+                                 int final_size,
+                                 int starting_point,
+                                 OutputIterator output_it)
   {
     typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
     
@@ -92,101 +93,30 @@ namespace subsampling {
     }
   }
   
-  template < typename Kernel,
-             typename Point_container,
-             typename OutputIterator>
-  void choose_by_farthest_point_old( Kernel& k,
-                                     Point_container const &points,
-                                     int final_size,
-                                     OutputIterator output_it)
-  {
-    // 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);
-    choose_by_farthest_point_old(k, points, final_size, starting_point, output_it);
-  }
-
+  /** 
+   *  \ingroup 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
+   *  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 final_size,
-                                 int starting_point,
                                  OutputIterator output_it)
   {
-    // typedef typename Kernel::Point_d Point_d;
-    // typedef typename Kernel::FT FT;
-    // typedef CGAL::Search_traits<
-    //   FT, Point_d,
-    //   typename Kernel::Cartesian_const_iterator_d,
-    //   typename Kernel::Construct_cartesian_const_iterator_d> Traits_base;
-
-    // typedef CGAL::Search_traits_adapter< std::ptrdiff_t, Point_d*, Traits_base > STraits;
-    // typedef CGAL::Fuzzy_sphere< STraits > Fuzzy_sphere;
-    
-    typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
-    
-    int nb_points = boost::size(points);
-    assert(nb_points >= final_size);
-
-    Clock t;
-    Gudhi::spatial_searching::Spatial_tree_data_structure< Kernel, Point_container> tree(points);
-    t.end();
-    //std::cout << "Constructed the Kd tree: " << t.num_seconds()  << " s." << std::endl;
-  
-    //CGAL::Fuzzy_sphere< CGAL::Search_trai>
-    
-    int current_number_of_landmarks = 0;  // counter for landmarks
-    const double infty = std::numeric_limits<double>::infinity();  // infinity (see next entry)
-    double curr_max_dist = infty;  // used for defining the furhest point from L
-    std::vector< double > dist_to_L(nb_points, infty);  // vector of current distances to L from points
-    
-    // Choose randomly the first landmark 
-    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];
-      // std::cout << curr_max_w << "\n";
-      //for (auto& p : points) {
-      auto search = tree.query_incremental_ANN(points[curr_max_w]);
-      auto search_it = search.begin();
-      while (search_it != search.end() && search_it->second <= curr_max_dist ) {
-        //std::cout << search_it->second << " " << curr_max_dist << "\n";
-        if (dist_to_L[search_it->first] > search_it->second)
-          dist_to_L[search_it->first] = search_it->second;
-        search_it++;
-      }
-      // choose the next curr_max_w
-      curr_max_dist = 0;
-      for (unsigned i = 0; i < dist_to_L.size(); i++)
-        if (dist_to_L[i] > curr_max_dist) {
-          curr_max_dist = dist_to_L[i];
-          curr_max_w = i;
-        }
-    }
-  }
-  
-  template < typename Kernel,
-             typename Point_container,
-             typename OutputIterator>
-  void choose_by_farthest_point( Kernel& k,
-                                 Point_container const &points,
-                                 int final_size,
-                                 OutputIterator output_it)
-  {    
     // 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);
-
-    choose_by_farthest_point_old(k, points, final_size, starting_point, output_it);
+    choose_by_farthest_point(k, points, final_size, starting_point, output_it);
   }
-
   
 } // namespace subsampling