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

5 files changed, 38 insertions, 182 deletions

diff --git a/src/Subsampling/doc/Intro_subsampling.h b/src/Subsampling/doc/Intro_subsampling.h
index e62ebc61..5ccab3af 100644
--- a/src/Subsampling/doc/Intro_subsampling.h
+++ b/src/Subsampling/doc/Intro_subsampling.h
@@ -46,6 +46,13 @@ namespace subsampling {
  * 
  * \include Subsampling/example_sparsify_point_set.cpp
  * 
+ * \section farthestpointexamples Example: choose_by_farthest_point
+ *
+ * This example outputs a subset of 100 points obtained by González algorithm,
+ * starting with a random point.
+ *
+ * \include Subsampling/example_choose_by_farthest_point.cpp
+ * 
  * \copyright GNU General Public License v3.                         
  * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
diff --git a/src/Subsampling/example/example_choose_by_farthest_point.cpp b/src/Subsampling/example/example_choose_by_farthest_point.cpp
index 5b81bc9d..29ecbf9b 100644
--- a/src/Subsampling/example/example_choose_by_farthest_point.cpp
+++ b/src/Subsampling/example/example_choose_by_farthest_point.cpp
@@ -1,26 +1,3 @@
-/*    This file is part of the Gudhi Library. The Gudhi library
- *    (Geometric Understanding in Higher Dimensions) is a generic C++
- *    library for computational topology.
- *
- *    Author(s):       Siargey Kachanovich
- *
- *    Copyright (C) 2016 INRIA
- *
- *    This program is free software: you can redistribute it and/or modify
- *    it under the terms of the GNU General Public License as published by
- *    the Free Software Foundation, either version 3 of the License, or
- *    (at your option) any later version.
- *
- *    This program is distributed in the hope that it will be useful,
- *    but WITHOUT ANY WARRANTY; without even the implied warranty of
- *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- *    GNU General Public License for more details.
- *
- *    You should have received a copy of the GNU General Public License
- *    along with this program.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-
 #include <gudhi/choose_by_farthest_point.h>
 
 #include <CGAL/Epick_d.h>
diff --git a/src/Subsampling/example/example_pick_random_points.cpp b/src/Subsampling/example/example_pick_random_points.cpp
index 49a027a4..348b1b81 100644
--- a/src/Subsampling/example/example_pick_random_points.cpp
+++ b/src/Subsampling/example/example_pick_random_points.cpp
@@ -1,26 +1,3 @@
-/*    This file is part of the Gudhi Library. The Gudhi library
- *    (Geometric Understanding in Higher Dimensions) is a generic C++
- *    library for computational topology.
- *
- *    Author(s):       Siargey Kachanovich
- *
- *    Copyright (C) 2016 INRIA
- *
- *    This program is free software: you can redistribute it and/or modify
- *    it under the terms of the GNU General Public License as published by
- *    the Free Software Foundation, either version 3 of the License, or
- *    (at your option) any later version.
- *
- *    This program is distributed in the hope that it will be useful,
- *    but WITHOUT ANY WARRANTY; without even the implied warranty of
- *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- *    GNU General Public License for more details.
- *
- *    You should have received a copy of the GNU General Public License
- *    along with this program.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-
 #include <gudhi/pick_random_points.h>
 
 #include <CGAL/Epick_d.h>
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
   
diff --git a/src/Subsampling/test/test_choose_farthest_point.cpp b/src/Subsampling/test/test_choose_farthest_point.cpp
index dff2cd4e..5f705de6 100644
--- a/src/Subsampling/test/test_choose_farthest_point.cpp
+++ b/src/Subsampling/test/test_choose_farthest_point.cpp
@@ -24,15 +24,14 @@
 // # define TBB_USE_THREADING_TOOL
 // #endif
 
-// #define BOOST_TEST_DYN_LINK
-// #define BOOST_TEST_MODULE "test_choose_farthest_point"
-//#include <boost/test/unit_test.hpp>
-//#include <boost/mpl/list.hpp>
+#define BOOST_TEST_DYN_LINK
+#define BOOST_TEST_MODULE "witness_complex_points"
+#include <boost/test/unit_test.hpp>
+#include <boost/mpl/list.hpp>
 
 #include <gudhi/choose_by_farthest_point.h>
 #include <vector>
 #include <iterator>
-#include <gudhi/Clock.h>
 
 #include <CGAL/Epick_d.h>
 
@@ -41,52 +40,18 @@ typedef typename K::FT                                            FT;
 typedef typename K::Point_d                                       Point_d;
 
 
-//BOOST_AUTO_TEST_CASE(test_choose_farthest_point)
-int main() {
-  std::vector< Point_d > points, results, results2;
-  K k;
-  Clock t;
-  // Add grid points (810000 points)
-  for (FT i = 0; i < 30; i += 1.0)
-    for (FT j = 0; j < 30; j += 1.0)
-      for (FT k = 0; k < 30; k += 1.0)
-        for (FT l = 0; l < 30; l += 1.0)
+BOOST_AUTO_TEST_CASE(test_choose_farthest_point) {
+  std::vector< Point_d > points, landmarks;
+  // Add grid points (625 points)
+  for (FT i = 0; i < 5; i += 1.0)
+    for (FT j = 0; j < 5; j += 1.0)
+      for (FT k = 0; k < 5; k += 1.0)
+        for (FT l = 0; l < 5; l += 1.0)
           points.push_back(Point_d(std::vector<FT>({i, j, k, l})));
 
-  unsigned final_size = 100, numeral = 1;
-  std::cout << "Test   New     Old\n";
-  while (final_size < 100001) {
-    std::cout << final_size << ": ";
-    results.clear();
-    t.begin();
-    Gudhi::subsampling::choose_by_farthest_point(k, points, final_size, 0, std::back_inserter(results));
-    t.end();
-    std::cout << t.num_seconds()  << " s, ";
-  
-  // std::cout << "New algorithm result:\n";
-  // for (auto p: results)
-  //   std::cout << p << std::endl;
-  
-    results2.clear();
-    t.begin();
-    Gudhi::subsampling::choose_by_farthest_point_old(k, points, final_size, 0, std::back_inserter(results2));
-    t.end();
-    std::cout << t.num_seconds()  << " s" << std::endl;
-
-  
-  // std::cout << "Old algorithm result:\n";
-  // for (auto p: results2)
-  //   std::cout << p << std::endl;
+  landmarks.clear();
+  K k;
+  Gudhi::subsampling::choose_by_farthest_point(k, points, 100, std::back_inserter(landmarks));
   
-    assert(results.size() == final_size);
-    assert(results2.size() == final_size);
-    assert(results == results2);
-
-    switch (numeral) {
-    case 1: numeral = 2; final_size *= 2; break;
-    case 2: numeral = 5; final_size = final_size/2*5; break;
-    case 5: numeral = 1; final_size *= 2; break;
-    default: assert(false);
-    }
-  }
+  assert(landmarks.size() == 100);
 }