fixcpplint

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1667 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: c6974fc881cae681326777d1e6f8bd7f475f235e

3 files changed, 111 insertions, 113 deletions

diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index a4719f56..60816a29 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -37,89 +37,88 @@
 #include <algorithm>  // for sort
 #include <vector>
 #include <random>
+#include <limits>  // for numeric_limits<>
 
 namespace Gudhi {
 
 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 `input_pts` and
-   *  outputs it in the output iterator `output_it`.
-   *  
-   */
-
-  template < typename Kernel,
-             typename Point_container,
-             typename OutputIterator>
-  void choose_n_farthest_points( Kernel const &k,
-                                 Point_container const &input_pts,
-                                 std::size_t final_size,
-                                 std::size_t starting_point,
-                                 OutputIterator output_it)
-  {
-    typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
-    
-    std::size_t nb_points = boost::size(input_pts);
-    assert(nb_points >= final_size);
-
-    std::size_t 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 input_pts
-
-    std::size_t 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++ = input_pts[curr_max_w];
-      // std::cout << curr_max_w << "\n";
-      std::size_t i = 0;
-      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;
-      }
-      // choose the next curr_max_w
-      curr_max_dist = 0;
-      for (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;
-        }
+
+/** 
+ *  \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 `input_pts` and
+ *  outputs it in the output iterator `output_it`.
+ *  
+ */
+template < typename Kernel,
+typename Point_container,
+typename OutputIterator>
+void choose_n_farthest_points(Kernel const &k,
+                              Point_container const &input_pts,
+                              std::size_t final_size,
+                              std::size_t starting_point,
+                              OutputIterator output_it) {
+  typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
+
+  std::size_t nb_points = boost::size(input_pts);
+  assert(nb_points >= final_size);
+
+  std::size_t 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 input_pts
+
+  std::size_t 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++ = input_pts[curr_max_w];
+    // std::cout << curr_max_w << "\n";
+    std::size_t i = 0;
+    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;
     }
+    // choose the next curr_max_w
+    curr_max_dist = 0;
+    for (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;
+      }
   }
-  
-  /** 
-   *  \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 `input_pts` and
-   *  outputs it in the output iterator `output_it`.
-   *  
-   */
-  template < typename Kernel,
-             typename Point_container,
-             typename OutputIterator>
-  void choose_n_farthest_points( Kernel const& k,
-                                 Point_container const &input_pts,
-                                 unsigned 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_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
-  }
-  
-} // 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 a random landmark.
+ *  \details It chooses `final_size` points from a random access range `input_pts` and
+ *  outputs it in the output iterator `output_it`.
+ *  
+ */
+template < typename Kernel,
+typename Point_container,
+typename OutputIterator>
+void choose_n_farthest_points(Kernel const& k,
+                              Point_container const &input_pts,
+                              unsigned 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_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
+}
+
+}  // namespace subsampling
+
 }  // namespace Gudhi
 
 #endif  // CHOOSE_N_FARTHEST_POINTS_H_
diff --git a/src/Subsampling/include/gudhi/pick_n_random_points.h b/src/Subsampling/include/gudhi/pick_n_random_points.h
index 52842a54..e89b2b2d 100644
--- a/src/Subsampling/include/gudhi/pick_n_random_points.h
+++ b/src/Subsampling/include/gudhi/pick_n_random_points.h
@@ -38,45 +38,44 @@
 namespace Gudhi {
 
 namespace subsampling {
-  
-  /**
-   *  \ingroup subsampling
-   * \brief Subsample a point set by picking random vertices.
-   *
-   *  \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_n_random_points(Point_container const &points,
+
+/**
+ *  \ingroup subsampling
+ * \brief Subsample a point set by picking random vertices.
+ *
+ *  \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_n_random_points(Point_container const &points,
                           std::size_t final_size,
                           OutputIterator output_it) {
 #ifdef GUDHI_SUBS_PROFILING
-    Gudhi::Clock t;
+  Gudhi::Clock t;
 #endif
 
-    std::size_t nbP = boost::size(points);
-    assert(nbP >= final_size);
-    std::vector<int> landmarks(nbP);
-    std::iota(landmarks.begin(), landmarks.end(), 0);
+  std::size_t nbP = boost::size(points);
+  assert(nbP >= final_size);
+  std::vector<int> landmarks(nbP);
+  std::iota(landmarks.begin(), landmarks.end(), 0);
 
-    std::random_device rd;
-    std::mt19937 g(rd());
+  std::random_device rd;
+  std::mt19937 g(rd());
 
-    std::shuffle(landmarks.begin(), landmarks.end(), g);
-    landmarks.resize(final_size);
+  std::shuffle(landmarks.begin(), landmarks.end(), g);
+  landmarks.resize(final_size);
 
-    for (int l: landmarks)
-      *output_it++ = points[l];
+  for (int l : landmarks)
+    *output_it++ = points[l];
 
 #ifdef GUDHI_SUBS_PROFILING
-    t.end();
-    std::cerr << "Random landmark choice took " << t.num_seconds()
+  t.end();
+  std::cerr << "Random landmark choice took " << t.num_seconds()
       << " seconds." << std::endl;
 #endif
-  }
+}
 
 }  // namespace subsampling
 
diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h
index edb9869b..7ff11b4c 100644
--- a/src/Subsampling/include/gudhi/sparsify_point_set.h
+++ b/src/Subsampling/include/gudhi/sparsify_point_set.h
@@ -32,6 +32,7 @@
 #include <vector>
 
 namespace Gudhi {
+
 namespace subsampling {
 
 /**
@@ -54,15 +55,14 @@ namespace subsampling {
  * @param[in] min_squared_dist Minimum squared distance separating the output points.
  * @param[out] output_it The output iterator.
  */
-
 template <typename Kernel, typename Point_range, typename OutputIterator>
 void
 sparsify_point_set(
-  const Kernel &k, Point_range const& input_pts,
-  typename Kernel::FT min_squared_dist,
-  OutputIterator output_it) { 
+                   const Kernel &k, Point_range const& input_pts,
+                   typename Kernel::FT min_squared_dist,
+                   OutputIterator output_it) {
   typedef typename Gudhi::spatial_searching::Kd_tree_search<
-    Kernel, Point_range>  Points_ds;
+      Kernel, Point_range> Points_ds;
 
   typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
 
@@ -77,9 +77,9 @@ sparsify_point_set(
   // Parse the input points, and add them if they are not too close to
   // the other points
   std::size_t pt_idx = 0;
-  for (typename Point_range::const_iterator it_pt = input_pts.begin() ;
-    it_pt != input_pts.end();
-    ++it_pt, ++pt_idx) {
+  for (typename Point_range::const_iterator it_pt = input_pts.begin();
+       it_pt != input_pts.end();
+       ++it_pt, ++pt_idx) {
     if (dropped_points[pt_idx])
       continue;
 
@@ -105,7 +105,7 @@ sparsify_point_set(
 #ifdef GUDHI_SUBSAMPLING_PROFILING
   t.end();
   std::cerr << "Point set sparsified in " << t.num_seconds()
-    << " seconds." << std::endl;
+      << " seconds." << std::endl;
 #endif
 }