merged trunk to the branch

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/relaxed-witness@1822 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: c909312a3141cea6bbfaab670be873f7e1c4957a

6 files changed, 90 insertions, 30 deletions

diff --git a/data/points/COIL_database/README b/data/points/COIL_database/README
index 70e013d7..32d059cf 100644
--- a/data/points/COIL_database/README
+++ b/data/points/COIL_database/README
@@ -1,5 +1,7 @@
 The datasets in this folder come from the Columbia University Image Library (COIL-100) dataset.
 
+Ambient dimension is 16384 (128*128)
+
 References:
 
 http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 40c7808d..9b45c640 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -60,10 +60,15 @@ void choose_n_farthest_points(Kernel const &k,
                               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);
+  if (final_size > nb_points)
+    final_size = nb_points;
+
+  // Tests to the limit
+  if (final_size < 1)
+    return;
+
+  typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
 
   std::size_t current_number_of_landmarks = 0;  // counter for landmarks
   const double infty = std::numeric_limits<double>::infinity();  // infinity (see next entry)
@@ -107,11 +112,16 @@ void choose_n_farthest_points(Kernel const& k,
                               Point_container const &input_pts,
                               unsigned final_size,
                               OutputIterator output_it) {
+  // Tests to the limit
+  if ((final_size < 1) || (input_pts.size() == 0))
+    return;
+
   // 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);
+  std::uniform_int_distribution<> dis(0, (input_pts.size() - 1));
+  std::size_t starting_point = dis(gen);
+
   choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
 }
 
diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp
index d064899a..0bc0dff4 100644
--- a/src/Subsampling/test/test_choose_n_farthest_points.cpp
+++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp
@@ -39,18 +39,65 @@ typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K;
 typedef typename K::FT FT;
 typedef typename K::Point_d Point_d;
 
-BOOST_AUTO_TEST_CASE(test_choose_farthest_point) {
+typedef boost::mpl::list<CGAL::Epick_d<CGAL::Dynamic_dimension_tag>, CGAL::Epick_d<CGAL::Dimension_tag<4>>> list_of_tested_kernels;
+
+BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point, Kernel, list_of_tested_kernels) {
+  typedef typename Kernel::FT FT;
+  typedef typename Kernel::Point_d Point_d;
   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})));
+        for (FT l = 0; l < 5; l += 1.0) {
+          std::vector<FT> point({i, j, k, l});
+          points.push_back(Point_d(point.begin(), point.end()));
+        }
 
   landmarks.clear();
-  K k;
+  Kernel k;
   Gudhi::subsampling::choose_n_farthest_points(k, points, 100, std::back_inserter(landmarks));
 
   BOOST_CHECK(landmarks.size() == 100);
+  for (auto landmark : landmarks)
+  {
+    // Check all landmarks are in points
+    BOOST_CHECK(std::find (points.begin(), points.end(), landmark) != points.end());
+  }
+}
+
+BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point_limits, Kernel, list_of_tested_kernels) {
+  typedef typename Kernel::FT FT;
+  typedef typename Kernel::Point_d Point_d;
+  std::vector< Point_d > points, landmarks;
+  landmarks.clear();
+  Kernel k;
+  // Choose -1 farthest points in an empty point cloud
+  Gudhi::subsampling::choose_n_farthest_points(k, points, -1, std::back_inserter(landmarks));
+  BOOST_CHECK(landmarks.size() == 0);
+  landmarks.clear();
+  // Choose 0 farthest points in an empty point cloud
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 0, std::back_inserter(landmarks));
+  BOOST_CHECK(landmarks.size() == 0);
+  landmarks.clear();
+  // Choose 1 farthest points in an empty point cloud
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 1, std::back_inserter(landmarks));
+  BOOST_CHECK(landmarks.size() == 0);
+  landmarks.clear();
+
+  std::vector<FT> point({0.0, 0.0, 0.0, 0.0});
+  points.push_back(Point_d(point.begin(), point.end()));
+  // Choose -1 farthest points in an empty point cloud
+  Gudhi::subsampling::choose_n_farthest_points(k, points, -1, std::back_inserter(landmarks));
+  BOOST_CHECK(landmarks.size() == 1);
+  landmarks.clear();
+  // Choose 0 farthest points in a one point cloud
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 0, std::back_inserter(landmarks));
+  BOOST_CHECK(landmarks.size() == 0);
+  landmarks.clear();
+  // Choose 1 farthest points in a one point cloud
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 1, std::back_inserter(landmarks));
+  BOOST_CHECK(landmarks.size() == 1);
+  landmarks.clear();
+
 }
diff --git a/src/Tangential_complex/benchmark/CMakeLists.txt b/src/Tangential_complex/benchmark/CMakeLists.txt
index a217d6e6..56dd8128 100644
--- a/src/Tangential_complex/benchmark/CMakeLists.txt
+++ b/src/Tangential_complex/benchmark/CMakeLists.txt
@@ -16,7 +16,7 @@ if(CGAL_FOUND)
     if (EIGEN3_FOUND)
       add_executable(Tangential_complex_benchmark benchmark_tc.cpp)
       target_link_libraries(Tangential_complex_benchmark 
-		${Boost_DATE_TIME_LIBRARY} ${Boost_SYSTEM_LIBRARY} ${CGAL_LIBRARY})
+        ${Boost_DATE_TIME_LIBRARY} ${Boost_SYSTEM_LIBRARY} ${CGAL_LIBRARY})
       if (TBB_FOUND)
         target_link_libraries(Tangential_complex_benchmark ${TBB_LIBRARIES})
       endif(TBB_FOUND)
diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h
index 7cf5c498..e748d3b7 100644
--- a/src/Tangential_complex/include/gudhi/Tangential_complex.h
+++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h
@@ -121,11 +121,12 @@ class Vertex_data {
  * \tparam Triangulation_ is the type used for storing the local regular triangulations. We highly recommend to use the default value (`CGAL::Regular_triangulation`).
  *
  */
-template <
-typename Kernel_,  // ambiant kernel
-typename DimensionTag,  // intrinsic dimension
-typename Concurrency_tag = CGAL::Parallel_tag,
-typename Triangulation_ = CGAL::Default
+template
+<
+  typename Kernel_,  // ambiant kernel
+  typename DimensionTag,  // intrinsic dimension
+  typename Concurrency_tag = CGAL::Parallel_tag,
+  typename Triangulation_ = CGAL::Default
 >
 class Tangential_complex {
   typedef Kernel_ K;
@@ -136,19 +137,20 @@ class Tangential_complex {
 
   typedef typename CGAL::Default::Get
   <
-  Triangulation_,
-  CGAL::Regular_triangulation
-  <
-  CGAL::Epick_d<DimensionTag>,
-  CGAL::Triangulation_data_structure
-  <
-  typename CGAL::Epick_d<DimensionTag>::Dimension,
-  CGAL::Triangulation_vertex<CGAL::Regular_triangulation_traits_adapter<
-  CGAL::Epick_d<DimensionTag> >, Vertex_data >,
-  CGAL::Triangulation_full_cell<CGAL::Regular_triangulation_traits_adapter<
-  CGAL::Epick_d<DimensionTag> > >
-  >
-  >
+    Triangulation_,
+    CGAL::Regular_triangulation
+    <
+      CGAL::Epick_d<DimensionTag>,
+      CGAL::Triangulation_data_structure
+      <
+        typename CGAL::Epick_d<DimensionTag>::Dimension,
+        CGAL::Triangulation_vertex
+        <
+          CGAL::Regular_triangulation_traits_adapter< CGAL::Epick_d<DimensionTag> >, Vertex_data
+        >,
+        CGAL::Triangulation_full_cell<CGAL::Regular_triangulation_traits_adapter< CGAL::Epick_d<DimensionTag> > >
+      >
+    >
   >::type Triangulation;
   typedef typename Triangulation::Geom_traits Tr_traits;
   typedef typename Triangulation::Weighted_point Tr_point;
diff --git a/src/Tangential_complex/include/gudhi/Tangential_complex/config.h b/src/Tangential_complex/include/gudhi/Tangential_complex/config.h
index 98a1b14f..ffefcd6b 100644
--- a/src/Tangential_complex/include/gudhi/Tangential_complex/config.h
+++ b/src/Tangential_complex/include/gudhi/Tangential_complex/config.h
@@ -26,8 +26,7 @@
 #include <cstddef>
 
 // ========================= Debugging & profiling =============================
-#define GUDHI_TC_PROFILING
-#define DEBUG_TRACES
+// #define GUDHI_TC_PROFILING
 // #define GUDHI_TC_VERY_VERBOSE
 // #define GUDHI_TC_PERFORM_EXTRA_CHECKS
 // #define GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES