Warning fixes

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@1905 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 7b7222ebe65fd01fde30f8ba527ebaee633fc87e

10 files changed, 63 insertions, 34 deletions

diff --git a/src/Contraction/example/Garland_heckbert.cpp b/src/Contraction/example/Garland_heckbert.cpp
index 5347830c..4689519f 100644
--- a/src/Contraction/example/Garland_heckbert.cpp
+++ b/src/Contraction/example/Garland_heckbert.cpp
@@ -63,12 +63,10 @@ typedef Skeleton_blocker_contractor<Complex> Complex_contractor;
  * the point minimizing the cost of the quadric.
  */
 class GH_placement : public Gudhi::contraction::Placement_policy<EdgeProfile> {
-  Complex& complex_;
-
  public:
   typedef Gudhi::contraction::Placement_policy<EdgeProfile>::Placement_type Placement_type;
 
-  GH_placement(Complex& complex) : complex_(complex) { }
+  GH_placement(Complex& complex) { }
 
   Placement_type operator()(const EdgeProfile& profile) const override {
     auto sum_quad(profile.v0().quadric);
@@ -87,12 +85,10 @@ class GH_placement : public Gudhi::contraction::Placement_policy<EdgeProfile> {
  * which expresses a squared distances with triangles planes.
  */
 class GH_cost : public Gudhi::contraction::Cost_policy<EdgeProfile> {
-  Complex& complex_;
-
  public:
   typedef Gudhi::contraction::Cost_policy<EdgeProfile>::Cost_type Cost_type;
 
-  GH_cost(Complex& complex) : complex_(complex) { }
+  GH_cost(Complex& complex) { }
 
   Cost_type operator()(EdgeProfile const& profile, boost::optional<Point> const& new_point) const override {
     Cost_type res;
@@ -111,10 +107,8 @@ class GH_cost : public Gudhi::contraction::Cost_policy<EdgeProfile> {
  * and we update them when contracting an edge (the quadric become the sum of both quadrics).
  */
 class GH_visitor : public Gudhi::contraction::Contraction_visitor<EdgeProfile> {
-  Complex& complex_;
-
  public:
-  GH_visitor(Complex& complex) : complex_(complex) { }
+  GH_visitor(Complex& complex) { }
 
   // Compute quadrics for every vertex v
   // The quadric of v consists in the sum of quadric
diff --git a/src/GudhUI/utils/Critical_points.h b/src/GudhUI/utils/Critical_points.h
index 3021a5fe..b88293e9 100644
--- a/src/GudhUI/utils/Critical_points.h
+++ b/src/GudhUI/utils/Critical_points.h
@@ -105,8 +105,6 @@ template<typename SkBlComplex> class Critical_points {
     if (link.empty())
       return 0;
 
-    Edge_contractor<Complex> contractor(link, link.num_vertices() - 1);
-
     if (link.num_connected_components() > 1)
       // one than more CC -> not contractible
       return 0;
diff --git a/src/GudhUI/utils/Is_manifold.h b/src/GudhUI/utils/Is_manifold.h
index 0640ea47..6dd7898e 100644
--- a/src/GudhUI/utils/Is_manifold.h
+++ b/src/GudhUI/utils/Is_manifold.h
@@ -76,7 +76,6 @@ template<typename SkBlComplex> class Is_manifold {
 
   bool is_k_sphere(Vertex_handle v, int k) {
     auto link = input_complex_.link(v);
-    Edge_contractor<Complex> contractor(link, link.num_vertices() - 1);
     return (is_sphere_simplex(link) == k);
   }
 
diff --git a/src/GudhUI/utils/Vertex_collapsor.h b/src/GudhUI/utils/Vertex_collapsor.h
index 2b36cb3a..3f0e7ffd 100644
--- a/src/GudhUI/utils/Vertex_collapsor.h
+++ b/src/GudhUI/utils/Vertex_collapsor.h
@@ -80,7 +80,6 @@ template<typename SkBlComplex> class Vertex_collapsor {
     if (link.empty()) return false;
     if (link.is_cone()) return true;
     if (link.num_connected_components() > 1) return false;
-    Edge_contractor<Complex> contractor(link, link.num_vertices() - 1);
     return (link.num_vertices() == 1);
   }
 };
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index ea387bf9..5e908090 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -145,7 +145,8 @@ void choose_n_farthest_points(Kernel const& k,
   std::random_device rd;
   std::mt19937 gen(rd());
   std::uniform_int_distribution<> dis(0, (input_pts.size() - 1));
-  int starting_point = dis(gen);
+  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/include/gudhi/pick_n_random_points.h b/src/Subsampling/include/gudhi/pick_n_random_points.h
index e89b2b2d..f0e3f1f1 100644
--- a/src/Subsampling/include/gudhi/pick_n_random_points.h
+++ b/src/Subsampling/include/gudhi/pick_n_random_points.h
@@ -57,7 +57,9 @@ void pick_n_random_points(Point_container const &points,
 #endif
 
   std::size_t nbP = boost::size(points);
-  assert(nbP >= final_size);
+  if (final_size > nbP)
+      final_size = nbP;
+
   std::vector<int> landmarks(nbP);
   std::iota(landmarks.begin(), landmarks.end(), 0);
 
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/include/gudhi/Tangential_complex.h b/src/Tangential_complex/include/gudhi/Tangential_complex.h
index 65de2743..cfc82eb1 100644
--- a/src/Tangential_complex/include/gudhi/Tangential_complex.h
+++ b/src/Tangential_complex/include/gudhi/Tangential_complex.h
@@ -1314,14 +1314,6 @@ class Tangential_complex {
         m_k.construct_vector_d_object();
     typename K::Compute_coordinate_d coord =
         m_k.compute_coordinate_d_object();
-    typename K::Squared_length_d sqlen =
-        m_k.squared_length_d_object();
-    typename K::Scaled_vector_d scaled_vec =
-        m_k.scaled_vector_d_object();
-    typename K::Scalar_product_d scalar_pdct =
-        m_k.scalar_product_d_object();
-    typename K::Difference_of_vectors_d diff_vec =
-        m_k.difference_of_vectors_d_object();
 
 #ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM
     KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors(
@@ -2159,7 +2151,7 @@ class Tangential_complex {
       typedef std::vector<Simplex> Triangles;
       Triangles triangles;
 
-      std::size_t num_vertices = c.size();
+      int num_vertices = static_cast<int>(c.size());
       // Do not export smaller dimension simplices
       if (num_vertices < m_intrinsic_dim + 1)
         continue;
diff --git a/src/Tangential_complex/test/test_tangential_complex.cpp b/src/Tangential_complex/test/test_tangential_complex.cpp
index ebe5cdb4..48156440 100644
--- a/src/Tangential_complex/test/test_tangential_complex.cpp
+++ b/src/Tangential_complex/test/test_tangential_complex.cpp
@@ -71,9 +71,7 @@ BOOST_AUTO_TEST_CASE(test_Spatial_tree_data_structure) {
 
 BOOST_AUTO_TEST_CASE(test_mini_tangential) {
   typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> Kernel;
-  typedef Kernel::FT FT;
   typedef Kernel::Point_d Point;
-  typedef Kernel::Vector_d Vector;
   typedef tc::Tangential_complex<Kernel, CGAL::Dynamic_dimension_tag, CGAL::Parallel_tag> TC;
 
 
diff --git a/src/common/include/gudhi/random_point_generators.h b/src/common/include/gudhi/random_point_generators.h
index c643a1e3..2ec465ef 100644
--- a/src/common/include/gudhi/random_point_generators.h
+++ b/src/common/include/gudhi/random_point_generators.h
@@ -192,10 +192,9 @@ static void generate_uniform_points_on_torus_d(const Kernel &k, int dim, std::si
                                                double radius_noise_percentage = 0.,
                                                std::vector<typename Kernel::FT> current_point = std::vector<typename Kernel::FT>()) {
   CGAL::Random rng;
-  if (current_point.size() == 2 * dim) {
-    *out++ = k.construct_point_d_object()(
-                                          static_cast<int> (current_point.size()),
-                                          current_point.begin(), current_point.end());
+  int point_size = static_cast<int>(current_point.size());
+  if (point_size == 2 * dim) {
+    *out++ = k.construct_point_d_object()(point_size, current_point.begin(), current_point.end());
   } else {
     for (std::size_t slice_idx = 0; slice_idx < num_slices; ++slice_idx) {
       double radius_noise_ratio = 1.;