Merge from trunk.

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/qt5@1848 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: c8e1376894207c8c08896f750f71c115e07f6d95

1 files changed, 103 insertions, 0 deletions

diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp
new file mode 100644
index 00000000..0bc0dff4
--- /dev/null
+++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp
@@ -0,0 +1,103 @@
+/*    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/>.
+ */
+
+// #ifdef _DEBUG
+// # define TBB_USE_THREADING_TOOL
+// #endif
+
+#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_n_farthest_points.h>
+#include <vector>
+#include <iterator>
+
+#include <CGAL/Epick_d.h>
+
+typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K;
+typedef typename K::FT FT;
+typedef typename K::Point_d Point_d;
+
+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) {
+          std::vector<FT> point({i, j, k, l});
+          points.push_back(Point_d(point.begin(), point.end()));
+        }
+
+  landmarks.clear();
+  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();
+
+}