5 files changed, 165 insertions, 0 deletions
diff --git a/example/Subsampling/CMakeLists.txt b/example/Subsampling/CMakeLists.txt
new file mode 100644
index 00000000..71b8d2e8
--- /dev/null
+++ b/example/Subsampling/CMakeLists.txt
@@ -0,0 +1,17 @@
+cmake_minimum_required(VERSION 2.6)
+project(Subsampling_examples)
+
+if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1)
+  add_executable(Subsampling_example_pick_n_random_points example_pick_n_random_points.cpp)
+  add_executable(Subsampling_example_choose_n_farthest_points example_choose_n_farthest_points.cpp)
+  add_executable(Subsampling_example_custom_kernel example_custom_kernel.cpp)
+  add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp)
+  target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY})
+
+  add_test(NAME Subsampling_example_pick_n_random_points
+      COMMAND $<TARGET_FILE:Subsampling_example_pick_n_random_points>)
+  add_test(NAME Subsampling_example_choose_n_farthest_points
+      COMMAND $<TARGET_FILE:Subsampling_example_choose_n_farthest_points>)
+  add_test(NAME Subsampling_example_sparsify_point_set
+      COMMAND $<TARGET_FILE:Subsampling_example_sparsify_point_set>)
+endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1)
diff --git a/example/Subsampling/example_choose_n_farthest_points.cpp b/example/Subsampling/example_choose_n_farthest_points.cpp
new file mode 100644
index 00000000..ebf631fc
--- /dev/null
+++ b/example/Subsampling/example_choose_n_farthest_points.cpp
@@ -0,0 +1,29 @@
+#include <gudhi/choose_n_farthest_points.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Random.h>
+
+#include <vector>
+#include <iterator>
+
+int main(void) {
+  typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+  typedef typename K::Point_d Point_d;
+
+  CGAL::Random rd;
+
+  std::vector<Point_d> points;
+  for (int i = 0; i < 500; ++i)
+    points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
+                             rd.get_double(-1., 1), rd.get_double(-1., 1)));
+
+  K k;
+  std::vector<Point_d> results;
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 100,
+                                               Gudhi::subsampling::random_starting_point,
+                                               std::back_inserter(results));
+  std::cout << "Before sparsification: " << points.size() << " points.\n";
+  std::cout << "After  sparsification: " << results.size() << " points.\n";
+
+  return 0;
+}
diff --git a/example/Subsampling/example_custom_kernel.cpp b/example/Subsampling/example_custom_kernel.cpp
new file mode 100644
index 00000000..2d42bdde
--- /dev/null
+++ b/example/Subsampling/example_custom_kernel.cpp
@@ -0,0 +1,65 @@
+#include <gudhi/choose_n_farthest_points.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Random.h>
+
+#include <vector>
+#include <iterator>
+
+
+/* The class Kernel contains a distance function defined on the set of points {0, 1, 2, 3}
+ * and computes a distance according to the matrix:
+ * 0 1 2 4
+ * 1 0 4 2
+ * 2 4 0 1
+ * 4 2 1 0
+ */
+class Kernel {
+ public:
+  typedef double FT;
+  typedef unsigned Point_d;
+
+  // Class Squared_distance_d
+  class Squared_distance_d {
+   private:
+    std::vector<std::vector<FT>> matrix_;
+
+   public:
+    Squared_distance_d() {
+      matrix_.push_back(std::vector<FT>({0, 1, 2, 4}));
+      matrix_.push_back(std::vector<FT>({1, 0, 4, 2}));
+      matrix_.push_back(std::vector<FT>({2, 4, 0, 1}));
+      matrix_.push_back(std::vector<FT>({4, 2, 1, 0}));
+    }
+
+    FT operator()(Point_d p1, Point_d p2) {
+      return matrix_[p1][p2];
+    }
+  };
+
+  // Constructor
+  Kernel() {}
+
+  // Object of type Squared_distance_d
+  Squared_distance_d squared_distance_d_object() const {
+    return Squared_distance_d();
+  }
+};
+
+int main(void) {
+  typedef Kernel K;
+  typedef typename K::Point_d Point_d;
+
+  K k;
+  std::vector<Point_d> points = {0, 1, 2, 3};
+  std::vector<Point_d> results;
+
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 2,
+                                               Gudhi::subsampling::random_starting_point,
+                                               std::back_inserter(results));
+  std::cout << "Before sparsification: " << points.size() << " points.\n";
+  std::cout << "After  sparsification: " << results.size() << " points.\n";
+  std::cout << "Result table: {" << results[0] << "," << results[1] << "}\n";
+
+  return 0;
+}
diff --git a/example/Subsampling/example_pick_n_random_points.cpp b/example/Subsampling/example_pick_n_random_points.cpp
new file mode 100644
index 00000000..1e38e405
--- /dev/null
+++ b/example/Subsampling/example_pick_n_random_points.cpp
@@ -0,0 +1,27 @@
+#include <gudhi/pick_n_random_points.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Random.h>
+
+#include <vector>
+#include <iterator>
+
+int main(void) {
+  typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+  typedef typename K::Point_d Point_d;
+
+  CGAL::Random rd;
+
+  std::vector<Point_d> points;
+  for (int i = 0; i < 500; ++i)
+    points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
+                             rd.get_double(-1., 1), rd.get_double(-1., 1)));
+
+  K k;
+  std::vector<Point_d> results;
+  Gudhi::subsampling::pick_n_random_points(points, 100, std::back_inserter(results));
+  std::cout << "Before sparsification: " << points.size() << " points.\n";
+  std::cout << "After  sparsification: " << results.size() << " points.\n";
+
+  return 0;
+}
diff --git a/example/Subsampling/example_sparsify_point_set.cpp b/example/Subsampling/example_sparsify_point_set.cpp
new file mode 100644
index 00000000..b35a18d9
--- /dev/null
+++ b/example/Subsampling/example_sparsify_point_set.cpp
@@ -0,0 +1,27 @@
+#include <gudhi/sparsify_point_set.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Random.h>
+
+#include <vector>
+#include <iterator>
+
+int main(void) {
+  typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+  typedef typename K::Point_d Point_d;
+
+  CGAL::Random rd;
+
+  std::vector<Point_d> points;
+  for (int i = 0; i < 500; ++i)
+    points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
+                             rd.get_double(-1., 1), rd.get_double(-1., 1)));
+
+  K k;
+  std::vector<Point_d> results;
+  Gudhi::subsampling::sparsify_point_set(k, points, 0.4, std::back_inserter(results));
+  std::cout << "Before sparsification: " << points.size() << " points.\n";
+  std::cout << "After  sparsification: " << results.size() << " points.\n";
+
+  return 0;
+}