s/kernel/distance/ for choose_n_farthest_points argument

4 files changed, 47 insertions, 66 deletions

diff --git a/src/Subsampling/example/CMakeLists.txt b/src/Subsampling/example/CMakeLists.txt
index 28aab103..fb6875e1 100644
--- a/src/Subsampling/example/CMakeLists.txt
+++ b/src/Subsampling/example/CMakeLists.txt
@@ -3,7 +3,7 @@ project(Subsampling_examples)
 if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0)
   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_custom_distance example_custom_distance.cpp)
   add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp)
   target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY})
 
@@ -16,7 +16,7 @@ if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0)
 
   install(TARGETS Subsampling_example_pick_n_random_points DESTINATION bin)
   install(TARGETS Subsampling_example_choose_n_farthest_points DESTINATION bin)
-  install(TARGETS Subsampling_example_custom_kernel DESTINATION bin)
+  install(TARGETS Subsampling_example_custom_distance DESTINATION bin)
   install(TARGETS Subsampling_example_sparsify_point_set DESTINATION bin)
 
 endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0)
diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp
index 27cf5d4e..e8b3ce2d 100644
--- a/src/Subsampling/example/example_choose_n_farthest_points.cpp
+++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp
@@ -20,7 +20,7 @@ int main(void) {
 
   K k;
   std::vector<Point_d> results;
-  Gudhi::subsampling::choose_n_farthest_points(k, points, 100,
+  Gudhi::subsampling::choose_n_farthest_points(k.squared_distance_d_object(), points, 100,
                                                Gudhi::subsampling::random_starting_point,
                                                std::back_inserter(results));
   std::clog << "Before sparsification: " << points.size() << " points.\n";
diff --git a/src/Subsampling/example/example_custom_distance.cpp b/src/Subsampling/example/example_custom_distance.cpp
new file mode 100644
index 00000000..3325b12d
--- /dev/null
+++ b/src/Subsampling/example/example_custom_distance.cpp
@@ -0,0 +1,44 @@
+#include <gudhi/choose_n_farthest_points.h>
+
+#include <iostream>
+#include <vector>
+#include <iterator>
+
+
+typedef unsigned Point;
+
+/* The class Distance 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 Distance {
+   private:
+    std::vector<std::vector<double>> matrix_;
+
+   public:
+    Distance() {
+      matrix_.push_back({0, 1, 2, 4});
+      matrix_.push_back({1, 0, 4, 2});
+      matrix_.push_back({2, 4, 0, 1});
+      matrix_.push_back({4, 2, 1, 0});
+    }
+
+    double operator()(Point p1, Point p2) const {
+      return matrix_[p1][p2];
+    }
+};
+
+int main(void) {
+  std::vector<Point> points = {0, 1, 2, 3};
+  std::vector<Point> results;
+
+  Gudhi::subsampling::choose_n_farthest_points(Distance(), points, 2,
+                                               Gudhi::subsampling::random_starting_point,
+                                               std::back_inserter(results));
+  std::clog << "Before sparsification: " << points.size() << " points.\n";
+  std::clog << "After  sparsification: " << results.size() << " points.\n";
+  std::clog << "Result table: {" << results[0] << "," << results[1] << "}\n";
+}
diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp
deleted file mode 100644
index 535bf42a..00000000
--- a/src/Subsampling/example/example_custom_kernel.cpp
+++ /dev/null
@@ -1,63 +0,0 @@
-#include <gudhi/choose_n_farthest_points.h>
-
-#include <iostream>
-#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::clog << "Before sparsification: " << points.size() << " points.\n";
-  std::clog << "After  sparsification: " << results.size() << " points.\n";
-  std::clog << "Result table: {" << results[0] << "," << results[1] << "}\n";
-
-  return 0;
-}