From 8d7329f3e5ad843e553c3c5503cecc28ef2eead6 Mon Sep 17 00:00:00 2001
From: Gard Spreemann <gspreemann@gmail.com>
Date: Thu, 20 Apr 2017 11:10:45 +0200
Subject: GUDHI 2.0.0 as released by upstream in a tarball.

---
 example/Subsampling/example_custom_kernel.cpp | 65 +++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 example/Subsampling/example_custom_kernel.cpp

(limited to 'example/Subsampling/example_custom_kernel.cpp')

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;
+}
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