Merge pull request #409 from mglisse/fardist

Pass a distance function instead of a kernel to choose_n_farthest_points

1 files changed, 44 insertions, 0 deletions

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";
+}