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diff --git a/cython/include/Subsampling_interface.h b/cython/include/Subsampling_interface.h
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+/*    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):       Vincent Rouvreau
+ *
+ *    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/>.
+ */
+
+#ifndef INCLUDE_SUBSAMPLING_INTERFACE_H_
+#define INCLUDE_SUBSAMPLING_INTERFACE_H_
+
+#include <gudhi/choose_n_farthest_points.h>
+#include <gudhi/pick_n_random_points.h>
+#include <gudhi/sparsify_point_set.h>
+#include <gudhi/Points_off_io.h>
+#include <CGAL/Epick_d.h>
+
+#include <iostream>
+#include <vector>
+#include <string>
+
+namespace Gudhi {
+
+namespace subsampling {
+
+using Subsampling_dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >;
+using Subsampling_point_d = Subsampling_dynamic_kernel::Point_d;
+using Subsampling_ft = Subsampling_dynamic_kernel::FT;
+
+// ------ choose_n_farthest_points ------
+std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points,
+                                                               unsigned nb_points) {
+  std::vector<std::vector<double>> landmarks;
+  Subsampling_dynamic_kernel k;
+  choose_n_farthest_points(k, points, nb_points, random_starting_point, std::back_inserter(landmarks));
+
+  return landmarks;
+}
+
+std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points,
+                                                               unsigned nb_points, unsigned starting_point) {
+  std::vector<std::vector<double>> landmarks;
+  Subsampling_dynamic_kernel k;
+  choose_n_farthest_points(k, points, nb_points, starting_point, std::back_inserter(landmarks));
+
+  return landmarks;
+}
+
+std::vector<std::vector<double>> subsampling_n_farthest_points_from_file(const std::string& off_file,
+                                                                         unsigned nb_points) {
+  Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file);
+  std::vector<std::vector<double>> points = off_reader.get_point_cloud();
+  return subsampling_n_farthest_points(points, nb_points);
+}
+
+std::vector<std::vector<double>> subsampling_n_farthest_points_from_file(const std::string& off_file,
+                                                                         unsigned nb_points, unsigned starting_point) {
+    Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file);
+    std::vector<std::vector<double>> points = off_reader.get_point_cloud();
+    return subsampling_n_farthest_points(points, nb_points, starting_point);
+}
+
+// ------ pick_n_random_points ------
+std::vector<std::vector<double>> subsampling_n_random_points(const std::vector<std::vector<double>>& points,
+                                                             unsigned nb_points) {
+  std::vector<std::vector<double>> landmarks;
+  pick_n_random_points(points, nb_points, std::back_inserter(landmarks));
+
+  return landmarks;
+}
+
+std::vector<std::vector<double>> subsampling_n_random_points_from_file(const std::string& off_file,
+                                                                       unsigned nb_points) {
+  Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file);
+  std::vector<std::vector<double>> points = off_reader.get_point_cloud();
+  return subsampling_n_random_points(points, nb_points);
+}
+
+// ------ sparsify_point_set ------
+std::vector<std::vector<double>> subsampling_sparsify_points(const std::vector<std::vector<double>>& points,
+                                                             double min_squared_dist) {
+  std::vector<Subsampling_point_d> input, output;
+  for (auto point : points)
+      input.push_back(Subsampling_point_d(point.size(), point.begin(), point.end()));
+  Subsampling_dynamic_kernel k;
+  sparsify_point_set(k, input, min_squared_dist, std::back_inserter(output));
+
+  std::vector<std::vector<double>> landmarks;
+  for (auto point : output)
+      landmarks.push_back(std::vector<double>(point.cartesian_begin(), point.cartesian_end()));
+  return landmarks;
+}
+
+std::vector<std::vector<double>> subsampling_sparsify_points_from_file(const std::string& off_file,
+                                                                       double min_squared_dist) {
+  Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file);
+  std::vector<std::vector<double>> points = off_reader.get_point_cloud();
+  return subsampling_sparsify_points(points, min_squared_dist);
+}
+
+}  // namespace subsampling
+
+}  // namespace Gudhi
+
+#endif  // INCLUDE_SUBSAMPLING_INTERFACE_H_