text: /*    This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
   *    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
   *    Author(s):       Vincent Rouvreau
   *
   *    Copyright (C) 2016 Inria
   *
   *    Modification(s):
   *      - YYYY/MM Author: Description of the modification
   */
  
  #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_
  