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/* 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): Siargey Kachanovich
*
* Copyright (C) 2016 Inria
*
* Modification(s):
* - YYYY/MM Author: Description of the modification
*/
#ifndef CHOOSE_N_FARTHEST_POINTS_H_
#define CHOOSE_N_FARTHEST_POINTS_H_
#include <boost/range.hpp>
#include <gudhi/Null_output_iterator.h>
#include <iterator>
#include <vector>
#include <random>
#include <limits> // for numeric_limits<>
namespace Gudhi {
namespace subsampling {
/**
* \ingroup subsampling
*/
enum : std::size_t {
/**
* Argument for `choose_n_farthest_points` to indicate that the starting point should be picked randomly.
*/
random_starting_point = std::size_t(-1)
};
/**
* \ingroup subsampling
* \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
* current chosen point set to the subsampling.
* The iteration starts with the landmark `starting point` or, if `starting point==random_starting_point`, with a random landmark.
* \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the
* concept <a target="_blank"
* href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a> (despite the name, taken from CGAL, this can be any kind of metric or proximity measure).
* It must also contain a public member `squared_distance_d_object()` that returns an object of this type.
* \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access
* via `operator[]` and the points should be stored contiguously in memory.
* \tparam PointOutputIterator Output iterator whose value type is Kernel::Point_d.
* \tparam DistanceOutputIterator Output iterator for distances.
* \details It chooses `final_size` points from a random access range
* `input_pts` and outputs them in the output iterator `output_it`. It also
* outputs the distance from each of those points to the set of previous
* points in `dist_it`.
* @param[in] k A kernel object.
* @param[in] input_pts Const reference to the input points.
* @param[in] final_size The size of the subsample to compute.
* @param[in] starting_point The seed in the farthest point algorithm.
* @param[out] output_it The output iterator for points.
* @param[out] dist_it The optional output iterator for distances.
*
*/
template < typename Kernel,
typename Point_range,
typename PointOutputIterator,
typename DistanceOutputIterator = Null_output_iterator>
void choose_n_farthest_points(Kernel const &k,
Point_range const &input_pts,
std::size_t final_size,
std::size_t starting_point,
PointOutputIterator output_it,
DistanceOutputIterator dist_it = {}) {
std::size_t nb_points = boost::size(input_pts);
if (final_size > nb_points)
final_size = nb_points;
// Tests to the limit
if (final_size < 1)
return;
if (starting_point == random_starting_point) {
// Choose randomly the first landmark
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<std::size_t> dis(0, nb_points - 1);
starting_point = dis(gen);
}
typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
std::size_t current_number_of_landmarks = 0; // counter for landmarks
const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry)
std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from input_pts
std::size_t curr_max_w = starting_point;
for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) {
// curr_max_w at this point is the next landmark
*output_it++ = input_pts[curr_max_w];
*dist_it++ = dist_to_L[curr_max_w];
std::size_t i = 0;
for (auto&& p : input_pts) {
double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w));
if (curr_dist < dist_to_L[i])
dist_to_L[i] = curr_dist;
++i;
}
// choose the next curr_max_w
double curr_max_dist = 0; // used for defining the furhest point from L
for (i = 0; i < dist_to_L.size(); i++)
if (dist_to_L[i] > curr_max_dist) {
curr_max_dist = dist_to_L[i];
curr_max_w = i;
}
}
}
} // namespace subsampling
} // namespace Gudhi
#endif // CHOOSE_N_FARTHEST_POINTS_H_
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