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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): Siargey Kachanovich
*
* Copyright (C) 2015 INRIA Sophia Antipolis-Méditerranée (France)
*
* 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 LANDMARK_CHOICE_BY_SPARSIFICATION_H_
#define LANDMARK_CHOICE_BY_SPARSIFICATION_H_
#include <utility> // for pair<>
#include <vector>
#include <cstddef> // for ptrdiff_t type
#include <algorithm>
//#include <CGAL/Cartesian_d.h>
#include <CGAL/Search_traits.h>
#include <CGAL/Search_traits_adapter.h>
//#include <CGAL/property_map.h>
#include <CGAL/Epick_d.h>
#include <CGAL/Orthogonal_incremental_neighbor_search.h>
#include <CGAL/Orthogonal_k_neighbor_search.h>
#include <CGAL/Kd_tree.h>
#include <CGAL/Euclidean_distance.h>
//#include <CGAL/Kernel_d/Vector_d.h>
#include <CGAL/Random.h>
#include <CGAL/Fuzzy_sphere.h>
namespace Gudhi {
namespace witness_complex {
typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K;
typedef K::FT FT;
typedef K::Point_d Point_d;
typedef CGAL::Search_traits< FT,
Point_d,
typename K::Cartesian_const_iterator_d,
typename K::Construct_cartesian_const_iterator_d > Traits_base;
typedef CGAL::Euclidean_distance<Traits_base> Euclidean_distance;
typedef CGAL::Search_traits_adapter< std::ptrdiff_t,
Point_d*,
Traits_base> STraits;
typedef CGAL::Distance_adapter< std::ptrdiff_t,
Point_d*,
Euclidean_distance > Distance_adapter;
typedef CGAL::Orthogonal_incremental_neighbor_search< STraits,
Distance_adapter > Neighbor_search;
typedef CGAL::Orthogonal_k_neighbor_search< STraits > Neighbor_search2;
typedef Neighbor_search::Tree Tree;
typedef CGAL::Fuzzy_sphere<STraits> Fuzzy_sphere;
/** \brief Landmark selection function by as a sub-epsilon-net of the
* given set of points.
*/
template <typename Point_random_access_range>
void landmark_choice_by_sparsification(Point_random_access_range & points,
unsigned nbL,
FT mu_epsilon,
Point_random_access_range & landmarks)
{
int nbP = points.end() - points.begin();
assert(nbP >= nbL);
CGAL::Random rand;
// TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
STraits points_traits(&(points[0]));
CGAL::Distance_adapter<std::ptrdiff_t,Point_d*,Euclidean_distance> points_adapter(&(points[0]));
std::vector<bool> dropped_points(nbP, false);
Tree witness_tree(boost::counting_iterator<std::ptrdiff_t>(0),
boost::counting_iterator<std::ptrdiff_t>(nbP),
typename Tree::Splitter(),
points_traits);
for (int points_i = 0; points_i < nbP; points_i++) {
if (dropped_points[points_i])
continue;
Point_d & w = points[points_i];
Fuzzy_sphere fs(w, mu_epsilon, 0, points_traits);
std::vector<int> close_neighbors;
witness_tree.search(std::insert_iterator<std::vector<int>>(close_neighbors,close_neighbors.begin()),fs);
for (int i: close_neighbors)
dropped_points[i] = true;
}
for (int points_i = 0; points_i < nbP; points_i++) {
if (dropped_points[points_i])
landmarks.push_back(points[points_i]);
}
if (nbL < landmarks.size()) {
std::random_shuffle(landmarks.begin(), landmarks.end());
landmarks.resize(nbL);
}
}
/** \brief Landmark choice strategy by taking random vertices for landmarks.
* \details It chooses nbL distinct landmarks from a random access range `points`
* and outputs a matrix {witness}*{closest landmarks} in knn.
*/
template <typename KNearestNeighbours,
typename Point_random_access_range,
typename Distance_matrix>
void build_distance_matrix(Point_random_access_range const & points,
Point_random_access_range & landmarks,
FT alpha,
unsigned limD,
KNearestNeighbours & knn,
Distance_matrix & distances)
{
int nbP = points.end() - points.begin();
knn = KNearestNeighbours(nbP);
distances = Distance_matrix(nbP);
STraits traits(&(landmarks[0]));
CGAL::Distance_adapter<std::ptrdiff_t,Point_d*,Euclidean_distance> adapter(&(landmarks[0]));
Euclidean_distance ed;
Tree landmark_tree(boost::counting_iterator<std::ptrdiff_t>(0),
boost::counting_iterator<std::ptrdiff_t>(landmarks.size()),
typename Tree::Splitter(),
traits);
for (int points_i = 0; points_i < nbP; points_i++) {
Point_d const & w = points[points_i];
Neighbor_search search(landmark_tree,
w,
FT(0),
true,
adapter);
Neighbor_search::iterator search_it = search.begin();
// Neighbor_search2 search(landmark_tree,
// w, limD+1,
// FT(0),
// true,
// adapter);
// Neighbor_search2::iterator search_it = search.begin();
while (knn[points_i].size() <= limD) {
distances[points_i].push_back(search_it->second); //!sq_dist
knn[points_i].push_back((search_it++)->first);
}
FT dtow = distances[points_i][limD];
while (search_it != search.end() && search_it->second < dtow + alpha) {
distances[points_i].push_back(search_it->second);
knn[points_i].push_back((search_it++)->first);
}
//std::cout << "k = " << knn[points_i].size() << std::endl;
}
}
/*
template <typename Kernel, typename Point_container>
std::vector<typename Point_container::value_type>
sparsify_point_set(const Kernel &k,
Point_container const& input_pts,
typename Kernel::FT min_squared_dist)
{
typedef typename CGAL::Tangential_complex_::Point_cloud_data_structure<Kernel, Point_container> Points_ds;
typedef typename Points_ds::INS_iterator INS_iterator;
typedef typename Points_ds::INS_range INS_range;
typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
// Create the output container
std::vector<typename Point_container::value_type> output;
Points_ds points_ds(input_pts);
std::vector<bool> dropped_points(input_pts.size(), false);
// Parse the input points, and add them if they are not too close to
// the other points
std::size_t pt_idx = 0;
for (typename Point_container::const_iterator it_pt = input_pts.begin() ;
it_pt != input_pts.end();
++it_pt, ++pt_idx)
{
if (dropped_points[pt_idx])
continue;
output.push_back(*it_pt);
INS_range ins_range = points_ds.query_incremental_ANN(*it_pt);
// If another point Q is closer that min_squared_dist, mark Q to be dropped
for (INS_iterator nn_it = ins_range.begin() ;
nn_it != ins_range.end() ;
++nn_it)
{
std::size_t neighbor_point_idx = nn_it->first;
// If the neighbor is too close, we drop the neighbor
if (nn_it->second < min_squared_dist)
{
// N.B.: If neighbor_point_idx < pt_idx,
// dropped_points[neighbor_point_idx] is already true but adding a
// test doesn't make things faster, so why bother?
dropped_points[neighbor_point_idx] = true;
}
else
break;
}
}
return output;
}
*/
} // namespace witness_complex
} // namespace Gudhi
#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_
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