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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: Francois Godi
*
* Copyright (C) 2015 Inria
*
* Modification(s):
* - YYYY/MM Author: Description of the modification
*/
#ifndef NEIGHBORS_FINDER_H_
#define NEIGHBORS_FINDER_H_
// Inclusion order is important for CGAL patch
#include <CGAL/Kd_tree.h>
#include <CGAL/Search_traits.h>
#include <gudhi/Persistence_graph.h>
#include <gudhi/Internal_point.h>
#include <unordered_set>
#include <vector>
#include <algorithm> // for std::max
#include <cmath> // for std::abs
namespace Gudhi {
namespace persistence_diagram {
/** \internal \brief Variant of CGAL::Fuzzy_iso_box to ensure that the box ic closed. It isn't clear how necessary that is.
*/
struct Square_query {
typedef CGAL::Dimension_tag<2> D;
typedef Internal_point Point_d;
typedef double FT;
bool contains(Point_d p) const {
return (std::max)(std::abs(p.x()-c.x()), std::abs(p.y()-c.y())) <= size;
}
bool inner_range_intersects(CGAL::Kd_tree_rectangle<FT, D> const&r) const {
return
r.max_coord(0) >= c.x() - size &&
r.min_coord(0) <= c.x() + size &&
r.max_coord(1) >= c.y() - size &&
r.min_coord(1) <= c.y() + size;
}
bool outer_range_contains(CGAL::Kd_tree_rectangle<FT, D> const&r) const {
return
r.min_coord(0) >= c.x() - size &&
r.max_coord(0) <= c.x() + size &&
r.min_coord(1) >= c.y() - size &&
r.max_coord(1) <= c.y() + size;
}
Point_d c;
FT size;
};
/** \internal \brief data structure used to find any point (including projections) in V near to a query point from U
* (which can be a projection).
*
* V points have to be added manually using their index and before the first pull. A neighbor pulled is automatically
* removed.
*
* \ingroup bottleneck_distance
*/
class Neighbors_finder {
typedef CGAL::Dimension_tag<2> D;
typedef CGAL::Search_traits<double, Internal_point, const double*, Construct_coord_iterator, D> Traits;
typedef CGAL::Kd_tree<Traits> Kd_tree;
public:
/** \internal \brief Constructor taking the near distance definition as parameter. */
Neighbors_finder(const Persistence_graph& g, double r);
/** \internal \brief A point added will be possibly pulled. */
void add(int v_point_index);
/** \internal \brief Returns and remove a V point near to the U point given as parameter, null_point_index() if
* there isn't such a point. */
int pull_near(int u_point_index);
/** \internal \brief Returns and remove all the V points near to the U point given as parameter. */
std::vector<int> pull_all_near(int u_point_index);
private:
const Persistence_graph& g;
const double r;
Kd_tree kd_t;
std::unordered_set<int> projections_f;
};
/** \internal \brief data structure used to find any point (including projections) in V near to a query point from U
* (which can be a projection) in a layered graph layer given as parmeter.
*
* V points have to be added manually using their index and before the first pull. A neighbor pulled is automatically
* removed.
*
* \ingroup bottleneck_distance
*/
class Layered_neighbors_finder {
public:
/** \internal \brief Constructor taking the near distance definition as parameter. */
Layered_neighbors_finder(const Persistence_graph& g, double r);
/** \internal \brief A point added will be possibly pulled. */
void add(int v_point_index, int vlayer);
/** \internal \brief Returns and remove a V point near to the U point given as parameter, null_point_index() if
* there isn't such a point. */
int pull_near(int u_point_index, int vlayer);
/** \internal \brief Returns the number of layers. */
int vlayers_number() const;
private:
const Persistence_graph& g;
const double r;
std::vector<std::unique_ptr<Neighbors_finder>> neighbors_finder;
};
inline Neighbors_finder::Neighbors_finder(const Persistence_graph& g, double r) :
g(g), r(r), kd_t(), projections_f() { }
inline void Neighbors_finder::add(int v_point_index) {
if (g.on_the_v_diagonal(v_point_index))
projections_f.emplace(v_point_index);
else
kd_t.insert(g.get_v_point(v_point_index));
}
inline int Neighbors_finder::pull_near(int u_point_index) {
int tmp;
int c = g.corresponding_point_in_v(u_point_index);
if (g.on_the_u_diagonal(u_point_index) && !projections_f.empty()) {
// Any pair of projection is at distance 0
tmp = *projections_f.cbegin();
projections_f.erase(tmp);
} else if (projections_f.count(c) && (g.distance(u_point_index, c) <= r)) {
// Is the query point near to its projection ?
tmp = c;
projections_f.erase(tmp);
} else {
// Is the query point near to a V point in the plane ?
Internal_point u_point = g.get_u_point(u_point_index);
auto neighbor = kd_t.search_any_point(Square_query{u_point, r});
if (!neighbor)
return null_point_index();
tmp = neighbor->point_index;
auto point = g.get_v_point(tmp);
int idx = point.point_index;
kd_t.remove(point, [idx](Internal_point const&p){return p.point_index == idx;});
}
return tmp;
}
inline std::vector<int> Neighbors_finder::pull_all_near(int u_point_index) {
std::vector<int> all_pull;
int last_pull = pull_near(u_point_index);
while (last_pull != null_point_index()) {
all_pull.emplace_back(last_pull);
last_pull = pull_near(u_point_index);
}
return all_pull;
}
inline Layered_neighbors_finder::Layered_neighbors_finder(const Persistence_graph& g, double r) :
g(g), r(r), neighbors_finder() { }
inline void Layered_neighbors_finder::add(int v_point_index, int vlayer) {
for (int l = neighbors_finder.size(); l <= vlayer; l++)
neighbors_finder.emplace_back(std::unique_ptr<Neighbors_finder>(new Neighbors_finder(g, r)));
neighbors_finder.at(vlayer)->add(v_point_index);
}
inline int Layered_neighbors_finder::pull_near(int u_point_index, int vlayer) {
if (static_cast<int> (neighbors_finder.size()) <= vlayer)
return null_point_index();
return neighbors_finder.at(vlayer)->pull_near(u_point_index);
}
inline int Layered_neighbors_finder::vlayers_number() const {
return static_cast<int> (neighbors_finder.size());
}
} // namespace persistence_diagram
} // namespace Gudhi
#endif // NEIGHBORS_FINDER_H_
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