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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:       Francois Godi
+ *
+ *    Copyright (C) 2015  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 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>
+
+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::abs(p.x()-c.x())<=size && 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_