CGAL 6.2 - dD Spatial Searching

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Classes
CGAL Namespace Reference

## Classes
class | Distance_adapter
| A class that uses a point property map to adapt a distance class to work on a key as point type. More...
class | Euclidean_distance
| The class `Euclidean_distance` provides an implementation of the concept `OrthogonalDistance`, with the Euclidean distance ( \( l_2\) metric). More...
class | Euclidean_distance_sphere_point
| The class `Euclidean_distance_sphere_point` provides an implementation of the `GeneralDistance` concept for the Euclidean distance ( \( l_2\) metric) between a \( d\)-dimensional sphere and a point, and the Euclidean distance between a \( d\)-dimensional sphere and a \( d\)-dimensional iso-rectangle defined as a \(k\)- \(d\) tree rectangle. More...
class | Fair
| Implements the fair splitting rule. More...
class | Fuzzy_iso_box
| The class `Fuzzy_iso_box` implements fuzzy `d`-dimensional (closed) iso boxes. More...
class | Fuzzy_sphere
| The class `Fuzzy_sphere` implements fuzzy `d`-dimensional spheres. More...
class | Incremental_neighbor_search
| The class `Incremental_neighbor_search` implements incremental nearest and furthest neighbor searching on a tree. More...
class | K_neighbor_search
| The class `K_neighbor_search` implements approximate `k`-nearest and `k`-furthest neighbor searching using standard search on a tree using a general distance class. More...
class | Kd_tree
| The class `Kd_tree` defines a `k-d` tree. More...
class | Kd_tree_internal_node
class | Kd_tree_leaf_node
class | Kd_tree_node
| The class `Kd_tree_node` implements a node class for a `k-d` tree. More...
class | Kd_tree_rectangle
| The class `Kd_tree_rectangle` implements `d`-dimensional iso-rectangles and related operations, e.g., methods to compute bounding boxes of point sets. More...
class | Manhattan_distance_iso_box_point
| The class `Manhattan_distance_iso_box_point` provides an implementation of the `GeneralDistance` concept for the Manhattan distance ( \( l_1\) metric) between a `d`-dimensional iso-box and a `d`-dimensional point and the Manhattan distance between a `d`-dimensional iso-box and a `d`-dimensional iso-box defined as a `k-d` tree rectangle. More...
class | Median_of_max_spread
| Implements the median of max spread splitting rule. More...
class | Median_of_rectangle
| Implements the median of rectangle splitting rule. More...
class | Midpoint_of_max_spread
| Implements the midpoint of max spread splitting rule. More...
class | Midpoint_of_rectangle
| Implements the midpoint of rectangle splitting rule. More...
class | Orthogonal_incremental_neighbor_search
| The class `Orthogonal_incremental_neighbor_search` implements incremental nearest and furthest neighbor searching on a tree. More...
class | Orthogonal_k_neighbor_search
| The class `Orthogonal_k_neighbor_search` implements approximate`k`-nearest and `k`-furthest neighbor searching on a tree using an orthogonal distance class. More...
class | Plane_separator
| The class `Plane_separator` implements a plane separator, i.e., a hyperplane that is used to separate two half spaces. More...
class | Point_container
| A custom container for points used to build a tree. More...
class | Search_traits
| The class `Search_traits` can be used as a template parameter of the kd tree and the search classes. More...
class | Search_traits_2
| The class `Search_traits_2` can be used as a template parameter of the kd tree and the search classes. More...
class | Search_traits_3
| The class `Search_traits_3` can be used as a template parameter of the kd tree and the search classes. More...
class | Search_traits_adapter
| The class `Search_traits_adapter` can be used as a template parameter of the kd tree and the search classes. More...
class | Search_traits_d
| The class `Search_traits_d` can be used as a template parameter of the kd tree and the search classes. More...
class | Sliding_fair
| Implements the sliding fair splitting rule. More...
class | Sliding_midpoint
| Implements the sliding midpoint splitting rule. More...
class | Weighted_Minkowski_distance
| The class `Weighted_Minkowski_distance` provides an implementation of the concept `OrthogonalDistance`, with a weighted Minkowski metric on \( d\)-dimensional points defined by \( l_p(w)(r,q)= ({\Sigma_{i=1}^{i=d} \, w_i(r_i-q_i)^p})^{1/p}\) for \( 0 < p <\infty\) and defined by \( l_{\infty}(w)(r,q)=max \{w_i |r_i-q_i| \mid 1 \leq i \leq d\}\). More...