CGAL 6.2 - dD Spatial Searching

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Class and Concept List
Here is the list of all concepts and classes of this package. Classes are inside the namespace CGAL. Concepts are in the global namespace.
[detail level 1 2]

▼N CGAL |
C Distance_adapter | A class that uses a point property map to adapt a distance class to work on a key as point type
C Euclidean_distance | The class `Euclidean_distance` provides an implementation of the concept `OrthogonalDistance`, with the Euclidean distance ( \( l_2\) metric)
C 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
C Fair | Implements the fair splitting rule
C Fuzzy_iso_box | The class `Fuzzy_iso_box` implements fuzzy `d`-dimensional (closed) iso boxes
C Fuzzy_sphere | The class `Fuzzy_sphere` implements fuzzy `d`-dimensional spheres
C Incremental_neighbor_search | The class `Incremental_neighbor_search` implements incremental nearest and furthest neighbor searching on a tree
C 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
C Kd_tree | The class `Kd_tree` defines a `k-d` tree
C Kd_tree_internal_node |
C Kd_tree_leaf_node |
C Kd_tree_node | The class `Kd_tree_node` implements a node class for a `k-d` tree
C 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
C 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
C Median_of_max_spread | Implements the median of max spread splitting rule
C Median_of_rectangle | Implements the median of rectangle splitting rule
C Midpoint_of_max_spread | Implements the midpoint of max spread splitting rule
C Midpoint_of_rectangle | Implements the midpoint of rectangle splitting rule
C Orthogonal_incremental_neighbor_search | The class `Orthogonal_incremental_neighbor_search` implements incremental nearest and furthest neighbor searching on a tree
C 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
C Plane_separator | The class `Plane_separator` implements a plane separator, i.e., a hyperplane that is used to separate two half spaces
C Point_container | A custom container for points used to build a tree
C Search_traits | The class `Search_traits` can be used as a template parameter of the kd tree and the search classes
C Search_traits_2 | The class `Search_traits_2` can be used as a template parameter of the kd tree and the search classes
C Search_traits_3 | The class `Search_traits_3` can be used as a template parameter of the kd tree and the search classes
C Search_traits_adapter | The class `Search_traits_adapter` can be used as a template parameter of the kd tree and the search classes
C Search_traits_d | The class `Search_traits_d` can be used as a template parameter of the kd tree and the search classes
C Sliding_fair | Implements the sliding fair splitting rule
C Sliding_midpoint | Implements the sliding midpoint splitting rule
C 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 \( l_{\infty}(w)(r,q)=max \{w_i |r_i-q_i| \mid 1 \leq i \leq d\}\)
C FuzzyQueryItem | The concept `FuzzyQueryItem` describes the requirements for fuzzy `d`-dimensional spatial objects
C GeneralDistance | Requirements of a distance class defining a distance between a query item denoting a spatial object and a point. To optimize distance computations transformed distances are used, e.g., for a Euclidean distance the transformed distance is the squared Euclidean distance
C OrthogonalDistance | Requirements of an orthogonal distance class supporting incremental distance updates. To optimize distance computations transformed distances are used. E.g., for a Euclidean distance the transformed distance is the squared Euclidean distance
C RangeSearchTraits | The concept `RangeSearchTraits` defines the requirements for the template parameter of the search classes. This concept also defines requirements to range search queries in a model of `SpatialTree`
C SearchGeomTraits_2 | The concept `SearchGeomTraits_2` defines the requirements for the template parameter of the search traits classes
C SearchGeomTraits_3 | The concept `SearchGeomTraits_3` defines the requirements for the template parameter of the search traits classes
C SearchTraits | The concept `SearchTraits` defines the requirements for the template parameter of the search classes
C SpatialSeparator | The concept `SpatialSeparator` defines the requirements for a separator
C SpatialTree | The concept `SpatialTree` defines the requirements for a tree supporting both neighbor searching and approximate range searching
C Splitter | This is an advanced concept