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CGAL 4.8 - dD Spatial Searching

All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Groups Pages
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]

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