\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.11 - 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 12]
oNboost
oNCGAL
|oCEuclidean_distanceThe class Euclidean_distance provides an implementation of the concept OrthogonalDistance, with the Euclidean distance ( \( l_2\) metric)
|oCEuclidean_distance_sphere_pointThe 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
|oCFuzzy_iso_boxThe class Fuzzy_iso_box implements fuzzy d-dimensional iso boxes
|oCFuzzy_sphereThe class Fuzzy_sphere implements fuzzy d-dimensional spheres
|oCIncremental_neighbor_searchThe class Incremental_neighbor_search implements incremental nearest and furthest neighbor searching on a tree
|oCK_neighbor_searchThe class K_neighbor_search implements approximate k-nearest and k-furthest neighbor searching using standard search on a tree using a general distance class
|oCKd_treeThe class Kd_tree defines a k-d tree
|oCKd_tree_nodeThe class Kd_tree_node implements a node class for a k-d tree
|oCKd_tree_leaf_node
|oCKd_tree_internal_node
|oCKd_tree_rectangleThe class Kd_tree_rectangle implements d-dimensional iso-rectangles and related operations, e.g., methods to compute bounding boxes of point sets
|oCManhattan_distance_iso_box_pointThe 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
|oCOrthogonal_incremental_neighbor_searchThe class Orthogonal_incremental_neighbor_search implements incremental nearest and furthest neighbor searching on a tree
|oCOrthogonal_k_neighbor_searchThe class Orthogonal_k_neighbor_search implements approximatek-nearest and k-furthest neighbor searching on a tree using an orthogonal distance class
|oCPlane_separatorThe class Plane_separator implements a plane separator, i.e., a hyperplane that is used to separate two half spaces
|oCPoint_containerA custom container for points used to build a tree
|oCSearch_traitsThe class Search_traits can be used as a template parameter of the kd tree and the search classes
|oCSearch_traits_2The class Search_traits_2 can be used as a template parameter of the kd tree and the search classes
|oCSearch_traits_3The class Search_traits_3 can be used as a template parameter of the kd tree and the search classes
|oCDistance_adapterA class that uses a point property map to adapt a distance class to work on a key as point type
|oCSearch_traits_adapterThe class Search_traits_adapter can be used as a template parameter of the kd tree and the search classes
|oCSearch_traits_dThe class Search_traits_d can be used as a template parameter of the kd tree and the search classes
|oCFairImplements the fair splitting rule
|oCMedian_of_max_spreadImplements the median of max spread splitting rule
|oCMedian_of_rectangleImplements the median of rectangle splitting rule
|oCMidpoint_of_max_spreadImplements the midpoint of max spread splitting rule
|oCMidpoint_of_rectangleImplements the midpoint of rectangle splitting rule
|oCSliding_fairImplements the sliding fair splitting rule
|oCSliding_midpointImplements the sliding midpoint splitting rule
|\CWeighted_Minkowski_distanceThe 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 \( l_{\infty}(w)(r,q)=max \{w_i |r_i-q_i| \mid 1 \leq i \leq d\}\)
oCFuzzyQueryItemThe concept FuzzyQueryItem describes the requirements for fuzzy d-dimensional spatial objects
oCGeneralDistanceRequirements 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
oCOrthogonalDistanceRequirements 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
oCRangeSearchTraitsThe 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
oCSearchGeomTraits_2The concept SearchGeomTraits_2 defines the requirements for the template parameter of the search traits classes
oCSearchGeomTraits_3The concept SearchGeomTraits_3 defines the requirements for the template parameter of the search traits classes
oCSearchTraitsThe concept SearchTraits defines the requirements for the template parameter of the search classes
oCSpatialSeparatorThe concept SpatialSeparator defines the requirements for a separator
oCSpatialTreeThe concept SpatialTree defines the requirements for a tree supporting both neighbor searching and approximate range searching
\CSplitter
Advanced
The concept Splitter defines the requirements for a function object class implementing a splitting rule.