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Feel++ 0.111.0-preview.14
Finite Element Embedded Library in C++
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Feel++ 0.111.0-preview.14
Finite Element Embedded Library in C++
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Collaboration diagram for Feel::KDTree:Typedefs | |
| typedef ublas::vector< double > | node_type |
| typedef boost::tuple< node_type, size_type, uint16_type, size_type > | index_node_type |
| typedef std::vector< index_node_type > | points_type |
| typedef points_type::iterator | points_iterator |
| typedef points_type::const_iterator | points_const_iterator |
| typedef boost::tuple< node_type, size_type, uint16_type, size_type, double > | index_node_search_type |
| typedef std::vector< index_node_search_type > | points_search_type |
| typedef points_search_type::iterator | points_search_iterator |
| typedef points_search_type::const_iterator | points_search_const_iterator |
Methods | |
| void | clear () |
| reset the tree, remove all points | |
| void | reserve (size_type n) |
| reserve memory for the index/node pair | |
| void | nbNearNeighbor (size_type n) |
| define the max number of point for the research( Default is 4 ) | |
| size_type | addPoint (node_type const &n, size_type indice_global=0) |
| insert a new point in the tree More... | |
| void | addPointWithId (const node_type &n, size_type i, uint16_type comp, size_type indice_global=0) |
| insert a new point, with an associated number. | |
| void | pointsInBox (points_type &inpts, const node_type &min, const node_type &max) |
| fills ipts with the indexes of points in the box [min,max] | |
| void | search (const node_type &node_) |
| search the neighbors points of M_node_search in the kd-tree | |
| void | showResultSearch () |
| print the result of the research | |
| void | writeLatexData (std::string __nameFile="kdtreeData.tex") |
| print in a file the scatterplot with the cuts of hyperplan (2d et 3d) | |
Constructors, destructor | |
| KDTree () | |
| KDTree (KDTree const &tree) | |
| ~KDTree () | |
Accessors | |
| size_type | nPoints () const |
| get the number of points in the tree | |
| const points_type & | points () const |
| get the points set | |
| const points_search_type & | pointsNearNeighbor () const |
| get the points Near Neighbor set | |
| size_type | nPtMaxNearNeighbor () |
KDTree class.
In computer science, a kd-tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. kd-trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbor searches). kd-trees are a special case of BSP trees.
1.9.3