mlpack_neighbor3
Neighbor-search routines.
Usage
Classes
class FurthestNeighborSort
class NeighborSearchStat
Traversal information
for
NeighborSearch.
class
RASearchRules
Typedefs
typedef NeighborSearch
metric::EuclideanDistance >
AllkFN"
metric::EuclideanDistance >
AllkNN"
Functions
void Unmap
(const
arma::Mat<
size_t
>
&neighbors,
const
arma::mat
&distances,
const
std::vector<
size_t
>
&referenceMap,
const
std::vector<
size_t
>
&queryMap,
arma::Mat<
size_t
>
&neighborsOut,
arma::mat
&distancesOut,
const
bool
squareRoot=false)
Detailed Description
Neighbor-search routines.
These include all-nearest-neighbors and all-furthest-neighbors searches.
Typedef Documentation
typedef NeighborSearch<FurthestNeighborSort, metric::EuclideanDistance> mlpack::neighbor::AllkFN
The AllkFN class is the all-k-furthest-neighbors method. It returns L2 distances (Euclidean distances) for each of the k furthest neighbors.
Definition at line 48 of file typedef.hpp.
typedef NeighborSearch<NearestNeighborSort, metric::EuclideanDistance> mlpack::neighbor::AllkNN
The AllkNN class is the all-k-nearest-neighbors method. It returns L2 distances (Euclidean distances) for each of the k nearest neighbors.
Definition at line 42 of file typedef.hpp.
typedef RASearch<FurthestNeighborSort> mlpack::neighbor::AllkRAFN
The AllkRAFN class is the all-k-rank-approximate-farthest-neighbors method. It returns squared L2 distances (squared Euclidean distances) for each of the k rank-approximate farthest-neighbors. Squared distances are used because they are slightly faster than non-squared distances (they have one fewer call to sqrt()).
The approximation is controlled with two parameters (see allkrann_main.cpp) which can be specified at search time. So the tree building is done only once while the search can be performed multiple times with different approximation levels.
Definition at line 63 of file ra_typedef.hpp.
typedef RASearch mlpack::neighbor::AllkRANN
The AllkRANN class is the all-k-rank-approximate-nearest-neighbors method. It returns squared L2 distances (squared Euclidean distances) for each of the k rank-approximate nearest-neighbors. Squared distances are used because they are slightly faster than non-squared distances (they have one fewer call to sqrt()).
The approximation is controlled with two parameters (see allkrann_main.cpp) which can be specified at search time. So the tree building is done only once while the search can be performed multiple times with different approximation levels.
Definition at line 49 of file ra_typedef.hpp.
Function Documentation
void mlpack::neighbor::Unmap (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, const std::vector< size_t > &queryMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const boolsquareRoot = false)
Assuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors. This is useful for the dual-tree case.
Parameters:
neighbors Matrix of neighbors resulting from neighbor search.
distances Matrix of distances resulting from neighbor search.
referenceMap Mapping of reference set to old points.
queryMap Mapping of query set to old points.
neighborsOut Matrix to store unmapped neighbors into.
distancesOut Matrix to store unmapped distances into.
squareRoot If true, take the square root of the distances.
void mlpack::neighbor::Unmap (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const boolsquareRoot = false)
Assuming that the datasets have been mapped using referenceMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors. This is useful for the single-tree case.
Parameters:
neighbors Matrix of neighbors resulting from neighbor search.
distances Matrix of distances resulting from neighbor search.
referenceMap Mapping of reference set to old points.
neighborsOut Matrix to store unmapped neighbors into.
distancesOut Matrix to store unmapped distances into.
squareRoot If true, take the square root of the distances.