mlpack
2.0.2

Neighborsearch routines. More...
Classes  
class  FurthestNeighborSort 
This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class. More...  
class  LSHSearch 
The LSHSearch class; this class builds a hash on the reference set and uses this hash to compute the distanceapproximate nearestneighbors of the given queries. More...  
class  NearestNeighborSort 
This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class. More...  
class  NeighborSearch 
The NeighborSearch class is a template class for performing distancebased neighbor searches. More...  
class  NeighborSearchRules 
class  NeighborSearchStat 
Extra data for each node in the tree. More...  
class  NSModel 
struct  NSModelName 
struct  NSModelName< FurthestNeighborSort > 
struct  NSModelName< NearestNeighborSort > 
class  RAModel 
The RAModel class provides an abstraction for the RASearch class, abstracting away the TreeType parameter and allowing it to be specified at runtime in this class. More...  
class  RAQueryStat 
Extra data for each node in the tree. More...  
class  RASearch 
The RASearch class: This class provides a generic manner to perform rankapproximate search via randomsampling. More...  
class  RASearchRules 
class  RAUtil 
Typedefs  
typedef NeighborSearch< FurthestNeighborSort, metric::EuclideanDistance >  AllkFN 
typedef NeighborSearch< NearestNeighborSort, metric::EuclideanDistance >  AllkNN 
typedef RASearch  AllkRAFN 
typedef RASearch  AllkRANN 
typedef NeighborSearch< FurthestNeighborSort, metric::EuclideanDistance >  KFN 
The KFN class is the kfurthestneighbors method. More...  
typedef NeighborSearch< NearestNeighborSort, metric::EuclideanDistance >  KNN 
The KNN class is the knearestneighbors method. More...  
typedef RASearch< FurthestNeighborSort >  KRAFN 
The KRAFN class is the krankapproximatefarthestneighbors method. More...  
typedef RASearch  KRANN 
The KRANN class is the krankapproximatenearestneighbors method. More...  
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) 
Assuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kdtree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors. More...  
void  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 bool squareRoot=false) 
Assuming that the datasets have been mapped using referenceMap (such as during kdtree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors. More...  
Detailed Description
Neighborsearch routines.
These include allnearestneighbors and allfurthestneighbors searches.
Typedef Documentation
It returns L2 distances (Euclidean distances) for each of the k furthest neighbors. This typedef will be removed in mlpack 3.0.0; use the KFN typedef instead.
Definition at line 56 of file typedef.hpp.
It returns L2 distances (Euclidean distances) for each of the k nearest neighbors. This typedef will be removed in mlpack 3.0.0; use the KNN typedef instead.
Definition at line 48 of file typedef.hpp.
typedef RASearch mlpack::neighbor::AllkRAFN 
It returns L2 distances for each of the k rankapproximate farthestneighbors.
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.
This typedef will be removed in mlpack 3.0.0; use the KRANN typedef instead.
Definition at line 78 of file ra_typedef.hpp.
typedef RASearch mlpack::neighbor::AllkRANN 
It returns L2 distances for each of the k rankapproximate nearestneighbors.
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.
This typedef will be removed in mlpack 3.0.0; use the KRANN typedef instead.
Definition at line 63 of file ra_typedef.hpp.
The KFN class is the kfurthestneighbors method.
It returns L2 distances (Euclidean distances) for each of the k furthest neighbors.
Definition at line 40 of file typedef.hpp.
The KNN class is the knearestneighbors method.
It returns L2 distances (Euclidean distances) for each of the k nearest neighbors.
Definition at line 34 of file typedef.hpp.
The KRAFN class is the krankapproximatefarthestneighbors method.
It returns L2 distances for each of the k rankapproximate farthestneighbors.
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.
typedef RASearch mlpack::neighbor::KRANN 
The KRANN class is the krankapproximatenearestneighbors method.
It returns L2 distances for each of the k rankapproximate nearestneighbors.
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 38 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 bool  squareRoot = false 

) 
Assuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kdtree 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 dualtree 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 bool  squareRoot = false 

) 
Assuming that the datasets have been mapped using referenceMap (such as during kdtree 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 singletree 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.
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