MLPACK  1.0.4
Classes | Typedefs
mlpack::neighbor Namespace Reference

Neighbor-search 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 distance-approximate nearest-neighbors 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 distance-based neighbor searches. More...
class  NeighborSearchRules
class  QueryStat
 Extra data for each node in the tree. More...

Typedefs

typedef NeighborSearch
< FurthestNeighborSort,
metric::SquaredEuclideanDistance
AllkFN
 The AllkFN class is the all-k-furthest-neighbors method.
typedef NeighborSearch
< NearestNeighborSort,
metric::SquaredEuclideanDistance
AllkNN
 The AllkNN class is the all-k-nearest-neighbors method.

Detailed Description

Neighbor-search routines.

These include all-nearest-neighbors and all-furthest-neighbors searches.


Typedef Documentation

The AllkFN class is the all-k-furthest-neighbors method.

It returns squared L2 distances (squared Euclidean distances) for each of the k furthest neighbors. Squared distances are used because they are slightly faster than non-squared distances (they have one fewer call to sqrt()).

Definition at line 54 of file typedef.hpp.

The AllkNN class is the all-k-nearest-neighbors method.

It returns squared L2 distances (squared Euclidean distances) for each of the k nearest neighbors. Squared distances are used because they are slightly faster than non-squared distances (they have one fewer call to sqrt()).

Definition at line 45 of file typedef.hpp.