Uses of Interface
moa.classifiers.lazy.neighboursearch.DistanceFunction

Packages that use DistanceFunction

Package	Description
moa.classifiers.lazy.neighboursearch

Uses of DistanceFunction in moa.classifiers.lazy.neighboursearch

Classes in moa.classifiers.lazy.neighboursearch that implement DistanceFunction

Modifier and Type	Class and Description
class	EuclideanDistance Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
class	NormalizableDistance Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.

Fields in moa.classifiers.lazy.neighboursearch declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction	NearestNeighbourSearch.m_DistanceFunction the distance function used.

Methods in moa.classifiers.lazy.neighboursearch that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction	NearestNeighbourSearch.getDistanceFunction() returns the distance function currently in use.
DistanceFunction	KDTree.getDistanceFunction() returns the distance function currently in use.

Methods in moa.classifiers.lazy.neighboursearch with parameters of type DistanceFunction

Modifier and Type	Method and Description
void	NearestNeighbourSearch.setDistanceFunction(DistanceFunction df) sets the distance function to use for nearest neighbour search.
void	KDTree.setDistanceFunction(DistanceFunction df) sets the distance function to use for nearest neighbour search.