Package | Description |
---|---|
de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise |
Instancewise normalization, where each instance is normalized independently.
|
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski |
Minkowski space L_p norms such as the popular Euclidean and Manhattan distances.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.kd |
K-d-tree and variants.
|
Modifier and Type | Field and Description |
---|---|
(package private) Norm<? super V> |
LengthNormalization.norm
Norm to use.
|
(package private) Norm<? super V> |
LengthNormalization.Parameterizer.norm
Norm to use.
|
Constructor and Description |
---|
LengthNormalization(Norm<? super V> norm)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractNumberVectorNorm
Abstract base class for double-valued number-vector-based distances based on
norms.
|
class |
AbstractSpatialNorm
Abstract base class for typical distance functions that allow
rectangle-to-rectangle lower bounds.
|
class |
LorentzianDistanceFunction
Lorentzian distance function for vector spaces.
|
Modifier and Type | Class and Description |
---|---|
class |
EuclideanDistanceFunction
Euclidean distance for
NumberVector s. |
class |
LPIntegerNormDistanceFunction
LP-Norm for
NumberVector s, optimized version for integer values of p. |
class |
LPNormDistanceFunction
LP-Norm for
NumberVector s. |
class |
ManhattanDistanceFunction
Manhattan distance for
NumberVector s. |
class |
MaximumDistanceFunction
Maximum distance for
NumberVector s. |
class |
MinimumDistanceFunction
Maximum distance for
NumberVector s. |
class |
SparseEuclideanDistanceFunction
Euclidean distance function, optimized for
SparseNumberVector s. |
class |
SparseLPNormDistanceFunction
LP-Norm, optimized for
SparseNumberVector s. |
class |
SparseManhattanDistanceFunction
Manhattan distance, optimized for
SparseNumberVector s. |
class |
SparseMaximumDistanceFunction
Maximum distance, optimized for
SparseNumberVector s. |
class |
SquaredEuclideanDistanceFunction
Squared Euclidean distance, optimized for
SparseNumberVector s. |
class |
WeightedEuclideanDistanceFunction
Weighted Euclidean distance for
NumberVector s. |
class |
WeightedLPNormDistanceFunction
Weighted version of the Minkowski L_p norm distance for
NumberVector . |
class |
WeightedManhattanDistanceFunction
Weighted version of the Minkowski L_p metrics distance function for
NumberVector s. |
class |
WeightedMaximumDistanceFunction
Weighted version of the Minkowski L_p metrics distance function for
NumberVector s. |
class |
WeightedSquaredEuclideanDistanceFunction
Squared Euclidean distance for
NumberVector s. |
Modifier and Type | Class and Description |
---|---|
class |
OnedimensionalDistanceFunction
Distance function that computes the distance between feature vectors as the
absolute difference of their values in a specified dimension only.
|
class |
SubspaceEuclideanDistanceFunction
Euclidean distance function between
NumberVector s only in specified
dimensions. |
class |
SubspaceLPNormDistanceFunction
LP-Norm distance function between
NumberVector s only in specified
dimensions. |
class |
SubspaceManhattanDistanceFunction
Manhattan distance function between
NumberVector s only in specified
dimensions. |
class |
SubspaceMaximumDistanceFunction
Maximum distance function between
NumberVector s only in specified
dimensions. |
Modifier and Type | Field and Description |
---|---|
private Norm<? super O> |
SmallMemoryKDTree.KDTreeKNNQuery.norm
Norm to use.
|
private Norm<? super O> |
SmallMemoryKDTree.KDTreeRangeQuery.norm
Norm to use.
|
private Norm<? super O> |
MinimalisticMemoryKDTree.KDTreeKNNQuery.norm
Norm to use.
|
private Norm<? super O> |
MinimalisticMemoryKDTree.KDTreeRangeQuery.norm
Norm to use.
|
Constructor and Description |
---|
MinimalisticMemoryKDTree.KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
MinimalisticMemoryKDTree.KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
SmallMemoryKDTree.KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
SmallMemoryKDTree.KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.