Package | Description |
---|---|
de.lmu.ifi.dbs.elki.database.query.distance |
Prepared queries for distances
|
de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries, that return all objects within
the radius ε
|
de.lmu.ifi.dbs.elki.database.query.similarity |
Prepared queries for similarity functions
|
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic |
Distance from probability theory, mostly divergences such as K-L-divergence,
J-divergence, F-divergence, χ²-divergence, etc.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.set |
Distance functions for binary and set type data.
|
de.lmu.ifi.dbs.elki.distance.similarityfunction |
Similarity functions
|
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster |
Similarity measures for comparing clusters.
|
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
|
Modifier and Type | Field and Description |
---|---|
private PrimitiveSimilarityFunction<? super O> |
PrimitiveDistanceSimilarityQuery.similarityFunction
Typed reference to the similarity function (usually the same as the
distance function!)
|
private PrimitiveSimilarityFunction<? super O> |
SpatialPrimitiveDistanceSimilarityQuery.similarityFunction
Typed reference to the similarity function (usually the same as the
distance function!)
|
Modifier and Type | Method and Description |
---|---|
PrimitiveSimilarityFunction<? super O> |
PrimitiveDistanceSimilarityQuery.getSimilarityFunction() |
PrimitiveSimilarityFunction<? super O> |
SpatialPrimitiveDistanceSimilarityQuery.getSimilarityFunction() |
Constructor and Description |
---|
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation,
PrimitiveDistanceFunction<? super O> distanceFunction,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
SpatialPrimitiveDistanceSimilarityQuery(Relation<? extends O> relation,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private PrimitiveSimilarityFunction<? super O> |
LinearScanPrimitiveSimilarityRangeQuery.rawsim
Unboxed similarity function.
|
Modifier and Type | Field and Description |
---|---|
protected PrimitiveSimilarityFunction<? super O> |
PrimitiveSimilarityQuery.similarityFunction
The distance function we use.
|
Modifier and Type | Method and Description |
---|---|
PrimitiveSimilarityFunction<? super O> |
PrimitiveSimilarityQuery.getSimilarityFunction() |
Constructor and Description |
---|
PrimitiveSimilarityQuery(Relation<? extends O> relation,
PrimitiveSimilarityFunction<? super O> similarityFunction)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
HellingerDistanceFunction
Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity
similarity, Matusita distance, Hellinger-Kakutani metric on a probability
distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
JaccardSimilarityDistanceFunction
A flexible extension of Jaccard similarity to non-binary vectors.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DBIDSimilarityFunction
Interface DBIDSimilarityFunction describes the requirements of any similarity
function defined over object IDs.
|
interface |
NormalizedPrimitiveSimilarityFunction<O>
Marker interface for similarity functions working on primitive objects, and
limited to the 0-1 value range.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDBIDSimilarityFunction
Abstract super class for distance functions needing a preprocessor.
|
class |
AbstractVectorSimilarityFunction
Abstract base class for double-valued primitive similarity functions.
|
class |
InvertedDistanceSimilarityFunction<O>
Adapter to use a primitive number-distance as similarity measure, by
computing 1/distance.
|
class |
Kulczynski1SimilarityFunction
Kulczynski similarity 1.
|
class |
Kulczynski2SimilarityFunction
Kulczynski similarity 2.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ClusteringDistanceSimilarityFunction
Distance and similarity measure for clusterings.
|
Modifier and Type | Class and Description |
---|---|
class |
ClusteringAdjustedRandIndexSimilarityFunction
Measure the similarity of clusters via the Adjusted Rand Index.
|
class |
ClusteringBCubedF1SimilarityFunction
Measure the similarity of clusters via the BCubed F1 Index.
|
class |
ClusteringFowlkesMallowsSimilarityFunction
Measure the similarity of clusters via the Fowlkes-Mallows Index.
|
class |
ClusteringRandIndexSimilarityFunction
Measure the similarity of clusters via the Rand Index.
|
class |
ClusterIntersectionSimilarityFunction
Measure the similarity of clusters via the intersection size.
|
class |
ClusterJaccardSimilarityFunction
Measure the similarity of clusters via the Jaccard coefficient.
|
Modifier and Type | Class and Description |
---|---|
class |
LaplaceKernelFunction
Laplace / exponential radial basis function kernel.
|
class |
LinearKernelFunction
Linear Kernel function that computes a similarity between the two feature
vectors x and y defined by \(x^T\cdot y\).
|
class |
PolynomialKernelFunction
Polynomial Kernel function that computes a similarity between the two feature
vectors x and y defined by \((x^T\cdot y+b)^{\text{degree}}\).
|
class |
RadialBasisFunctionKernelFunction
Gaussian radial basis function kernel (RBF Kernel).
|
class |
RationalQuadraticKernelFunction
Rational quadratic kernel, a less computational approximation of the Gaussian
RBF kernel (
RadialBasisFunctionKernelFunction ). |
class |
SigmoidKernelFunction
Sigmoid kernel function (aka: hyperbolic tangent kernel, multilayer
perceptron MLP kernel).
|
Constructor and Description |
---|
KernelMatrix(PrimitiveSimilarityFunction<? super O> kernelFunction,
Relation<? extends O> relation,
DBIDs ids)
Provides a new kernel matrix.
|
Copyright © 2019 ELKI Development Team. License information.