Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms
Clustering algorithms are supposed to implement the
Algorithm -Interface. |
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
Axis-parallel subspace clustering algorithms
The clustering algorithms in this package are instances of both, projected clustering algorithms or
subspace clustering algorithms according to the classical but somewhat obsolete classification schema
of clustering algorithms for axis-parallel subspaces.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique |
Helper classes for the
CLIQUE algorithm. |
de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
de.lmu.ifi.dbs.elki.application.visualization |
Visualization applications in ELKI.
|
de.lmu.ifi.dbs.elki.data |
Basic classes for different data types, database object types and label types.
|
de.lmu.ifi.dbs.elki.data.model |
Cluster models classes for various algorithms.
|
de.lmu.ifi.dbs.elki.data.type |
Data type information, also used for type restrictions.
|
de.lmu.ifi.dbs.elki.datasource.filter |
Data filtering, in particular for normalization and projection.
|
de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types.
|
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram |
Distance functions using correlations.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
Distance functions using correlations.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.geo |
Geographic (earth) distance functions.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries |
Distance functions designed for time series.
|
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
|
de.lmu.ifi.dbs.elki.index.preprocessed |
Index structure based on preprocessors
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
|
de.lmu.ifi.dbs.elki.index.preprocessed.preference |
Indexes storing preference vectors.
|
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj |
Index using a preprocessed local subspaces.
|
de.lmu.ifi.dbs.elki.index.tree.spatial |
Tree-based index structures for spatial indexing.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants |
R*-Tree and variants.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar | |
de.lmu.ifi.dbs.elki.math |
Mathematical operations and utilities used throughout the framework.
|
de.lmu.ifi.dbs.elki.math.linearalgebra |
Linear Algebra package provides classes and computational methods for operations on matrices.
|
de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
de.lmu.ifi.dbs.elki.math.spacefillingcurves |
Space filling curves.
|
de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
|
de.lmu.ifi.dbs.elki.utilities.referencepoints |
Package containing strategies to obtain reference points
Shared code for various algorithms that use reference points.
|
de.lmu.ifi.dbs.elki.visualization.projections |
Visualization projections
|
de.lmu.ifi.dbs.elki.visualization.projector |
Projectors are responsible for finding appropriate projections for data relations.
|
de.lmu.ifi.dbs.elki.visualization.scales |
Scales handling for plotting.
|
de.lmu.ifi.dbs.elki.visualization.svg |
Base SVG functionality (generation, markers, thumbnails, export, ...).
|
de.lmu.ifi.dbs.elki.visualization.visualizers |
Visualizers for various results
|
de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d |
Visualizers based on 1D projections.
|
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d |
Visualizers based on 2D projections.
|
Modifier and Type | Class and Description |
---|---|
class |
DependencyDerivator<V extends NumberVector<V,?>,D extends Distance<D>>
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.
|
static class |
DependencyDerivator.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>
Parameterization class.
|
class |
DummyAlgorithm<O extends NumberVector<?,?>>
Dummy Algorithm, which just iterates over all points once, doing a 10NN query
each.
|
class |
KNNJoin<V extends NumberVector<V,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its k-nearest neighbors.
|
static class |
KNNJoin.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>>
|
class |
AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
|
static class |
AbstractProjectedDBSCAN.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>
Parameterization class.
|
class |
DeLiClu<NV extends NumberVector<NV,?>,D extends Distance<D>>
DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find
density-connected sets in a database.
|
static class |
DeLiClu.Parameterizer<NV extends NumberVector<NV,?>,D extends Distance<D>>
Parameterization class.
|
class |
EM<V extends NumberVector<V,?>>
Provides the EM algorithm (clustering by expectation maximization).
|
static class |
EM.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
KMeans<V extends NumberVector<V,?>,D extends Distance<D>>
Provides the k-means algorithm.
|
static class |
KMeans.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
COPAC<V extends NumberVector<V,?>,D extends Distance<D>>
Provides the COPAC algorithm, an algorithm to partition a database according
to the correlation dimension of its objects and to then perform an arbitrary
clustering algorithm over the partitions.
|
static class |
COPAC.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>
Parameterization class.
|
class |
ERiC<V extends NumberVector<V,?>>
Performs correlation clustering on the data partitioned according to local
correlation dimensionality and builds a hierarchy of correlation clusters
that allows multiple inheritance from the clustering result.
|
static class |
ERiC.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
FourC<V extends NumberVector<V,?>>
4C identifies local subgroups of data objects sharing a uniform correlation.
|
static class |
FourC.Parameterizer<O extends NumberVector<O,?>>
Parameterization class.
|
class |
HiCO<V extends NumberVector<V,?>>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies
of correlation clusters.
|
static class |
HiCO.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
ORCLUS<V extends NumberVector<V,?>>
ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high
dimensional spaces.
|
static class |
ORCLUS.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
(package private) V |
ORCLUS.ORCLUSCluster.centroid
The centroid of this cluster.
|
Modifier and Type | Class and Description |
---|---|
class |
CLIQUE<V extends NumberVector<V,?>>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify
dense clusters in subspaces of maximum dimensionality.
|
static class |
CLIQUE.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
DiSH<V extends NumberVector<V,?>>
Algorithm for detecting subspace hierarchies.
|
static class |
DiSH.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
HiSC<V extends NumberVector<V,?>>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies
of subspace clusters.
|
static class |
HiSC.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
PreDeCon<V extends NumberVector<V,?>>
PreDeCon computes clusters of subspace preference weighted connected points.
|
static class |
PreDeCon.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
PROCLUS<V extends NumberVector<V,?>>
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in
high dimensional spaces.
|
static class |
PROCLUS.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
SUBCLU<V extends NumberVector<V,?>>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily
shaped and positioned clusters in subspaces.
|
static class |
SUBCLU.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
(package private) V |
PROCLUS.PROCLUSCluster.centroid
The centroids of this cluster along each dimension.
|
Modifier and Type | Class and Description |
---|---|
class |
CLIQUESubspace<V extends NumberVector<V,?>>
Represents a subspace of the original data space in the CLIQUE algorithm.
|
class |
CLIQUEUnit<V extends NumberVector<V,?>>
Represents a unit in the CLIQUE algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
ABOD<V extends NumberVector<V,?>>
Angle-Based Outlier Detection
Outlier detection using variance analysis on angles, especially for high
dimensional data sets.
|
static class |
ABOD.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
AbstractAggarwalYuOutlier<V extends NumberVector<?,?>>
Abstract base class for the sparse-grid-cell based outlier detection of
Aggarwal and Yu.
|
class |
AggarwalYuEvolutionary<V extends NumberVector<?,?>>
EAFOD provides the evolutionary outlier detection algorithm, an algorithm to
detect outliers for high dimensional data.
|
static class |
AggarwalYuEvolutionary.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
class |
AggarwalYuNaive<V extends NumberVector<?,?>>
BruteForce provides a naive brute force algorithm in which all k-subsets of
dimensions are examined and calculates the sparsity coefficient to find
outliers.
|
static class |
AggarwalYuNaive.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
class |
EMOutlier<V extends NumberVector<V,?>>
outlier detection algorithm using EM Clustering.
|
static class |
EMOutlier.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
GaussianModel<V extends NumberVector<V,?>>
Outlier have smallest GMOD_PROB: the outlier scores is the
probability density of the assumed distribution.
|
static class |
GaussianModel.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
GaussianUniformMixture<V extends NumberVector<V,?>>
Outlier detection algorithm using a mixture model approach.
|
static class |
GaussianUniformMixture.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
ReferenceBasedOutlierDetection<V extends NumberVector<?,?>,D extends NumberDistance<D,?>>
provides the Reference-Based Outlier Detection algorithm, an algorithm that
computes kNN distances approximately, using reference points.
|
static class |
ReferenceBasedOutlierDetection.Parameterizer<V extends NumberVector<?,?>,D extends NumberDistance<D,?>>
Parameterization class.
|
class |
SOD<V extends NumberVector<V,?>> |
static class |
SOD.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
static class |
SOD.SODModel<O extends NumberVector<O,?>> |
Modifier and Type | Field and Description |
---|---|
private O |
SOD.SODModel.center |
Modifier and Type | Method and Description |
---|---|
OutlierResult |
FeatureBagging.run(Relation<NumberVector<?,?>> relation)
Run the algorithm on a data set.
|
Modifier and Type | Class and Description |
---|---|
class |
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?,?>,D extends NumberDistance<D,?>>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
|
static class |
CTLuGLSBackwardSearchAlgorithm.Parameterizer<V extends NumberVector<?,?>,D extends NumberDistance<D,?>>
Parameterization class
|
class |
CTLuMeanMultipleAttributes<N,O extends NumberVector<?,?>>
Mean Approach is used to discover spatial outliers with multiple attributes.
|
static class |
CTLuMeanMultipleAttributes.Parameterizer<N,O extends NumberVector<?,?>>
Parameterization class.
|
class |
CTLuMedianMultipleAttributes<N,O extends NumberVector<?,?>>
Median Approach is used to discover spatial outliers with multiple
attributes.
|
static class |
CTLuMedianMultipleAttributes.Parameterizer<N,O extends NumberVector<?,?>>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Run the algorithm
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<N> spatial,
Relation<? extends NumberVector<?,?>> relation)
Run the algorithm
|
OutlierResult |
CTLuScatterplotOutlier.run(Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuMedianAlgorithm.run(Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Relation<V> relationx,
Relation<? extends NumberVector<?,?>> relationy)
Run the algorithm
|
private Pair<DBID,Double> |
CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx,
Relation<? extends NumberVector<?,?>> relationy)
Run a single iteration of the GLS-SOD modeling step
|
Modifier and Type | Class and Description |
---|---|
class |
EvaluateRankingQuality<V extends NumberVector<V,?>,D extends NumberDistance<D,?>>
Evaluate a distance function with respect to kNN queries.
|
static class |
EvaluateRankingQuality.Parameterizer<V extends NumberVector<V,?>,D extends NumberDistance<D,?>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
KNNExplorer<O extends NumberVector<?,?>,D extends NumberDistance<D,?>>
User application to explore the k Nearest Neighbors for a given data set and
distance function.
|
static class |
KNNExplorer.Parameterizer<O extends NumberVector<?,?>,D extends NumberDistance<D,?>>
Parameterization class.
|
Modifier and Type | Interface and Description |
---|---|
interface |
NumberVector<V extends NumberVector<? extends V,N>,N extends Number>
Interface NumberVector defines the methods that should be implemented by any
Object that is element of a real vector space of type N.
|
Modifier and Type | Interface and Description |
---|---|
interface |
SparseNumberVector<V extends SparseNumberVector<V,N>,N extends Number>
Combines the SparseFeatureVector and NumberVector
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractNumberVector<V extends AbstractNumberVector<? extends V,N>,N extends Number>
AbstractNumberVector is an abstract implementation of FeatureVector.
|
class |
BitVector
Provides a BitVector wrapping a BitSet.
|
class |
DoubleVector
A DoubleVector is to store real values approximately as double values.
|
class |
FloatVector
A FloatVector is to store real values approximately as float values.
|
class |
IntegerVector
An IntegerVector is to store integer values.
|
class |
OneDimensionalDoubleVector
Specialized class implementing a one-dimensional double vector without using
an array.
|
class |
ParameterizationFunction
A parameterization function describes all lines in a d-dimensional feature
space intersecting in one point p.
|
class |
SparseFloatVector
A SparseFloatVector is to store real values approximately as float values.
|
Modifier and Type | Method and Description |
---|---|
static <V extends NumberVector<?,?>> |
VectorUtil.getRangeDouble(V vec)
Return the range across all dimensions.
|
static <V extends NumberVector<V,?>> |
VectorUtil.randomVector(V template)
Produce a new vector based on random numbers in [0:1] of the same type and
dimensionality as the given vector.
|
static <V extends NumberVector<V,?>> |
VectorUtil.randomVector(V template,
Random r)
Produce a new vector based on random numbers in [0:1] of the same type and
dimensionality as the given vector.
|
Modifier and Type | Class and Description |
---|---|
class |
CorrelationAnalysisSolution<V extends NumberVector<V,?>>
A solution of correlation analysis is a matrix of equations describing the
dependencies.
|
Modifier and Type | Field and Description |
---|---|
static VectorFieldTypeInformation<NumberVector<?,?>> |
TypeUtil.NUMBER_VECTOR_FIELD
Input type for algorithms that require number vector fields.
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractFeatureSelectionFilter.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
static class |
AbstractRandomFeatureSelectionFilter.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
AttributeWiseErfNormalization<O extends NumberVector<O,?>>
Attribute-wise Normalization using the error function.
|
class |
AttributeWiseMinMaxNormalization<V extends NumberVector<V,?>>
Class to perform and undo a normalization on real vectors with respect to
given minimum and maximum in each dimension.
|
static class |
AttributeWiseMinMaxNormalization.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
AttributeWiseVarianceNormalization<V extends NumberVector<V,?>>
Class to perform and undo a normalization on real vectors with respect to
given mean and standard deviation in each dimension.
|
static class |
AttributeWiseVarianceNormalization.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
SplitNumberVectorFilter<V extends NumberVector<V,?>>
Split an existing column into two types.
|
static class |
SplitNumberVectorFilter.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
NumberVectorLabelParser<V extends NumberVector<?,?>>
Provides a parser for parsing one point per line, attributes separated by
whitespace.
|
static class |
NumberVectorLabelParser.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
Modifier and Type | Interface and Description |
---|---|
interface |
FilteredLocalPCABasedDistanceFunction<O extends NumberVector<?,?>,P extends FilteredLocalPCAIndex<? super O>,D extends Distance<D>>
Interface for local PCA based preprocessors.
|
static interface |
FilteredLocalPCABasedDistanceFunction.Instance<T extends NumberVector<?,?>,I extends Index,D extends Distance<D>>
Instance produced by the distance function.
|
class |
LocallyWeightedDistanceFunction<V extends NumberVector<?,?>>
Provides a locally weighted distance function.
|
static class |
LocallyWeightedDistanceFunction.Instance<V extends NumberVector<?,?>>
Instance of this distance for a particular database.
|
static class |
LocallyWeightedDistanceFunction.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
<T extends NumberVector<?,?>> |
AbstractCosineDistanceFunction.instantiate(Relation<T> relation) |
<T extends NumberVector<?,?>> |
SquaredEuclideanDistanceFunction.instantiate(Relation<T> relation) |
<T extends NumberVector<?,?>> |
ManhattanDistanceFunction.instantiate(Relation<T> relation) |
<T extends NumberVector<?,?>> |
EuclideanDistanceFunction.instantiate(Relation<T> relation) |
<T extends NumberVector<?,?>> |
MaximumDistanceFunction.instantiate(Relation<T> relation) |
<T extends NumberVector<?,?>> |
MinimumDistanceFunction.instantiate(Relation<T> relation) |
Modifier and Type | Method and Description |
---|---|
SimpleTypeInformation<? super NumberVector<?,?>> |
AbstractVectorDoubleDistanceFunction.getInputTypeRestriction() |
VectorFieldTypeInformation<? super NumberVector<?,?>> |
WeightedDistanceFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
protected double |
AbstractCosineDistanceFunction.angle(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Compute the angle between two vectors.
|
protected double |
AbstractCosineDistanceFunction.angle(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Compute the angle between two vectors.
|
DoubleDistance |
AbstractVectorDoubleDistanceFunction.distance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
DoubleDistance |
AbstractVectorDoubleDistanceFunction.distance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
SquaredEuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the squared Euclidean distance between the given two vectors.
|
double |
SquaredEuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the squared Euclidean distance between the given two vectors.
|
double |
ManhattanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Compute the Manhattan distance
|
double |
ManhattanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Compute the Manhattan distance
|
double |
EuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Euclidean distance between the given two vectors.
|
double |
EuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Euclidean distance between the given two vectors.
|
double |
MaximumDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
MaximumDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
WeightedLPNormDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
WeightedLPNormDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
MinimumDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
MinimumDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
ArcCosineDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the cosine distance for two given feature vectors.
|
double |
ArcCosineDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the cosine distance for two given feature vectors.
|
double |
WeightedSquaredEuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the squared Euclidean distance between the given two vectors.
|
double |
WeightedSquaredEuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the squared Euclidean distance between the given two vectors.
|
double |
LPNormDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Returns the distance between the specified FeatureVectors as a LP-Norm for
the currently set p.
|
double |
LPNormDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Returns the distance between the specified FeatureVectors as a LP-Norm for
the currently set p.
|
double |
CosineDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the cosine distance for two given feature vectors.
|
double |
CosineDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the cosine distance for two given feature vectors.
|
double |
WeightedDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2)
Provides the Weighted distance for feature vectors.
|
double |
WeightedDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2)
Provides the Weighted distance for feature vectors.
|
protected double |
SquaredEuclideanDistanceFunction.doubleMinDistObject(SpatialComparable mbr,
NumberVector<?,?> v) |
protected double |
ManhattanDistanceFunction.doubleMinDistObject(SpatialComparable mbr,
NumberVector<?,?> v) |
protected double |
EuclideanDistanceFunction.doubleMinDistObject(SpatialComparable mbr,
NumberVector<?,?> v) |
Modifier and Type | Method and Description |
---|---|
<T extends NumberVector<?,?>> |
HistogramIntersectionDistanceFunction.instantiate(Relation<T> relation) |
Modifier and Type | Method and Description |
---|---|
double |
HistogramIntersectionDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
double |
HistogramIntersectionDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2) |
Modifier and Type | Class and Description |
---|---|
static class |
ERiCDistanceFunction.Instance<V extends NumberVector<?,?>>
The actual instance bound to a particular database.
|
static class |
PCABasedCorrelationDistanceFunction.Instance<V extends NumberVector<?,?>>
The actual instance bound to a particular database.
|
Modifier and Type | Method and Description |
---|---|
<T extends NumberVector<?,?>> |
PCABasedCorrelationDistanceFunction.instantiate(Relation<T> database) |
<T extends NumberVector<?,?>> |
ERiCDistanceFunction.instantiate(Relation<T> database) |
Modifier and Type | Method and Description |
---|---|
BitDistance |
ERiCDistanceFunction.distance(NumberVector<?,?> v1,
NumberVector<?,?> v2,
PCAFilteredResult pca1,
PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
BitDistance |
ERiCDistanceFunction.distance(NumberVector<?,?> v1,
NumberVector<?,?> v2,
PCAFilteredResult pca1,
PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
double |
SquaredPearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
double |
SquaredPearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
double |
PearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
PearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
WeightedPearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
WeightedPearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the Pearson correlation distance for two given feature vectors.
|
double |
WeightedSquaredPearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
double |
WeightedSquaredPearsonCorrelationDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
Constructor and Description |
---|
ERiCDistanceFunction(IndexFactory<NumberVector<?,?>,FilteredLocalPCAIndex<NumberVector<?,?>>> indexFactory,
double delta,
double tau)
Constructor.
|
ERiCDistanceFunction(IndexFactory<NumberVector<?,?>,FilteredLocalPCAIndex<NumberVector<?,?>>> indexFactory,
double delta,
double tau)
Constructor.
|
PCABasedCorrelationDistanceFunction(IndexFactory<NumberVector<?,?>,FilteredLocalPCAIndex<NumberVector<?,?>>> indexFactory,
double delta)
Constructor
|
PCABasedCorrelationDistanceFunction(IndexFactory<NumberVector<?,?>,FilteredLocalPCAIndex<NumberVector<?,?>>> indexFactory,
double delta)
Constructor
|
Modifier and Type | Method and Description |
---|---|
SimpleTypeInformation<? super NumberVector<?,?>> |
LngLatDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector<?,?>> |
LatLngDistanceFunction.getInputTypeRestriction() |
SimpleTypeInformation<? super NumberVector<?,?>> |
DimensionSelectingLatLngDistanceFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
double |
LngLatDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
LngLatDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
LatLngDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
LatLngDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
DimensionSelectingLatLngDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
DimensionSelectingLatLngDistanceFunction.doubleDistance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
Modifier and Type | Class and Description |
---|---|
class |
AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<?,?>,P extends PreferenceVectorIndex<V>>
Abstract super class for all preference vector based correlation distance
functions.
|
static class |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance<V extends NumberVector<?,?>,P extends PreferenceVectorIndex<V>>
Instance to compute the distances on an actual database.
|
static class |
DiSHDistanceFunction.Instance<V extends NumberVector<?,?>>
The actual instance bound to a particular database.
|
class |
HiSCDistanceFunction<V extends NumberVector<?,?>>
Distance function used in the HiSC algorithm.
|
static class |
HiSCDistanceFunction.Instance<V extends NumberVector<?,?>>
The actual instance bound to a particular database.
|
static class |
HiSCDistanceFunction.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
static class |
SubspaceDistanceFunction.Instance<V extends NumberVector<?,?>>
The actual instance bound to a particular database.
|
Modifier and Type | Method and Description |
---|---|
<T extends NumberVector<?,?>> |
DimensionsSelectingEuclideanDistanceFunction.instantiate(Relation<T> database) |
<T extends NumberVector<?,?>> |
DiSHDistanceFunction.instantiate(Relation<T> database) |
<T extends NumberVector<?,?>> |
DimensionSelectingDistanceFunction.instantiate(Relation<T> database) |
<V extends NumberVector<?,?>> |
SubspaceDistanceFunction.instantiate(Relation<V> database) |
Modifier and Type | Method and Description |
---|---|
VectorFieldTypeInformation<? super NumberVector<?,?>> |
DimensionsSelectingEuclideanDistanceFunction.getInputTypeRestriction() |
VectorTypeInformation<? super NumberVector<?,?>> |
DimensionSelectingDistanceFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
DoubleDistance |
DimensionSelectingDistanceFunction.distance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
DoubleDistance |
DimensionSelectingDistanceFunction.distance(NumberVector<?,?> o1,
NumberVector<?,?> o2) |
double |
DimensionsSelectingEuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Euclidean distance between two given feature vectors in the
selected dimensions.
|
double |
DimensionsSelectingEuclideanDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Euclidean distance between two given feature vectors in the
selected dimensions.
|
double |
DimensionSelectingDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
double |
DimensionSelectingDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the distance between two given DatabaseObjects according to this
distance function.
|
protected double |
DimensionsSelectingEuclideanDistanceFunction.doubleMinDistObject(SpatialComparable mbr,
NumberVector<?,?> v) |
Constructor and Description |
---|
DiSHDistanceFunction(DiSHPreferenceVectorIndex.Factory<NumberVector<?,?>> indexFactory,
double epsilon)
Constructor.
|
SubspaceDistanceFunction(IndexFactory<NumberVector<?,?>,FilteredLocalPCAIndex<NumberVector<?,?>>> indexFactory)
Constructor
|
SubspaceDistanceFunction(IndexFactory<NumberVector<?,?>,FilteredLocalPCAIndex<NumberVector<?,?>>> indexFactory)
Constructor
|
Modifier and Type | Method and Description |
---|---|
VectorFieldTypeInformation<? super NumberVector<?,?>> |
AbstractEditDistanceFunction.getInputTypeRestriction() |
VectorFieldTypeInformation<? super NumberVector<?,?>> |
LCSSDistanceFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
double |
ERPDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Edit Distance With Real Penalty distance between the given two
vectors.
|
double |
ERPDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Edit Distance With Real Penalty distance between the given two
vectors.
|
double |
EDRDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Edit Distance on Real Sequence distance between the given two
vectors.
|
double |
EDRDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Edit Distance on Real Sequence distance between the given two
vectors.
|
double |
DTWDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Dynamic Time Warping distance between the given two vectors.
|
double |
DTWDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Dynamic Time Warping distance between the given two vectors.
|
double |
LCSSDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Longest Common Subsequence distance between the given two
vectors.
|
double |
LCSSDistanceFunction.doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Provides the Longest Common Subsequence distance between the given two
vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
LinearKernelFunction<O extends NumberVector<?,?>>
Provides a linear Kernel function that computes a similarity between the two
feature vectors V1 and V2 defined by V1^T*V2.
|
Modifier and Type | Method and Description |
---|---|
<T extends NumberVector<?,?>> |
FooKernelFunction.instantiate(Relation<T> database) |
<T extends NumberVector<?,?>> |
PolynomialKernelFunction.instantiate(Relation<T> database) |
Modifier and Type | Method and Description |
---|---|
VectorFieldTypeInformation<? super NumberVector<?,?>> |
FooKernelFunction.getInputTypeRestriction() |
VectorFieldTypeInformation<? super NumberVector<?,?>> |
PolynomialKernelFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
DoubleDistance |
FooKernelFunction.distance(NumberVector<?,?> fv1,
NumberVector<?,?> fv2) |
DoubleDistance |
FooKernelFunction.distance(NumberVector<?,?> fv1,
NumberVector<?,?> fv2) |
DoubleDistance |
PolynomialKernelFunction.distance(NumberVector<?,?> fv1,
NumberVector<?,?> fv2) |
DoubleDistance |
PolynomialKernelFunction.distance(NumberVector<?,?> fv1,
NumberVector<?,?> fv2) |
DoubleDistance |
FooKernelFunction.similarity(NumberVector<?,?> o1,
NumberVector<?,?> o2)
Provides an experimental kernel similarity between the given two vectors.
|
DoubleDistance |
FooKernelFunction.similarity(NumberVector<?,?> o1,
NumberVector<?,?> o2)
Provides an experimental kernel similarity between the given two vectors.
|
DoubleDistance |
PolynomialKernelFunction.similarity(NumberVector<?,?> o1,
NumberVector<?,?> o2)
Provides the linear kernel similarity between the given two vectors.
|
DoubleDistance |
PolynomialKernelFunction.similarity(NumberVector<?,?> o1,
NumberVector<?,?> o2)
Provides the linear kernel similarity between the given two vectors.
|
Modifier and Type | Interface and Description |
---|---|
interface |
LocalProjectionIndex<V extends NumberVector<?,?>,P extends ProjectionResult>
Abstract index interface for local projections
|
static interface |
LocalProjectionIndex.Factory<V extends NumberVector<?,?>,I extends LocalProjectionIndex<V,?>>
Factory
|
Modifier and Type | Class and Description |
---|---|
class |
MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector<? super O,?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
static class |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory<O extends NumberVector<? super O,?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>>
The parameterizable factory.
|
static class |
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector<? super O,?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>>
Parameterization class.
|
class |
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<?,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry>
A preprocessor for annotation of the k nearest neighbors (and their
distances) to each database object.
|
Modifier and Type | Method and Description |
---|---|
SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?,?>,D,N,E> |
SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector<?,?>> relation) |
Modifier and Type | Method and Description |
---|---|
SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?,?>,D,N,E> |
SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector<?,?>> relation) |
Constructor and Description |
---|
SpatialApproximationMaterializeKNNPreprocessor.Factory(int k,
DistanceFunction<? super NumberVector<?,?>,D> distanceFunction)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractFilteredPCAIndex<NV extends NumberVector<? extends NV,?>>
Abstract base class for a local PCA based index.
|
static class |
AbstractFilteredPCAIndex.Factory<NV extends NumberVector<NV,?>,I extends AbstractFilteredPCAIndex<NV>>
Factory class
|
static class |
AbstractFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<NV,?>,I extends AbstractFilteredPCAIndex<NV>>
Parameterization class.
|
interface |
FilteredLocalPCAIndex<NV extends NumberVector<?,?>>
Interface for an index providing local PCA results.
|
static interface |
FilteredLocalPCAIndex.Factory<NV extends NumberVector<?,?>,I extends FilteredLocalPCAIndex<NV>>
Factory interface
|
class |
KNNQueryFilteredPCAIndex<NV extends NumberVector<? extends NV,?>>
Provides the local neighborhood to be considered in the PCA as the k nearest
neighbors of an object.
|
static class |
KNNQueryFilteredPCAIndex.Factory<V extends NumberVector<V,?>>
Factory class
|
static class |
KNNQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
class |
RangeQueryFilteredPCAIndex<NV extends NumberVector<? extends NV,?>>
Provides the local neighborhood to be considered in the PCA as the neighbors
within an epsilon range query of an object.
|
static class |
RangeQueryFilteredPCAIndex.Factory<V extends NumberVector<V,?>>
Factory class
|
static class |
RangeQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractPreferenceVectorIndex<NV extends NumberVector<?,?>>
Abstract base class for preference vector based algorithms.
|
static class |
AbstractPreferenceVectorIndex.Factory<V extends NumberVector<?,?>,I extends PreferenceVectorIndex<V>>
Factory class
|
class |
DiSHPreferenceVectorIndex<V extends NumberVector<?,?>>
Preprocessor for DiSH preference vector assignment to objects of a certain
database.
|
static class |
DiSHPreferenceVectorIndex.Factory<V extends NumberVector<?,?>>
Factory class
|
static class |
DiSHPreferenceVectorIndex.Factory.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
class |
HiSCPreferenceVectorIndex<V extends NumberVector<?,?>>
Preprocessor for HiSC preference vector assignment to objects of a certain
database.
|
static class |
HiSCPreferenceVectorIndex.Factory<V extends NumberVector<?,?>>
Factory class
|
static class |
HiSCPreferenceVectorIndex.Factory.Parameterizer<V extends NumberVector<?,?>>
Parameterization class.
|
interface |
PreferenceVectorIndex<NV extends NumberVector<?,?>>
Interface for an index providing preference vectors.
|
static interface |
PreferenceVectorIndex.Factory<V extends NumberVector<?,?>,I extends PreferenceVectorIndex<V>>
Factory interface
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractSubspaceProjectionIndex<NV extends NumberVector<?,?>,D extends Distance<D>,P extends ProjectionResult>
Abstract base class for a local PCA based index.
|
static class |
AbstractSubspaceProjectionIndex.Factory<NV extends NumberVector<?,?>,D extends Distance<D>,I extends AbstractSubspaceProjectionIndex<NV,D,?>>
Factory class
|
static class |
AbstractSubspaceProjectionIndex.Factory.Parameterizer<NV extends NumberVector<?,?>,D extends Distance<D>,C>
Parameterization class.
|
class |
FourCSubspaceIndex<V extends NumberVector<V,?>,D extends Distance<D>>
Preprocessor for 4C local dimensionality and locally weighted matrix
assignment to objects of a certain database.
|
static class |
FourCSubspaceIndex.Factory<V extends NumberVector<V,?>,D extends Distance<D>>
Factory class for 4C preprocessors.
|
static class |
FourCSubspaceIndex.Factory.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>>
Parameterization class.
|
class |
PreDeConSubspaceIndex<V extends NumberVector<? extends V,?>,D extends Distance<D>>
Preprocessor for PreDeCon local dimensionality and locally weighted matrix
assignment to objects of a certain database.
|
static class |
PreDeConSubspaceIndex.Factory<V extends NumberVector<? extends V,?>,D extends Distance<D>>
Factory
|
static class |
PreDeConSubspaceIndex.Factory.Parameterizer<V extends NumberVector<? extends V,?>,D extends Distance<D>>
Parameterization class.
|
interface |
SubspaceProjectionIndex<NV extends NumberVector<?,?>,P extends ProjectionResult>
Interface for an index providing local subspaces.
|
static interface |
SubspaceProjectionIndex.Factory<NV extends NumberVector<?,?>,I extends SubspaceProjectionIndex<NV,?>>
Factory interface
|
Constructor and Description |
---|
SpatialPointLeafEntry(DBID id,
NumberVector<?,?> vector)
Constructor from number vector
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractRStarTreeFactory<O extends NumberVector<O,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,I extends AbstractRStarTree<N,E> & Index>
Abstract factory for R*-Tree based trees.
|
static class |
AbstractRStarTreeFactory.Parameterizer<O extends NumberVector<O,?>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
DeLiCluTreeFactory<O extends NumberVector<O,?>>
Factory for DeLiClu R*-Trees.
|
static class |
DeLiCluTreeFactory.Parameterizer<O extends NumberVector<O,?>>
Parameterization class.
|
class |
DeLiCluTreeIndex<O extends NumberVector<?,?>>
The common use of the DeLiClu tree: indexing number vectors.
|
Constructor and Description |
---|
DeLiCluLeafEntry(DBID id,
NumberVector<?,?> vector)
Constructs a new LeafEntry object with the given parameters.
|
Modifier and Type | Class and Description |
---|---|
class |
RStarTreeFactory<O extends NumberVector<O,?>>
Factory for regular R*-Trees.
|
static class |
RStarTreeFactory.Parameterizer<O extends NumberVector<O,?>>
Parameterization class.
|
class |
RStarTreeIndex<O extends NumberVector<?,?>>
The common use of the rstar tree: indexing number vectors.
|
Modifier and Type | Method and Description |
---|---|
static double |
MathUtil.pearsonCorrelationCoefficient(NumberVector<?,?> x,
NumberVector<?,?> y)
Provides the Pearson product-moment correlation coefficient for two
FeatureVectors.
|
static double |
MathUtil.pearsonCorrelationCoefficient(NumberVector<?,?> x,
NumberVector<?,?> y)
Provides the Pearson product-moment correlation coefficient for two
FeatureVectors.
|
static double |
MathUtil.weightedPearsonCorrelationCoefficient(NumberVector<?,?> x,
NumberVector<?,?> y,
double[] weights)
Provides the Pearson product-moment correlation coefficient for two
FeatureVectors.
|
static double |
MathUtil.weightedPearsonCorrelationCoefficient(NumberVector<?,?> x,
NumberVector<?,?> y,
double[] weights)
Provides the Pearson product-moment correlation coefficient for two
FeatureVectors.
|
Modifier and Type | Method and Description |
---|---|
<F extends NumberVector<? extends F,?>> |
CovarianceMatrix.getMeanVector(Relation<? extends F> relation)
Get the mean as vector.
|
<F extends NumberVector<? extends F,?>> |
Centroid.toVector(Relation<? extends F> relation)
Get the data as vector
|
Modifier and Type | Method and Description |
---|---|
void |
ProjectedCentroid.put(NumberVector<?,?> val)
Add a single value with weight 1.0
|
void |
Centroid.put(NumberVector<?,?> val)
Add a single value with weight 1.0
|
void |
CovarianceMatrix.put(NumberVector<?,?> val)
Add a single value with weight 1.0
|
void |
ProjectedCentroid.put(NumberVector<?,?> val,
double weight)
Add data with a given weight.
|
void |
Centroid.put(NumberVector<?,?> val,
double weight)
Add data with a given weight.
|
void |
CovarianceMatrix.put(NumberVector<?,?> val,
double weight)
Add data with a given weight.
|
Modifier and Type | Method and Description |
---|---|
static ProjectedCentroid |
ProjectedCentroid.make(BitSet dims,
Relation<? extends NumberVector<?,?>> relation)
Static Constructor from a relation.
|
static ProjectedCentroid |
ProjectedCentroid.make(BitSet dims,
Relation<? extends NumberVector<?,?>> relation,
Iterable<DBID> ids)
Static Constructor from a relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector<?,?>> relation)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector<?,?>> relation)
Static Constructor from a full relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector<?,?>> relation,
Iterable<DBID> ids)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector<?,?>> relation,
Iterable<DBID> ids)
Static Constructor from a full relation.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>
Abstract class with the task of computing a Covariance matrix to be used in PCA.
|
interface |
CovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>
Interface for computing covariance matrixes on a data set.
|
class |
PCAFilteredRunner<V extends NumberVector<? extends V,?>>
PCA runner that will do dimensionality reduction.
|
static class |
PCAFilteredRunner.Parameterizer<V extends NumberVector<? extends V,?>>
Parameterization class.
|
class |
PCARunner<V extends NumberVector<? extends V,?>>
Class to run PCA on given data.
|
static class |
PCARunner.Parameterizer<V extends NumberVector<? extends V,?>>
Parameterization class.
|
class |
StandardCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>
Class for building a "traditional" covariance matrix.
|
class |
WeightedCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>
CovarianceMatrixBuilder with weights. |
static class |
WeightedCovarianceMatrixBuilder.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
BigInteger |
ZCurve.Transformer.asBigInteger(NumberVector<?,?> vector)
Transform a single vector.
|
byte[] |
ZCurve.Transformer.asByteArray(NumberVector<?,?> vector)
Transform a single vector.
|
Constructor and Description |
---|
ZCurve.Transformer(Relation<? extends NumberVector<?,?>> relation,
DBIDs ids)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static <V extends NumberVector<? extends V,?>> |
DatabaseUtil.centroid(Relation<? extends V> relation)
Returns the centroid as a NumberVector object of the specified database.
|
static <V extends NumberVector<? extends V,?>> |
DatabaseUtil.centroid(Relation<? extends V> relation,
DBIDs ids)
Returns the centroid as a NumberVector object of the specified objects
stored in the given database.
|
static <V extends NumberVector<? extends V,?>> |
DatabaseUtil.centroid(Relation<? extends V> relation,
DBIDs ids,
BitSet dimensions)
Returns the centroid w.r.t. the dimensions specified by the given BitSet as
a NumberVector object of the specified objects stored in the given
database.
|
static <NV extends NumberVector<NV,?>> |
DatabaseUtil.computeMinMax(Relation<NV> database)
Determines the minimum and maximum values in each dimension of all objects
stored in the given database.
|
static <V extends NumberVector<? extends V,?>> |
DatabaseUtil.covarianceMatrix(Relation<? extends V> database,
DBIDs ids)
Determines the covariance matrix of the objects stored in the given
database.
|
static <V extends NumberVector<?,?>> |
DatabaseUtil.exactMedian(Relation<V> relation,
DBIDs ids,
int dimension)
Returns the median of a data set in the given dimension.
|
static <V extends NumberVector<?,?>> |
DatabaseUtil.quickMedian(Relation<V> relation,
ArrayDBIDs ids,
int dimension,
int numberOfSamples)
Returns the median of a data set in the given dimension by using a sampling
method.
|
static <V extends NumberVector<?,?>,T extends NumberVector<?,?>> |
DatabaseUtil.relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.
|
static <V extends NumberVector<?,?>,T extends NumberVector<?,?>> |
DatabaseUtil.relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.
|
static <V extends NumberVector<? extends V,?>> |
DatabaseUtil.variances(Relation<V> database)
Determines the variances in each dimension of all objects stored in the
given database.
|
static <V extends NumberVector<? extends V,?>> |
DatabaseUtil.variances(Relation<V> database,
DBIDs ids)
Determines the variances in each dimension of the specified objects stored
in the given database.
|
Modifier and Type | Method and Description |
---|---|
static double[] |
DatabaseUtil.variances(Relation<? extends NumberVector<?,?>> database,
NumberVector<?,?> centroid,
DBIDs ids)
Determines the variances in each dimension of the specified objects stored
in the given database.
|
Modifier and Type | Method and Description |
---|---|
static double[] |
DatabaseUtil.variances(Relation<? extends NumberVector<?,?>> database,
NumberVector<?,?> centroid,
DBIDs ids)
Determines the variances in each dimension of the specified objects stored
in the given database.
|
Modifier and Type | Class and Description |
---|---|
class |
AxisBasedReferencePoints<V extends NumberVector<V,?>>
Strategy to pick reference points by placing them on the axis ends.
|
static class |
AxisBasedReferencePoints.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
FullDatabaseReferencePoints<O extends NumberVector<? extends O,?>>
Strategy to use the complete database as reference points.
|
class |
GridBasedReferencePoints<V extends NumberVector<V,?>>
Grid-based strategy to pick reference points.
|
static class |
GridBasedReferencePoints.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
RandomGeneratedReferencePoints<V extends NumberVector<V,?>>
Reference points generated randomly within the used data space.
|
static class |
RandomGeneratedReferencePoints.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
class |
RandomSampleReferencePoints<V extends NumberVector<? extends V,?>>
Random-Sampling strategy for picking reference points.
|
static class |
RandomSampleReferencePoints.Parameterizer<V extends NumberVector<? extends V,?>>
Parameterization class.
|
class |
StarBasedReferencePoints<V extends NumberVector<V,?>>
Star-based strategy to pick reference points.
|
static class |
StarBasedReferencePoints.Parameterizer<V extends NumberVector<V,?>>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
<NV extends NumberVector<NV,?>> |
AbstractProjection.projectRelativeRenderToDataSpace(Vector v,
NV prototype)
Project a relative vector from rendering space to data space.
|
<NV extends NumberVector<NV,?>> |
Projection.projectRelativeRenderToDataSpace(Vector v,
NV prototype)
Project a relative vector from rendering space to data space.
|
<NV extends NumberVector<NV,?>> |
AbstractProjection.projectRelativeScaledToDataSpace(Vector v,
NV prototype)
Project a relative vector from scaled space to data space.
|
<NV extends NumberVector<NV,?>> |
Projection.projectRelativeScaledToDataSpace(Vector v,
NV prototype)
Project a relative vector from scaled space to data space.
|
<NV extends NumberVector<NV,?>> |
AbstractProjection.projectRenderToDataSpace(Vector v,
NV prototype)
Project a vector from rendering space to data space.
|
<NV extends NumberVector<NV,?>> |
Projection.projectRenderToDataSpace(Vector v,
NV prototype)
Project a vector from rendering space to data space.
|
<NV extends NumberVector<NV,?>> |
AbstractProjection.projectScaledToDataSpace(Vector v,
NV factory)
Project a vector from scaled space to data space.
|
<NV extends NumberVector<NV,?>> |
Projection.projectScaledToDataSpace(Vector v,
NV factory)
Project a vector from scaled space to data space.
|
Modifier and Type | Method and Description |
---|---|
double[] |
AffineProjection.fastProjectDataToRenderSpace(NumberVector<?,?> data) |
double |
Simple1D.fastProjectDataToRenderSpace(NumberVector<?,?> data) |
double |
Projection1D.fastProjectDataToRenderSpace(NumberVector<?,?> data)
Project a data vector from data space to rendering space.
|
double[] |
Simple2D.fastProjectDataToRenderSpace(NumberVector<?,?> data) |
double[] |
Projection2D.fastProjectDataToRenderSpace(NumberVector<?,?> data)
Project a data vector from data space to rendering space.
|
double[] |
AffineProjection.fastProjectRelativeDataToRenderSpace(NumberVector<?,?> data) |
double |
Simple1D.fastProjectRelativeDataToRenderSpace(NumberVector<?,?> data) |
double |
Projection1D.fastProjectRelativeDataToRenderSpace(NumberVector<?,?> data)
Project a data vector from data space to rendering space.
|
double[] |
Simple2D.fastProjectRelativeDataToRenderSpace(NumberVector<?,?> data) |
double[] |
Projection2D.fastProjectRelativeDataToRenderSpace(NumberVector<?,?> data)
Project a data vector from data space to rendering space.
|
Vector |
AbstractProjection.projectDataToRenderSpace(NumberVector<?,?> data)
Project a data vector from data space to rendering space.
|
Vector |
Projection.projectDataToRenderSpace(NumberVector<?,?> data)
Project a data vector from data space to rendering space.
|
Vector |
AbstractProjection.projectDataToScaledSpace(NumberVector<?,?> data)
Project a data vector from data space to scaled space.
|
Vector |
Projection.projectDataToScaledSpace(NumberVector<?,?> data)
Project a data vector from data space to scaled space.
|
Vector |
AbstractProjection.projectRelativeDataToRenderSpace(NumberVector<?,?> data)
Project a relative data vector from data space to rendering space.
|
Vector |
Projection.projectRelativeDataToRenderSpace(NumberVector<?,?> data)
Project a relative data vector from data space to rendering space.
|
Vector |
AbstractProjection.projectRelativeDataToScaledSpace(NumberVector<?,?> data)
Project a relative data vector from data space to scaled space.
|
Vector |
Projection.projectRelativeDataToScaledSpace(NumberVector<?,?> data)
Project a relative data vector from data space to scaled space.
|
Modifier and Type | Class and Description |
---|---|
class |
HistogramProjector<V extends NumberVector<?,?>>
ScatterPlotProjector is responsible for producing a set of scatterplot
visualizations.
|
class |
ScatterPlotProjector<V extends NumberVector<?,?>>
ScatterPlotProjector is responsible for producing a set of scatterplot
visualizations.
|
Modifier and Type | Method and Description |
---|---|
static <O extends NumberVector<?,? extends Number>> |
Scales.calcScales(Relation<O> db)
Compute a linear scale for each dimension.
|
Modifier and Type | Method and Description |
---|---|
static <V extends NumberVector<V,?>,D extends NumberDistance<?,?>> |
SVGHyperSphere.drawCross(SVGPlot svgp,
Projection2D proj,
V mid,
D rad)
Wireframe "cross" hypersphere
|
static <V extends NumberVector<V,?>,D extends NumberDistance<?,?>> |
SVGHyperSphere.drawEuclidean(SVGPlot svgp,
Projection2D proj,
V mid,
D rad)
Wireframe "euclidean" hypersphere
|
static <V extends NumberVector<V,?>> |
SVGHyperCube.drawFilled(SVGPlot svgp,
String cls,
Projection2D proj,
V min,
V max)
Filled hypercube.
|
static <V extends NumberVector<V,?>> |
SVGHyperCube.drawFrame(SVGPlot svgp,
Projection2D proj,
V min,
V max)
Wireframe hypercube.
|
static <V extends NumberVector<V,?>,D extends NumberDistance<?,?>> |
SVGHyperSphere.drawLp(SVGPlot svgp,
Projection2D proj,
V mid,
D rad,
double p)
Wireframe "Lp" hypersphere
|
static <V extends NumberVector<V,?>,D extends NumberDistance<?,?>> |
SVGHyperSphere.drawManhattan(SVGPlot svgp,
Projection2D proj,
V mid,
D rad)
Wireframe "manhattan" hypersphere
|
Modifier and Type | Method and Description |
---|---|
static Iterator<Relation<? extends NumberVector<?,?>>> |
VisualizerUtil.iterateVectorFieldRepresentations(Result result)
Filter for number vector field representations
|
Modifier and Type | Class and Description |
---|---|
class |
P1DHistogramVisualizer<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing a histogram representing the distribution
of the database's objects.
|
static class |
P1DHistogramVisualizer.Factory<NV extends NumberVector<NV,?>>
Visualizer factory for 1D histograms
|
static class |
P1DHistogramVisualizer.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractTooltipVisualization<NV extends NumberVector<NV,?>>
General base class for a tooltip visualizer.
|
class |
AxisVisualization<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing axes, including labeling.
|
static class |
AxisVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for axis visualizations
|
class |
BubbleVisualization<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing bubbles.
|
static class |
BubbleVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for producing bubble visualizations
|
static class |
BubbleVisualization.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
class |
ClusterConvexHullVisualization<NV extends NumberVector<NV,?>>
Visualizer for generating an SVG-Element containing the convex hull of each
cluster.
|
static class |
ClusterConvexHullVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for visualizers to generate an SVG-Element containing the convex
hull of a cluster.
|
class |
ClusteringVisualization<NV extends NumberVector<NV,?>>
Visualize a clustering using different markers for different clusters.
|
static class |
ClusteringVisualization.Factory<NV extends NumberVector<NV,?>>
Visualization factory
|
class |
ClusterMeanVisualization<NV extends NumberVector<NV,?>>
Visualize the mean of a KMeans-Clustering
|
static class |
ClusterMeanVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for visualizers to generate an SVG-Element containing a marker for
the mean in a KMeans-Clustering
|
class |
ClusterOrderVisualization<NV extends NumberVector<NV,?>>
Cluster order visualizer.
|
static class |
ClusterOrderVisualization.Factory<NV extends NumberVector<NV,?>>
Visualize an OPTICS cluster order by drawing connection lines.
|
class |
DotVisualization<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing "dots" as markers representing the
Database's objects.
|
static class |
DotVisualization.Factory<NV extends NumberVector<NV,?>>
The visualization factory
|
class |
EMClusterVisualization<NV extends NumberVector<NV,?>>
Visualizer for generating SVG-Elements containing ellipses for first, second
and third standard deviation
|
static class |
EMClusterVisualization.Factory<NV extends NumberVector<NV,?>>
Visualizer for generating SVG-Elements containing ellipses for first,
second and third standard deviation
|
class |
MoveObjectsToolVisualization<NV extends NumberVector<NV,?>>
Tool to move the currently selected objects.
|
static class |
MoveObjectsToolVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for tool visualizations for changing objects in the database
|
class |
P2DVisualization<NV extends NumberVector<?,?>>
Default class to handle 2D projected visualizations.
|
class |
PolygonVisualization<V extends NumberVector<?,?>>
Renders PolygonsObject in the data set.
|
class |
ReferencePointsVisualization<NV extends NumberVector<NV,?>>
The actual visualization instance, for a single projection
|
static class |
ReferencePointsVisualization.Factory<NV extends NumberVector<NV,?>>
Generates a SVG-Element visualizing reference points.
|
class |
SelectionConvexHullVisualization<NV extends NumberVector<NV,?>>
Visualizer for generating an SVG-Element containing the convex hull of the
selected points
|
static class |
SelectionConvexHullVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for visualizers to generate an SVG-Element containing the convex
hull of the selected points
|
class |
SelectionCubeVisualization<NV extends NumberVector<NV,?>>
Visualizer for generating an SVG-Element containing a cube as marker
representing the selected range for each dimension
|
static class |
SelectionCubeVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for visualizers to generate an SVG-Element containing a cube as
marker representing the selected range for each dimension
|
static class |
SelectionCubeVisualization.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
class |
SelectionDotVisualization<NV extends NumberVector<NV,?>>
Visualizer for generating an SVG-Element containing dots as markers
representing the selected Database's objects.
|
static class |
SelectionDotVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for visualizers to generate an SVG-Element containing dots as
markers representing the selected Database's objects.
|
class |
SelectionToolCubeVisualization<NV extends NumberVector<NV,?>>
Tool-Visualization for the tool to select ranges
|
static class |
SelectionToolCubeVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for tool visualizations for selecting ranges and the inclosed
objects
|
class |
SelectionToolDotVisualization<NV extends NumberVector<NV,?>>
Tool-Visualization for the tool to select objects
|
static class |
SelectionToolDotVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for tool visualizations for selecting objects
|
class |
ToolBox2DVisualization<NV extends NumberVector<NV,?>>
Renders a tool box on the left of the 2D visualization
|
static class |
ToolBox2DVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for visualizers for a toolbox
|
class |
TooltipScoreVisualization<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing Tooltips.
|
static class |
TooltipScoreVisualization.Factory<NV extends NumberVector<NV,?>>
Factory for tooltip visualizers
|
static class |
TooltipScoreVisualization.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
class |
TooltipStringVisualization<NV extends NumberVector<NV,?>>
Generates a SVG-Element containing Tooltips.
|
static class |
TooltipStringVisualization.Factory<NV extends NumberVector<NV,?>>
Factory
|
class |
TreeMBRVisualization<NV extends NumberVector<NV,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry>
Visualize the bounding rectangles of an R-Tree based index.
|
static class |
TreeMBRVisualization.Factory<NV extends NumberVector<NV,?>>
Factory
|
static class |
TreeMBRVisualization.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
class |
TreeSphereVisualization<NV extends NumberVector<NV,?>,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<NV,D,N,E>,E extends MTreeEntry<D>>
Visualize the bounding sphere of a metric index.
|
static class |
TreeSphereVisualization.Factory<NV extends NumberVector<NV,?>>
Factory
|
static class |
TreeSphereVisualization.Factory.Parameterizer<NV extends NumberVector<NV,?>>
Parameterization class.
|
Modifier and Type | Method and Description |
---|---|
private static <NV extends NumberVector<NV,?>> |
ClusterMeanVisualization.Factory.findMeanModel(Clustering<?> c)
Test if the given clustering has a mean model.
|
private static <NV extends NumberVector<NV,?>> |
EMClusterVisualization.Factory.findMeanModel(Clustering<?> c)
Test if the given clustering has a mean model.
|