Uses of Interface
de.lmu.ifi.dbs.elki.data.NumberVector

Packages that use NumberVector

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.benchmark	Benchmarking pseudo algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Biclustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional	Clustering algorithms for one-dimensional data.
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.lof	LOF family of 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.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
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.greedyensemble	Greedy ensembles for outlier detection.
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.projection	Data projections.
de.lmu.ifi.dbs.elki.data.type	Data type information, also used for type restrictions.
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views).
de.lmu.ifi.dbs.elki.datasource.filter	Data filtering, in particular for normalization and projection.
de.lmu.ifi.dbs.elki.datasource.filter.normalization	Data normalization.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations.
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.histogram	Distance functions for one-dimensional histograms.
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.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence.
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	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.roc	Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH.
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions	Hash functions for LSH
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.projected	Projected indexes for data.
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing.
de.lmu.ifi.dbs.elki.index.tree.spatial.kd	K-d-tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	R*-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework.
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).
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.linearalgebra.randomprojections	Random projection families.
de.lmu.ifi.dbs.elki.math.scales	Scales handling for plotting.
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.utilities	Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike	Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
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.svg	Base SVG functionality (generation, markers, thumbnails, export, ...).
de.lmu.ifi.dbs.elki.visualization.visualizers.histogram	Visualizers based on 1D projected histograms.
de.lmu.ifi.dbs.elki.visualization.visualizers.parallel	Visualizers based on parallel coordinates.
de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster	Visualizers for clustering results based on parallel coordinates.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot	Visualizers based on scatterplots.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster	Visualizers for clustering results based on 2D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection	Visualizers for object selection based on 2D projections.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.distancefunction	Classes from the tutorial on implementing distance functions.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm

Classes in de.lmu.ifi.dbs.elki.algorithm with type parameters of type NumberVector

Modifier and Type	Class and Description
class	DependencyDerivator<V extends NumberVector<?>,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<?>,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<?>,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<?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.benchmark

Classes in de.lmu.ifi.dbs.elki.algorithm.benchmark with type parameters of type NumberVector

Modifier and Type	Class and Description
class	RangeQueryBenchmarkAlgorithm<O extends NumberVector<?>,D extends NumberDistance<D,?>> Benchmarking algorithm that computes a range query for each point.
static class	RangeQueryBenchmarkAlgorithm.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class

Method parameters in de.lmu.ifi.dbs.elki.algorithm.benchmark with type arguments of type NumberVector

Modifier and Type	Method and Description
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation, Relation<NumberVector<?>> radrel) Run the algorithm, with separate radius relation

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector<?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
static class	AbstractProjectedDBSCAN.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	DeLiClu<NV extends NumberVector<?>,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<?>,D extends Distance<D>> Parameterization class.
class	EM<V extends NumberVector<?>> Provides the EM algorithm (clustering by expectation maximization).
static class	EM.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	NaiveMeanShiftClustering<V extends NumberVector<?>,D extends NumberDistance<D,?>> Mean-shift based clustering algorithm.
static class	NaiveMeanShiftClustering.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterizer.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering with type arguments of type NumberVector

Modifier and Type	Method and Description
static double	EM.assignProbabilitiesToInstances(Relation<? extends NumberVector<?>> relation, double[] normDistrFactor, Vector[] means, Matrix[] invCovMatr, double[] clusterWeights, WritableDataStore<double[]> probClusterIGivenX) Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
static void	EM.recomputeCovarianceMatrices(Relation<? extends NumberVector<?>> relation, WritableDataStore<double[]> probClusterIGivenX, Vector[] means, Matrix[] covarianceMatrices, int dimensionality) Recompute the covariance matrixes.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractBiclustering<V extends NumberVector<?>,M extends BiclusterModel> Abstract class as a convenience for different biclustering approaches.
class	ChengAndChurch<V extends NumberVector<?>> Perform Cheng and Church biclustering.
static class	ChengAndChurch.Parameterizer<V extends NumberVector<?>> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type NumberVector

Modifier and Type	Class and Description
class	CASH<V extends NumberVector<?>> Provides the CASH algorithm, an subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector<?>,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<?>,D extends Distance<D>> Parameterization class.
class	ERiC<V extends NumberVector<?>> 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<?>> Parameterization class.
class	FourC<V extends NumberVector<?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
static class	FourC.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	HiCO<V extends NumberVector<?>> Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
static class	HiCO.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	ORCLUS<V extends NumberVector<?>> ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces.
static class	ORCLUS.Parameterizer<V extends NumberVector<?>> Parameterization class.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as NumberVector

Modifier and Type	Field and Description
(package private) V	ORCLUS.ORCLUSCluster.centroid The centroid of this cluster.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return types with arguments of type NumberVector

Modifier and Type	Method and Description
protected CASH<NumberVector<?>>	CASH.Parameterizer.makeInstance()

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type arguments of type NumberVector

Modifier and Type	Method and Description
private LMCLUS.Separation	LMCLUS.findSeparation(Relation<NumberVector<?>> relation, DBIDs currentids, int dimension, Random r) This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.
Clustering<Model>	LMCLUS.run(Database database, Relation<NumberVector<?>> relation) The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash declared as NumberVector

Modifier and Type	Field and Description
private NumberVector<?>	ParameterizationFunction.vec The actual vector.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash with parameters of type NumberVector

Constructor and Description
ParameterizationFunction(NumberVector<?> vec) Provides a new parameterization function describing all lines in a d-dimensional feature space intersecting in one point p.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractKMeans<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>> Abstract base class for k-means implementations.
static class	AbstractKMeans.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	BestOfMultipleKMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Run K-Means multiple times, and keep the best run.
static class	BestOfMultipleKMeans.Parameterizer<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>> Parameterization class.
static class	FirstKInitialMeans.Parameterizer<V extends NumberVector<?>> Parameterization class.
interface	KMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Some constants and options shared among kmeans family algorithms.
class	KMeansBatchedLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
static class	KMeansBatchedLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	KMeansBisecting<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
static class	KMeansBisecting.Parameterizer<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> Parameterization class.
class	KMeansHybridLloydMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.
static class	KMeansHybridLloydMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
static class	KMeansLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using MacQueen style incremental updates.
static class	KMeansMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations.
static class	KMediansLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	RandomlyGeneratedInitialMeans<V extends NumberVector<?>> Initialize k-means by generating random vectors (within the data sets value range).
static class	RandomlyGeneratedInitialMeans.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	SampleKMeansInitialization<V extends NumberVector<?>,D extends Distance<?>> Initialize k-means by running k-means on a sample of the data set only.
static class	SampleKMeansInitialization.Parameterizer<V extends NumberVector<?>,D extends Distance<?>> Parameterization class.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return types with arguments of type NumberVector

Modifier and Type	Method and Description
protected List<NumberVector<?>>	AbstractKMeans.medians(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector<?>> medians, Relation<V> database) Returns the median vectors of the given clusters in the given database.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with type arguments of type NumberVector

Modifier and Type	Method and Description
protected boolean	KMeansBatchedLloyd.assignToNearestCluster(Relation<V> relation, DBIDs ids, List<? extends NumberVector<?>> oldmeans, double[][] meanshift, int[] changesize, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment) Returns a list of clusters.
protected boolean	AbstractKMeans.assignToNearestCluster(Relation<V> relation, List<? extends NumberVector<?>> means, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment) Returns a list of clusters.
List<V>	KMeansInitialization.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction) Choose initial means
List<V>	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	RandomlyGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	PAMInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
protected List<Vector>	AbstractKMeans.means(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector<?>> means, Relation<V> database) Returns the mean vectors of the given clusters in the given database.
protected List<NumberVector<?>>	AbstractKMeans.medians(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector<?>> medians, Relation<V> database) Returns the median vectors of the given clusters in the given database.
void	KMeansBisecting.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector<?>,D> distanceFunction)
void	KMeans.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector<?>,D> distanceFunction) Set the distance function to use.
void	BestOfMultipleKMeans.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector<?>,D> distanceFunction)
void	AbstractKMeans.setDistanceFunction(PrimitiveDistanceFunction<? super NumberVector<?>,D> distanceFunction)

Constructor parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with type arguments of type NumberVector

Constructor and Description
AbstractKMeans(PrimitiveDistanceFunction<? super NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMeansBatchedLloyd(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer, int blocks, RandomFactory random) Constructor.
KMeansHybridLloydMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMeansLloyd(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMeansMacQueen(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.
KMediansLloyd(PrimitiveDistanceFunction<NumberVector<?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with type parameters of type NumberVector

Modifier and Type	Interface and Description
interface	KMeansQualityMeasure<O extends NumberVector<?>,D extends Distance<?>> Interface for computing the quality of a K-Means clustering.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with type parameters of type NumberVector

Modifier and Type	Method and Description
<V extends NumberVector<?>> double	WithinClusterVarianceQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction, Relation<V> relation)
<V extends NumberVector<?>> double	WithinClusterMeanDistanceQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction, Relation<V> relation)

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional with type parameters of type NumberVector

Modifier and Type	Class and Description
class	KNNKernelDensityMinimaClustering<V extends NumberVector<?>> Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.
static class	KNNKernelDensityMinimaClustering.Parameterizer<V extends NumberVector<?>> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type parameters of type NumberVector

Modifier and Type	Class and Description
class	CLIQUE<V extends NumberVector<?>> 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<?>> Parameterization class.
class	DiSH<V extends NumberVector<?>> Algorithm for detecting subspace hierarchies.
static class	DiSH.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	DOC<V extends NumberVector<?>> Provides the DOC algorithm, and it's heuristic variant, FastDOC.
static class	DOC.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	HiSC<V extends NumberVector<?>> Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
static class	HiSC.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	P3C<V extends NumberVector<?>> P3C: A Robust Projected Clustering Algorithm.
static class	P3C.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	PreDeCon<V extends NumberVector<?>> PreDeCon computes clusters of subspace preference weighted connected points.
static class	PreDeCon.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	PROCLUS<V extends NumberVector<?>> Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
static class	PROCLUS.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	SUBCLU<V extends NumberVector<?>> Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.
static class	SUBCLU.Parameterizer<V extends NumberVector<?>> Parameterization class.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as NumberVector

Modifier and Type	Field and Description
(package private) V	PROCLUS.PROCLUSCluster.centroid The centroids of this cluster along each dimension.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique with type parameters of type NumberVector

Modifier and Type	Class and Description
class	CLIQUESubspace<V extends NumberVector<?>> Represents a subspace of the original data space in the CLIQUE algorithm.
class	CLIQUEUnit<V extends NumberVector<?>> Represents a unit in the CLIQUE algorithm.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.outlier

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with type parameters of type NumberVector

Modifier and Type	Class and Description
class	ABOD<V extends NumberVector<?>> Angle-Based Outlier Detection / Angle-Based Outlier Factor.
static class	ABOD.Parameterizer<V extends NumberVector<?>> 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	COP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces in: Proc.
static class	COP.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	EMOutlier<V extends NumberVector<?>> outlier detection algorithm using EM Clustering.
static class	EMOutlier.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	FastABOD<V extends NumberVector<?>> Angle-Based Outlier Detection / Angle-Based Outlier Factor.
static class	FastABOD.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	GaussianModel<V extends NumberVector<?>> Outlier have smallest GMOD_PROB: the outlier scores is the probability density of the assumed distribution.
static class	GaussianModel.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	GaussianUniformMixture<V extends NumberVector<?>> Outlier detection algorithm using a mixture model approach.
static class	GaussianUniformMixture.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	HilOut<O extends NumberVector<?>> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
static class	HilOut.Parameterizer<O extends NumberVector<?>> Parameterization class
class	LBABOD<V extends NumberVector<?>> Angle-Based Outlier Detection / Angle-Based Outlier Factor.
static class	LBABOD.Parameterizer<V extends NumberVector<?>> 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	SimpleCOP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Algorithm to compute local correlation outlier probability.
static class	SimpleCOP.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type NumberVector

Modifier and Type	Method and Description
private double	HilOut.HilbertFeatures.getDimForObject(NumberVector<?> obj, int dim) Get the (projected) position of the object in dimension dim.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with type parameters of type NumberVector

Modifier and Type	Class and Description
class	ALOCI<O extends NumberVector<?>,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "approximate Local Correlation Integral".
static class	ALOCI.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	LDF<O extends NumberVector<?>,D extends NumberDistance<D,?>> Outlier Detection with Kernel Density Functions.
static class	LDF.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	SimpleKernelDensityLOF<O extends NumberVector<?>,D extends NumberDistance<D,?>> A simple variant of the LOF algorithm, which uses a simple kernel density estimation instead of the local reachability density.
static class	SimpleKernelDensityLOF.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with type parameters of type NumberVector

Modifier and Type	Field and Description
private Relation<? extends NumberVector<?>>	ALOCI.ALOCIQuadTree.relation Relation indexed.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type NumberVector

Modifier and Type	Method and Description
ALOCI.Node	ALOCI.ALOCIQuadTree.findClosestNode(NumberVector<?> vec, int tlevel) Find the closest node (of depth tlevel or above, if there is no node at this depth) for the given vector.
private double	ALOCI.ALOCIQuadTree.getShiftedDim(NumberVector<?> obj, int dim, int level) Shift and wrap a single dimension.

Constructor parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with type arguments of type NumberVector

Constructor and Description
ALOCI.ALOCIQuadTree(double[] min, double[] max, double[] shift, int nmin, Relation<? extends NumberVector<?>> relation) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.outlier.meta

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with type parameters of type NumberVector

Modifier and Type	Class and Description
class	HiCS<V extends NumberVector<?>> Algorithm to compute High Contrast Subspaces for Density-Based Outlier Ranking.
static class	HiCS.Parameterizer<V extends NumberVector<?>> Parameterization class.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with type arguments of type NumberVector

Modifier and Type	Method and Description
private ArrayList<ArrayDBIDs>	HiCS.buildOneDimIndexes(Relation<? extends NumberVector<?>> relation) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private void	HiCS.calculateContrast(Relation<? extends NumberVector<?>> relation, HiCS.HiCSSubspace subspace, ArrayList<ArrayDBIDs> subspaceIndex, Random random) Calculates the actual contrast of a given subspace.
private Set<HiCS.HiCSSubspace>	HiCS.calculateSubspaces(Relation<? extends NumberVector<?>> relation, ArrayList<ArrayDBIDs> subspaceIndex, Random random) Identifies high contrast subspaces in a given full-dimensional database.
OutlierResult	FeatureBagging.run(Database database, Relation<NumberVector<?>> relation) Run the algorithm on a data set.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with type parameters of type NumberVector

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.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with type arguments of type NumberVector

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	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run the algorithm
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	CTLuRandomWalkEC.run(Relation<N> spatial, Relation<? extends NumberVector<?>> relation) Run the algorithm.
OutlierResult	CTLuScatterplotOutlier.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
private Pair<DBIDVar,Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run a single iteration of the GLS-SOD modeling step

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with type parameters of type NumberVector

Modifier and Type	Class and Description
class	OUTRES<V extends NumberVector<?>> Adaptive outlierness for subspace outlier ranking (OUTRES).
static class	OUTRES.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	SOD<V extends NumberVector<?>,D extends NumberDistance<D,?>> Subspace Outlier Degree.
static class	SOD.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with type arguments of type NumberVector

Modifier and Type	Method and Description
private static double[]	SOD.computePerDimensionVariances(Relation<? extends NumberVector<?>> relation, Vector center, DBIDs neighborhood) Compute the per-dimension variances for the given neighborhood and center.

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial with type arguments of type NumberVector

Modifier and Type	Method and Description
OutlierResult	TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector<?>> relation) Run the actual algorithm.
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector<?>> vecs, Relation<?> labels) Run the algorithm

Uses of NumberVector in de.lmu.ifi.dbs.elki.algorithm.statistics

Classes in de.lmu.ifi.dbs.elki.algorithm.statistics with type parameters of type NumberVector

Modifier and Type	Class and Description
static class	AveragePrecisionAtK.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	EvaluateRankingQuality<V extends NumberVector<?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.
static class	EvaluateRankingQuality.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.statistics with type arguments of type NumberVector

Modifier and Type	Method and Description
private ScalesResult	AddSingleScale.run(Relation<? extends NumberVector<?>> rel) Add scales to a single vector relation.

Uses of NumberVector in de.lmu.ifi.dbs.elki.application.greedyensemble

Classes in de.lmu.ifi.dbs.elki.application.greedyensemble with type parameters of type NumberVector

Modifier and Type	Class and Description
class	ComputeKNNOutlierScores<O extends NumberVector<?>,D extends NumberDistance<D,?>> Application that runs a series of kNN-based algorithms on a data set, for building an ensemble in a second step.
static class	ComputeKNNOutlierScores.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble that return types with arguments of type NumberVector

Modifier and Type	Method and Description
static Relation<NumberVector<?>>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector<?>> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.
private PrimitiveDoubleDistanceFunction<NumberVector<?>>	GreedyEnsembleExperiment.getDistanceFunction(double[] estimated_weights)

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble with parameters of type NumberVector

Modifier and Type	Method and Description
protected void	GreedyEnsembleExperiment.singleEnsemble(double[] ensemble, NumberVector<?> vec) Build a single-element "ensemble".

Method parameters in de.lmu.ifi.dbs.elki.application.greedyensemble with type arguments of type NumberVector

Modifier and Type	Method and Description
static Relation<NumberVector<?>>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector<?>> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.

Uses of NumberVector in de.lmu.ifi.dbs.elki.data

Classes in de.lmu.ifi.dbs.elki.data with type parameters of type NumberVector

Modifier and Type	Interface and Description
static interface	NumberVector.Factory<V extends NumberVector<? extends N>,N extends Number> Factory API for this feature vector.

Subinterfaces of NumberVector in de.lmu.ifi.dbs.elki.data

Modifier and Type	Interface and Description
interface	SparseNumberVector<N extends Number> Combines the SparseFeatureVector and NumberVector.

Classes in de.lmu.ifi.dbs.elki.data that implement NumberVector

Modifier and Type	Class and Description
class	AbstractNumberVector<N extends Number> AbstractNumberVector is an abstract implementation of FeatureVector.
class	BitVector Provides a BitVector wrapping a BitSet.
class	ByteVector A ByteVector stores the data using bytes.
class	DoubleVector A DoubleVector is to store real values approximately as double values.
class	FloatVector A FloatVector is to store real values with lower memory requirements by using 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	ShortVector An ShortVector is to store Short values.
class	SparseByteVector A SparseByteVector is to store real values as double values.
class	SparseDoubleVector A SparseDoubleVector is to store real values as double values.
class	SparseFloatVector A SparseFloatVector is to store real values approximately as float values.
class	SparseIntegerVector A SparseIntegerVector is to store real values as double values.
class	SparseShortVector A SparseShortVector is to store real values as double values.

Fields in de.lmu.ifi.dbs.elki.data with type parameters of type NumberVector

Modifier and Type	Field and Description
private Relation<? extends NumberVector<?>>	VectorUtil.SortDBIDsBySingleDimension.data The relation to sort.

Methods in de.lmu.ifi.dbs.elki.data with type parameters of type NumberVector

Modifier and Type	Method and Description
static <V extends NumberVector<?>> V	VectorUtil.project(V v, BitSet selectedAttributes, NumberVector.Factory<V,?> factory) Provides a new NumberVector as a projection on the specified attributes.
static <V extends NumberVector<?>> V	VectorUtil.randomVector(NumberVector.Factory<V,?> factory, int dim) Produce a new vector based on random numbers in [0:1].
static <V extends NumberVector<?>> V	VectorUtil.randomVector(NumberVector.Factory<V,?> factory, int dim, Random r) Produce a new vector based on random numbers in [0:1].

Methods in de.lmu.ifi.dbs.elki.data with parameters of type NumberVector

Modifier and Type	Method and Description
static double	VectorUtil.angle(NumberVector<?> v1, NumberVector<?> v2, NumberVector<?> o) Compute the angle between two vectors.
static double	VectorUtil.angle(NumberVector<?> v1, NumberVector<?> v2, NumberVector<?> o) Compute the angle between two vectors.
static double	VectorUtil.angle(NumberVector<?> v1, NumberVector<?> v2, NumberVector<?> o) Compute the angle between two vectors.
static double	VectorUtil.angle(NumberVector<?> v1, NumberVector<?> v2, Vector o) Compute the angle between two vectors.
static double	VectorUtil.angle(NumberVector<?> v1, NumberVector<?> v2, Vector o) Compute the angle between two vectors.
int	VectorUtil.SortVectorsBySingleDimension.compare(NumberVector<?> o1, NumberVector<?> o2)
int	VectorUtil.SortVectorsBySingleDimension.compare(NumberVector<?> o1, NumberVector<?> o2)
static double	VectorUtil.cosAngle(NumberVector<?> v1, NumberVector<?> v2) Compute the absolute cosine of the angle between two vectors.
static double	VectorUtil.cosAngle(NumberVector<?> v1, NumberVector<?> v2) Compute the absolute cosine of the angle between two vectors.
static double[]	VectorUtil.fastTimes(Matrix mat, NumberVector<?> v) This is an ugly hack, but we don't want to have the Matrix class depend on NumberVector.
static DoubleMinMax	VectorUtil.getRangeDouble(NumberVector<?> vec) Return the range across all dimensions.
static double	VectorUtil.scalarProduct(NumberVector<?> d1, NumberVector<?> d2) Provides the scalar product (inner product) of this and the given DoubleVector.
static double	VectorUtil.scalarProduct(NumberVector<?> d1, NumberVector<?> d2) Provides the scalar product (inner product) of this and the given DoubleVector.

Method parameters in de.lmu.ifi.dbs.elki.data with type arguments of type NumberVector

Modifier and Type	Method and Description
static Vector	VectorUtil.computeMedoid(Relation<? extends NumberVector<?>> relation, DBIDs sample) Compute medoid for a given subset.

Constructor parameters in de.lmu.ifi.dbs.elki.data with type arguments of type NumberVector

Constructor and Description
VectorUtil.SortDBIDsBySingleDimension(Relation<? extends NumberVector<?>> data) Constructor.
VectorUtil.SortDBIDsBySingleDimension(Relation<? extends NumberVector<?>> data, int dim) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.data.model

Classes in de.lmu.ifi.dbs.elki.data.model with type parameters of type NumberVector

Modifier and Type	Class and Description
class	CorrelationAnalysisSolution<V extends NumberVector<?>> A solution of correlation analysis is a matrix of equations describing the dependencies.
class	KMeansModel<V extends NumberVector<?>> Trivial subclass of the MeanModel that indicates the clustering to be produced by k-means (so the Voronoi cell visualization is sensible).

Uses of NumberVector in de.lmu.ifi.dbs.elki.data.projection

Classes in de.lmu.ifi.dbs.elki.data.projection with type parameters of type NumberVector

Modifier and Type	Class and Description
class	LatLngToECEFProjection<V extends NumberVector<?>> Project (Latitude, Longitude) vectors to (X, Y, Z), from spherical coordinates to ECEF (earth-centered earth-fixed).
class	LngLatToECEFProjection<V extends NumberVector<?>> Project (Longitude, Latitude) vectors to (X, Y, Z), from spherical coordinates to ECEF (earth-centered earth-fixed).
class	NumericalFeatureSelection<V extends NumberVector<?>> Projection class for number vectors.
static class	NumericalFeatureSelection.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	RandomProjection<V extends NumberVector<?>> Randomized projections of the data.

Methods in de.lmu.ifi.dbs.elki.data.projection that return types with arguments of type NumberVector

Modifier and Type	Method and Description
protected LatLngToECEFProjection<NumberVector<?>>	LatLngToECEFProjection.Parameterizer.makeInstance()
protected RandomProjection<NumberVector<?>>	RandomProjection.Parameterizer.makeInstance()
protected LngLatToECEFProjection<NumberVector<?>>	LngLatToECEFProjection.Parameterizer.makeInstance()

Uses of NumberVector in de.lmu.ifi.dbs.elki.data.type

Fields in de.lmu.ifi.dbs.elki.data.type with type parameters of type NumberVector

Modifier and Type	Field and Description
static VectorFieldTypeInformation<NumberVector<?>>	TypeUtil.NUMBER_VECTOR_FIELD Input type for algorithms that require number vector fields.
static SimpleTypeInformation<? super NumberVector<?>>	TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH Number vectors of variable length.

Uses of NumberVector in de.lmu.ifi.dbs.elki.database.relation

Methods in de.lmu.ifi.dbs.elki.database.relation with type parameters of type NumberVector

Modifier and Type	Method and Description
static <V extends NumberVector<? extends N>,N extends Number> NumberVector.Factory<V,N>	RelationUtil.getNumberVectorFactory(Relation<V> relation) Get the number vector factory of a database relation.

Method parameters in de.lmu.ifi.dbs.elki.database.relation with type arguments of type NumberVector

Modifier and Type	Method and Description
static double[][]	RelationUtil.relationAsMatrix(Relation<? extends NumberVector<?>> relation, ArrayDBIDs ids) Copy a relation into a double matrix.

Uses of NumberVector in de.lmu.ifi.dbs.elki.datasource.filter

Classes in de.lmu.ifi.dbs.elki.datasource.filter with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractVectorConversionFilter<I,O extends NumberVector<?>> Abstract class for filters that produce number vectors.
class	AbstractVectorStreamConversionFilter<I,O extends NumberVector<?>> Abstract base class for streaming filters that produce vectors.
class	HistogramJitterFilter<V extends NumberVector<?>> Add Jitter, preserving the histogram properties (same sum, nonnegative).
class	SplitNumberVectorFilter<V extends NumberVector<?>> Split an existing column into two types.
static class	SplitNumberVectorFilter.Parameterizer<V extends NumberVector<?>> Parameterization class.

Methods in de.lmu.ifi.dbs.elki.datasource.filter with type parameters of type NumberVector

Modifier and Type	Method and Description
static <V extends NumberVector<?>> NumberVector.Factory<V,?>	FilterUtil.guessFactory(SimpleTypeInformation<V> in) Try to guess the appropriate factory.

Uses of NumberVector in de.lmu.ifi.dbs.elki.datasource.filter.normalization

Classes in de.lmu.ifi.dbs.elki.datasource.filter.normalization with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractNormalization<O extends NumberVector<?>> Abstract super class for all normalizations.
class	AbstractStreamNormalization<O extends NumberVector<?>> Abstract super class for all normalizations.
class	AttributeWiseCDFNormalization<V extends NumberVector<?>> Class to perform and undo a normalization on real vectors by estimating the distribution of values along each dimension independently, then rescaling objects to the cumulative density function (CDF) value at the original coordinate.
static class	AttributeWiseCDFNormalization.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	AttributeWiseErfNormalization<O extends NumberVector<?>> Attribute-wise Normalization using the error function.
class	AttributeWiseMADNormalization<V extends NumberVector<?>> Median Absolute Deviation is used for scaling the data set as follows: First, the median, and median absolute deviation are computed in each axis.
class	AttributeWiseMinMaxNormalization<V extends NumberVector<?>> 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<?>> Parameterization class.
class	AttributeWiseVarianceNormalization<V extends NumberVector<?>> 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<?>> Parameterization class.
class	LengthNormalization<V extends NumberVector<?>> Class to perform a normalization on vectors to norm 1.
static class	LengthNormalization.Parameterizer<V extends NumberVector<?>> Parameterization class.

Method parameters in de.lmu.ifi.dbs.elki.datasource.filter.normalization with type arguments of type NumberVector

Modifier and Type	Method and Description
Double	AttributeWiseCDFNormalization.Adapter.get(List<? extends NumberVector<?>> array, int off)
byte	AttributeWiseCDFNormalization.Adapter.getByte(List<? extends NumberVector<?>> array, int off)
double	AttributeWiseCDFNormalization.Adapter.getDouble(List<? extends NumberVector<?>> array, int off)
float	AttributeWiseCDFNormalization.Adapter.getFloat(List<? extends NumberVector<?>> array, int off)
int	AttributeWiseCDFNormalization.Adapter.getInteger(List<? extends NumberVector<?>> array, int off)
long	AttributeWiseCDFNormalization.Adapter.getLong(List<? extends NumberVector<?>> array, int off)
short	AttributeWiseCDFNormalization.Adapter.getShort(List<? extends NumberVector<?>> array, int off)
int	AttributeWiseCDFNormalization.Adapter.size(List<? extends NumberVector<?>> array)

Uses of NumberVector in de.lmu.ifi.dbs.elki.datasource.filter.transform

Classes in de.lmu.ifi.dbs.elki.datasource.filter.transform with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractSupervisedProjectionVectorFilter<V extends NumberVector<?>> Base class for supervised projection methods.
static class	AbstractSupervisedProjectionVectorFilter.Parameterizer<V extends NumberVector<?>> Parameterization class.
static class	ClassicMultidimensionalScalingTransform.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	GlobalPrincipalComponentAnalysisTransform<O extends NumberVector<?>> Apply principal component analysis to the data set.
static class	GlobalPrincipalComponentAnalysisTransform.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	LatLngToECEFFilter<V extends NumberVector<?>> Project a 2D data set (latitude, longitude) to a 3D coordinate system (X, Y, Z), such that Euclidean distance is line-of-sight.
static class	LatLngToECEFFilter.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	LinearDiscriminantAnalysisFilter<V extends NumberVector<?>> Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
static class	LinearDiscriminantAnalysisFilter.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	LngLatToECEFFilter<V extends NumberVector<?>> Project a 2D data set (longitude, latitude) to a 3D coordinate system (X, Y, Z), such that Euclidean distance is line-of-sight.
static class	LngLatToECEFFilter.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	NumberVectorFeatureSelectionFilter<V extends NumberVector<?>> Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes.
class	NumberVectorRandomFeatureSelectionFilter<V extends NumberVector<?>> Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes.

Uses of NumberVector in de.lmu.ifi.dbs.elki.datasource.parser

Classes in de.lmu.ifi.dbs.elki.datasource.parser with type parameters of type NumberVector

Modifier and Type	Class and Description
class	CategorialDataAsNumberVectorParser<V extends NumberVector<?>> A very simple parser for categorial data, which will then be encoded as numbers.
static class	CategorialDataAsNumberVectorParser.Parameterizer<V extends NumberVector<?>> Parameterization class.
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.

Fields in de.lmu.ifi.dbs.elki.datasource.parser declared as NumberVector

Modifier and Type	Field and Description
protected V	NumberVectorLabelParser.curvec Current vector.

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type NumberVector

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.

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type NumberVector

Modifier and Type	Method and Description
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	AbstractSpatialDoubleDistanceFunction.instantiate(Relation<T> relation)
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	AbstractSpatialDoubleDistanceNorm.instantiate(Relation<T> relation)

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction that return types with arguments of type NumberVector

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector<?>>	ArcCosineDistanceFunction.getInputTypeRestriction()
VectorFieldTypeInformation<? super NumberVector<?>>	WeightedDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector<?>>	AbstractVectorDoubleDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector<?>>	CosineDistanceFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type NumberVector

Modifier and Type	Method and Description
static int	AbstractVectorDoubleDistanceFunction.dimensionality(NumberVector<?> o1, NumberVector<?> o2) Get the common dimensionality of the two objects.
static int	AbstractVectorDoubleDistanceFunction.dimensionality(NumberVector<?> o1, NumberVector<?> o2) Get the common dimensionality of the two objects.
static int	AbstractVectorDoubleDistanceFunction.dimensionality(NumberVector<?> o1, NumberVector<?> o2, int expect) Get the common dimensionality of the two objects.
static int	AbstractVectorDoubleDistanceFunction.dimensionality(NumberVector<?> o1, NumberVector<?> o2, int expect) Get the common dimensionality of the two objects.
DoubleDistance	AbstractVectorDoubleDistanceFunction.distance(NumberVector<?> o1, NumberVector<?> o2)
DoubleDistance	AbstractVectorDoubleDistanceFunction.distance(NumberVector<?> o1, NumberVector<?> o2)
double	ClarkDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	ClarkDistanceFunction.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	WeightedDistanceFunction.doubleDistance(NumberVector<?> o1, NumberVector<?> o2)
double	WeightedDistanceFunction.doubleDistance(NumberVector<?> o1, NumberVector<?> o2)
double	WeightedCanberraDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedCanberraDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
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	CanberraDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	CanberraDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	LorentzianDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	LorentzianDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	BrayCurtisDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	BrayCurtisDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	Kulczynski1DistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	Kulczynski1DistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	LorentzianDistanceFunction.doubleNorm(NumberVector<?> v1)
DoubleDistance	AbstractVectorDoubleDistanceNorm.norm(NumberVector<?> obj)

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram with parameters of type NumberVector

Modifier and Type	Method and Description
double	HistogramIntersectionDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	HistogramIntersectionDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with type parameters of type NumberVector

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.

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with type parameters of type NumberVector

Modifier and Type	Method and Description
<T extends NumberVector<?>> ERiCDistanceFunction.Instance<T>	ERiCDistanceFunction.instantiate(Relation<T> database)
<T extends NumberVector<?>> PCABasedCorrelationDistanceFunction.Instance<T>	PCABasedCorrelationDistanceFunction.instantiate(Relation<T> database)

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with parameters of type NumberVector

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	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.
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	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.

Constructor parameters in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with type arguments of type NumberVector

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.geo

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.geo that return types with arguments of type NumberVector

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector<?>>	LngLatDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector<?>>	LatLngDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector<?>>	DimensionSelectingLatLngDistanceFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.geo with parameters of type NumberVector

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)

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram with parameters of type NumberVector

Modifier and Type	Method and Description
double	KolmogorovSmirnovDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	KolmogorovSmirnovDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	HistogramMatchDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	HistogramMatchDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski that return types with arguments of type NumberVector

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector<?>>	WeightedLPNormDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector<?>>	LPNormDistanceFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski with parameters of type NumberVector

Modifier and Type	Method and Description
double	MinimumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	MinimumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	SquaredEuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	SquaredEuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedManhattanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedManhattanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	MaximumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	MaximumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedMaximumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedMaximumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedLPNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedLPNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedEuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	WeightedEuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	EuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	EuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
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	LPIntegerNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	LPIntegerNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	ManhattanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	ManhattanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	LPNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	LPNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
protected double	SquaredEuclideanDistanceFunction.doubleMinDistObject(NumberVector<?> v, SpatialComparable mbr)
double	MinimumDistanceFunction.doubleNorm(NumberVector<?> v)
double	SquaredEuclideanDistanceFunction.doubleNorm(NumberVector<?> v)
double	WeightedManhattanDistanceFunction.doubleNorm(NumberVector<?> v)
double	MaximumDistanceFunction.doubleNorm(NumberVector<?> v)
double	WeightedMaximumDistanceFunction.doubleNorm(NumberVector<?> v)
double	WeightedLPNormDistanceFunction.doubleNorm(NumberVector<?> v)
double	WeightedEuclideanDistanceFunction.doubleNorm(NumberVector<?> v)
double	EuclideanDistanceFunction.doubleNorm(NumberVector<?> v)
double	WeightedSquaredEuclideanDistanceFunction.doubleNorm(NumberVector<?> obj)
double	LPIntegerNormDistanceFunction.doubleNorm(NumberVector<?> v)
double	ManhattanDistanceFunction.doubleNorm(NumberVector<?> v)
double	LPNormDistanceFunction.doubleNorm(NumberVector<?> v)
private double	WeightedManhattanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedManhattanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	MaximumDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	MaximumDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedMaximumDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedMaximumDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedLPNormDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedLPNormDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedEuclideanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	WeightedEuclideanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	EuclideanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	EuclideanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	LPIntegerNormDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	LPIntegerNormDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	ManhattanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	ManhattanDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg)
private double	LPNormDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg) Compute unscaled distance in a range of dimensions.
private double	LPNormDistanceFunction.doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg) Compute unscaled distance in a range of dimensions.
private double	WeightedManhattanDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	MaximumDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	WeightedMaximumDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	WeightedLPNormDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	WeightedEuclideanDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	EuclideanDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	LPIntegerNormDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	ManhattanDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg)
private double	LPNormDistanceFunction.doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg) Compute unscaled distance in a range of dimensions.
private double	WeightedManhattanDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	MaximumDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	WeightedMaximumDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	WeightedLPNormDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	WeightedEuclideanDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	EuclideanDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	LPIntegerNormDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	ManhattanDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg)
private double	LPNormDistanceFunction.doublePreNorm(NumberVector<?> v, int start, int end, double agg) Compute unscaled norm in a range of dimensions.

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic with parameters of type NumberVector

Modifier and Type	Method and Description
double	ChiSquaredDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	ChiSquaredDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	JensenShannonDivergenceDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	JensenShannonDivergenceDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	JeffreyDivergenceDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	JeffreyDivergenceDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	KullbackLeiblerDivergenceAsymmetricDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	KullbackLeiblerDivergenceAsymmetricDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	SqrtJensenShannonDivergenceDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
double	SqrtJensenShannonDivergenceDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2)

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with type parameters of type NumberVector

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	LocalSubspaceDistanceFunction.Instance<V extends NumberVector<?>> The actual instance bound to a particular database.

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with type parameters of type NumberVector

Modifier and Type	Method and Description
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	SubspaceLPNormDistanceFunction.instantiate(Relation<T> database)
<T extends NumberVector<?>> DiSHDistanceFunction.Instance<T>	DiSHDistanceFunction.instantiate(Relation<T> database)
<V extends NumberVector<?>> LocalSubspaceDistanceFunction.Instance<V>	LocalSubspaceDistanceFunction.instantiate(Relation<V> database)

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace that return types with arguments of type NumberVector

Modifier and Type	Method and Description
VectorFieldTypeInformation<? super NumberVector<?>>	SubspaceLPNormDistanceFunction.getInputTypeRestriction()
VectorTypeInformation<? super NumberVector<?>>	DimensionSelectingDistanceFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with parameters of type NumberVector

Modifier and Type	Method and Description
double	SubspaceManhattanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	SubspaceManhattanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	SubspaceLPNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	SubspaceLPNormDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	SubspaceMaximumDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	SubspaceMaximumDistanceFunction.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.
double	SubspaceEuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
double	SubspaceEuclideanDistanceFunction.doubleDistance(NumberVector<?> v1, NumberVector<?> v2) Provides the Euclidean distance between two given feature vectors in the selected dimensions.
protected double	SubspaceManhattanDistanceFunction.doubleMinDistObject(SpatialComparable mbr, NumberVector<?> v)
protected double	SubspaceLPNormDistanceFunction.doubleMinDistObject(SpatialComparable mbr, NumberVector<?> v)
protected double	SubspaceMaximumDistanceFunction.doubleMinDistObject(SpatialComparable mbr, NumberVector<?> v)
protected double	SubspaceEuclideanDistanceFunction.doubleMinDistObject(SpatialComparable mbr, NumberVector<?> v)
double	SubspaceManhattanDistanceFunction.doubleNorm(NumberVector<?> obj)
double	SubspaceLPNormDistanceFunction.doubleNorm(NumberVector<?> obj)
double	SubspaceMaximumDistanceFunction.doubleNorm(NumberVector<?> obj)
double	DimensionSelectingDistanceFunction.doubleNorm(NumberVector<?> obj)
double	SubspaceEuclideanDistanceFunction.doubleNorm(NumberVector<?> obj)
DoubleDistance	SubspaceLPNormDistanceFunction.norm(NumberVector<?> obj)

Constructor parameters in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with type arguments of type NumberVector

Constructor and Description
DiSHDistanceFunction(DiSHPreferenceVectorIndex.Factory<NumberVector<?>> indexFactory, double epsilon) Constructor.
LocalSubspaceDistanceFunction(IndexFactory<NumberVector<?>,FilteredLocalPCAIndex<NumberVector<?>>> indexFactory) Constructor
LocalSubspaceDistanceFunction(IndexFactory<NumberVector<?>,FilteredLocalPCAIndex<NumberVector<?>>> indexFactory) Constructor

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries that return types with arguments of type NumberVector

Modifier and Type	Method and Description
VectorFieldTypeInformation<? super NumberVector<?>>	AbstractEditDistanceFunction.getInputTypeRestriction()
VectorFieldTypeInformation<? super NumberVector<?>>	LCSSDistanceFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries with parameters of type NumberVector

Modifier and Type	Method and Description
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.
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.

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.similarityfunction

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction that return types with arguments of type NumberVector

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector<?>>	AbstractVectorDoubleSimilarityFunction.getInputTypeRestriction()

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction with parameters of type NumberVector

Modifier and Type	Method and Description
double	Kulczynski2SimilarityFunction.doubleSimilarity(NumberVector<?> v1, NumberVector<?> v2)
double	Kulczynski2SimilarityFunction.doubleSimilarity(NumberVector<?> v1, NumberVector<?> v2)
double	Kulczynski1SimilarityFunction.doubleSimilarity(NumberVector<?> v1, NumberVector<?> v2)
double	Kulczynski1SimilarityFunction.doubleSimilarity(NumberVector<?> v1, NumberVector<?> v2)
static double	JaccardPrimitiveSimilarityFunction.doubleSimilarityNumberVector(NumberVector<?> o1, NumberVector<?> o2) Compute Jaccard similarity for two number vectors.
static double	JaccardPrimitiveSimilarityFunction.doubleSimilarityNumberVector(NumberVector<?> o1, NumberVector<?> o2) Compute Jaccard similarity for two number vectors.
DoubleDistance	AbstractVectorDoubleSimilarityFunction.similarity(NumberVector<?> o1, NumberVector<?> o2)
DoubleDistance	AbstractVectorDoubleSimilarityFunction.similarity(NumberVector<?> o1, NumberVector<?> o2)

Uses of NumberVector in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with type parameters of type NumberVector

Modifier and Type	Method and Description
<T extends NumberVector<?>> DistanceSimilarityQuery<T,DoubleDistance>	PolynomialKernelFunction.instantiate(Relation<T> database)

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type NumberVector

Modifier and Type	Method and Description
DoubleDistance	PolynomialKernelFunction.distance(NumberVector<?> fv1, NumberVector<?> fv2)
DoubleDistance	PolynomialKernelFunction.distance(NumberVector<?> fv1, NumberVector<?> fv2)
double	LinearKernelFunction.doubleDistance(NumberVector<?> fv1, NumberVector<?> fv2)
double	LinearKernelFunction.doubleDistance(NumberVector<?> fv1, NumberVector<?> fv2)
double	PolynomialKernelFunction.doubleDistance(NumberVector<?> fv1, NumberVector<?> fv2)
double	PolynomialKernelFunction.doubleDistance(NumberVector<?> fv1, NumberVector<?> fv2)
double	SigmoidKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	SigmoidKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	LinearKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	LinearKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	RationalQuadraticKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	RationalQuadraticKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	PolynomialKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	PolynomialKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	LaplaceKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	LaplaceKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	RadialBasisFunctionKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)
double	RadialBasisFunctionKernelFunction.doubleSimilarity(NumberVector<?> o1, NumberVector<?> o2)

Uses of NumberVector in de.lmu.ifi.dbs.elki.evaluation.roc

Fields in de.lmu.ifi.dbs.elki.evaluation.roc declared as NumberVector

Modifier and Type	Field and Description
private NumberVector<?>	ROC.DecreasingVectorIter.vec Data vector.
private NumberVector<?>	ROC.IncreasingVectorIter.vec Data vector.
(package private) NumberVector<?>	ROC.VectorOverThreshold.vec Vector to use as reference
(package private) NumberVector<?>	ROC.VectorZero.vec Vector to use as reference

Constructors in de.lmu.ifi.dbs.elki.evaluation.roc with parameters of type NumberVector

Constructor and Description
ROC.DecreasingVectorIter(NumberVector<?> vec) Constructor.
ROC.IncreasingVectorIter(NumberVector<?> vec) Constructor.
ROC.VectorNonZero(NumberVector<?> vec) Constructor.
ROC.VectorOverThreshold(NumberVector<?> vec, double threshold) Constructor.
ROC.VectorZero(NumberVector<?> vec) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies

Methods in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies that return types with arguments of type NumberVector

Modifier and Type	Method and Description
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector<?>>>	AbstractHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector<?>> relation, int l)

Method parameters in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with type arguments of type NumberVector

Modifier and Type	Method and Description
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector<?>>>	AbstractHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector<?>> relation, int l)

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.lsh.hashfunctions

Methods in de.lmu.ifi.dbs.elki.index.lsh.hashfunctions with parameters of type NumberVector

Modifier and Type	Method and Description
int	MultipleProjectionsLocalitySensitiveHashFunction.hashObject(NumberVector<?> vec)

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.preprocessed

Classes in de.lmu.ifi.dbs.elki.index.preprocessed with type parameters of type NumberVector

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.knn with type parameters of type NumberVector

Modifier and Type	Class and Description
class	KNNJoinMaterializeKNNPreprocessor<V extends NumberVector<?>,D extends Distance<D>> Class to materialize the kNN using a spatial join on an R-tree.
static class	KNNJoinMaterializeKNNPreprocessor.Factory<O extends NumberVector<?>,D extends Distance<D>> The parameterizable factory.
static class	KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector<?>,D extends Distance<D>> Parameterization class
class	MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
static class	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry> The parameterizable factory.
static class	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry> 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.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn that return types with arguments of type NumberVector

Modifier and Type	Method and Description
SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>,D,N,E>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector<?>> relation)

Method parameters in de.lmu.ifi.dbs.elki.index.preprocessed.knn with type arguments of type NumberVector

Modifier and Type	Method and Description
SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>,D,N,E>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector<?>> relation)

Constructor parameters in de.lmu.ifi.dbs.elki.index.preprocessed.knn with type arguments of type NumberVector

Constructor and Description
SpatialApproximationMaterializeKNNPreprocessor.Factory(int k, DistanceFunction<? super NumberVector<?>,D> distanceFunction) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractFilteredPCAIndex<NV extends NumberVector<?>> Abstract base class for a local PCA based index.
static class	AbstractFilteredPCAIndex.Factory<NV extends NumberVector<?>,I extends AbstractFilteredPCAIndex<NV>> Factory class.
static class	AbstractFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<?>,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<?>> 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<?>> Factory class.
static class	KNNQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<?>> Parameterization class.
class	RangeQueryFilteredPCAIndex<NV extends NumberVector<?>> 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<?>> Factory class.
static class	RangeQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<?>> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.preprocessed.preference

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.preference with type parameters of type NumberVector

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj with type parameters of type NumberVector

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<?>,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<?>,D extends Distance<D>> Factory class for 4C preprocessors.
static class	FourCSubspaceIndex.Factory.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> Parameterization class.
class	PreDeConSubspaceIndex<V extends NumberVector<?>,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<?>,D extends Distance<D>> Factory.
static class	PreDeConSubspaceIndex.Factory.Parameterizer<V extends NumberVector<?>,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

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.projected

Classes in de.lmu.ifi.dbs.elki.index.projected with type parameters of type NumberVector

Modifier and Type	Class and Description
class	LatLngAsECEFIndex<O extends NumberVector<?>> Index a 2d data set (consisting of Lat/Lng pairs) by using a projection to 3D coordinates (WGS-86 to ECEF).
static class	LatLngAsECEFIndex.Factory<O extends NumberVector<?>> Index factory.
static class	LatLngAsECEFIndex.Factory.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	LngLatAsECEFIndex<O extends NumberVector<?>> Index a 2d data set (consisting of Lng/Lat pairs) by using a projection to 3D coordinates (WGS-86 to ECEF).
static class	LngLatAsECEFIndex.Factory<O extends NumberVector<?>> Index factory.
static class	LngLatAsECEFIndex.Factory.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	PINN<O extends NumberVector<?>> Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the nearest neighbors in high dimensional spaces by using a random projection based index.
static class	PINN.Parameterizer<O extends NumberVector<?>> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.tree.spatial

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial that implement NumberVector

Modifier and Type	Class and Description
class	SpatialPointLeafEntry Represents an entry in a leaf node of a spatial index.

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial with parameters of type NumberVector

Constructor and Description
SpatialPointLeafEntry(DBID id, NumberVector<?> vector) Constructor from number vector.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.tree.spatial.kd

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.kd with type parameters of type NumberVector

Modifier and Type	Class and Description
class	MinimalisticMemoryKDTree<O extends NumberVector<?>> Simple implementation of a static in-memory K-D-tree.
static class	MinimalisticMemoryKDTree.Factory<O extends NumberVector<?>> Factory class

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractRStarTreeFactory<O extends NumberVector<?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,I extends AbstractRStarTree<N,E,S> & Index,S extends AbstractRTreeSettings> Abstract factory for R*-Tree based trees.
static class	AbstractRStarTreeFactory.Parameterizer<O extends NumberVector<?>,S extends AbstractRTreeSettings> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu with type parameters of type NumberVector

Modifier and Type	Class and Description
class	DeLiCluTreeFactory<O extends NumberVector<?>> Factory for DeLiClu R*-Trees.
static class	DeLiCluTreeFactory.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	DeLiCluTreeIndex<O extends NumberVector<?>> The common use of the DeLiClu tree: indexing number vectors.

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu that implement NumberVector

Modifier and Type	Class and Description
class	DeLiCluLeafEntry Defines the requirements for a leaf entry in an DeLiClu-Tree node.

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu with parameters of type NumberVector

Constructor and Description
DeLiCluLeafEntry(DBID id, NumberVector<?> vector) Constructs a new LeafEntry object with the given parameters.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with type parameters of type NumberVector

Modifier and Type	Class and Description
class	RStarTreeFactory<O extends NumberVector<?>> Factory for regular R*-Trees.
static class	RStarTreeFactory.Parameterizer<O extends NumberVector<?>> Parameterization class.
class	RStarTreeIndex<O extends NumberVector<?>> The common use of the rstar tree: indexing number vectors.

Uses of NumberVector in de.lmu.ifi.dbs.elki.index.vafile

Classes in de.lmu.ifi.dbs.elki.index.vafile with type parameters of type NumberVector

Modifier and Type	Class and Description
class	PartialVAFile<V extends NumberVector<?>> PartialVAFile.
static class	PartialVAFile.Factory<V extends NumberVector<?>> Index factory class.
class	VAFile<V extends NumberVector<?>> Vector-approximation file (VAFile) Reference: Weber, R. and Blott, S.
static class	VAFile.Factory<V extends NumberVector<?>> Index factory class.

Methods in de.lmu.ifi.dbs.elki.index.vafile with parameters of type NumberVector

Modifier and Type	Method and Description
protected static VectorApproximation	PartialVAFile.calculatePartialApproximation(DBID id, NumberVector<?> dv, List<DoubleObjPair<DAFile>> daFiles) Calculate partial vector approximation.
protected static void	PartialVAFile.calculateSelectivityCoeffs(List<DoubleObjPair<DAFile>> daFiles, NumberVector<?> query, double epsilon) Calculate selectivity coefficients.
private void	VALPNormDistance.initializeLookupTable(double[][] splitPositions, NumberVector<?> query, double p) Initialize the lookup table.

Method parameters in de.lmu.ifi.dbs.elki.index.vafile with type arguments of type NumberVector

Modifier and Type	Method and Description
protected static BitSet	PartialVAFile.fakeSubspace(Relation<? extends NumberVector<?>> relation) Fake subspace (full-dimensional).

Constructors in de.lmu.ifi.dbs.elki.index.vafile with parameters of type NumberVector

Constructor and Description
VALPNormDistance(double p, double[][] splitPositions, NumberVector<?> query, VectorApproximation queryApprox) Constructor.

Constructor parameters in de.lmu.ifi.dbs.elki.index.vafile with type arguments of type NumberVector

Constructor and Description
DAFile(Relation<? extends NumberVector<?>> relation, int dimension, int partitions) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.math

Methods in de.lmu.ifi.dbs.elki.math with parameters of type NumberVector

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.
static double	MathUtil.weightedPearsonCorrelationCoefficient(NumberVector<?> x, NumberVector<?> y, NumberVector<?> weights) Provides the Pearson product-moment correlation coefficient for two FeatureVectors.
static double	MathUtil.weightedPearsonCorrelationCoefficient(NumberVector<?> x, NumberVector<?> y, NumberVector<?> weights) Provides the Pearson product-moment correlation coefficient for two FeatureVectors.
static double	MathUtil.weightedPearsonCorrelationCoefficient(NumberVector<?> x, NumberVector<?> y, NumberVector<?> weights) Provides the Pearson product-moment correlation coefficient for two FeatureVectors.

Uses of NumberVector in de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Method parameters in de.lmu.ifi.dbs.elki.math.dimensionsimilarity with type arguments of type NumberVector

Modifier and Type	Method and Description
private ArrayList<ArrayDBIDs>	HiCSDimensionSimilarity.buildOneDimIndexes(Relation<? extends NumberVector<?>> relation, DBIDs ids, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private ArrayList<ArrayList<DBIDs>>	MCEDimensionSimilarity.buildPartitions(Relation<? extends NumberVector<?>> relation, DBIDs ids, int depth, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list.
private double	HiCSDimensionSimilarity.calculateContrast(Relation<? extends NumberVector<?>> relation, DBIDs subset, ArrayDBIDs subspaceIndex1, ArrayDBIDs subspaceIndex2, int dim1, int dim2, Random random) Calculates the actual contrast of a given subspace
void	SlopeInversionDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	SlopeDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	CovarianceDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	HiCSDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	HSMDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	SURFINGDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	MCEDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)

Uses of NumberVector in de.lmu.ifi.dbs.elki.math.linearalgebra

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra that implement NumberVector

Modifier and Type	Class and Description
class	Centroid Class to compute the centroid of some data.
class	ProjectedCentroid Centroid only using a subset of dimensions.
class	Vector Provides a vector object that encapsulates an m x 1 - matrix object.

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra with type parameters of type NumberVector

Modifier and Type	Method and Description
<F extends NumberVector<?>> F	CovarianceMatrix.getMeanVector(Relation<? extends F> relation) Get the mean as vector.
<F extends NumberVector<?>> F	Centroid.toVector(Relation<? extends F> relation) Get the data as vector.

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra with parameters of type NumberVector

Modifier and Type	Method and Description
void	Centroid.put(NumberVector<?> val) Add a single value with weight 1.0.
void	ProjectedCentroid.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	Centroid.put(NumberVector<?> val, double weight) Add data with a given weight.
void	ProjectedCentroid.put(NumberVector<?> val, double weight) Add data with a given weight.
void	CovarianceMatrix.put(NumberVector<?> val, double weight) Add data with a given weight.

Method parameters in de.lmu.ifi.dbs.elki.math.linearalgebra with type arguments of type NumberVector

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, DBIDs 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, DBIDs ids) Static constructor from an existing relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector<?>> relation, DBIDs ids) Static Constructor from a full relation.

Uses of NumberVector in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractCovarianceMatrixBuilder<V extends NumberVector<?>> Abstract class with the task of computing a Covariance matrix to be used in PCA.
interface	CovarianceMatrixBuilder<V extends NumberVector<?>> Interface for computing covariance matrixes on a data set.
class	PCAFilteredAutotuningRunner<V extends NumberVector<?>> Performs a self-tuning local PCA based on the covariance matrices of given objects.
static class	PCAFilteredAutotuningRunner.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	PCAFilteredRunner<V extends NumberVector<?>> PCA runner that will do dimensionality reduction.
static class	PCAFilteredRunner.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	PCARunner<V extends NumberVector<?>> Class to run PCA on given data.
static class	PCARunner.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	RANSACCovarianceMatrixBuilder<V extends NumberVector<?>> RANSAC based approach to a more robust covariance matrix computation.
static class	RANSACCovarianceMatrixBuilder.Parameterizer<V extends NumberVector<?>> Parameterization class
class	StandardCovarianceMatrixBuilder<V extends NumberVector<?>> Class for building a "traditional" covariance matrix.
class	WeightedCovarianceMatrixBuilder<V extends NumberVector<?>> CovarianceMatrixBuilder with weights.
static class	WeightedCovarianceMatrixBuilder.Parameterizer<V extends NumberVector<?>> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections with parameters of type NumberVector

Modifier and Type	Method and Description
double[]	AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector<?> in)
double[]	RandomProjectionFamily.Projection.project(NumberVector<?> in) Project a single vector.
double[]	RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector<?> in)

Uses of NumberVector in de.lmu.ifi.dbs.elki.math.scales

Methods in de.lmu.ifi.dbs.elki.math.scales with type parameters of type NumberVector

Modifier and Type	Method and Description
static <O extends NumberVector<? extends Number>> LinearScale[]	Scales.calcScales(Relation<O> db) Compute a linear scale for each dimension.

Uses of NumberVector in de.lmu.ifi.dbs.elki.math.spacefillingcurves

Methods in de.lmu.ifi.dbs.elki.math.spacefillingcurves with parameters of type NumberVector

Modifier and Type	Method and Description
BigInteger	ZCurveTransformer.asBigInteger(NumberVector<?> vector) Deprecated.
byte[]	ZCurveTransformer.asByteArray(NumberVector<?> vector) Transform a single vector.

Constructor parameters in de.lmu.ifi.dbs.elki.math.spacefillingcurves with type arguments of type NumberVector

Constructor and Description
ZCurveTransformer(Relation<? extends NumberVector<?>> relation, DBIDs ids) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.result

Method parameters in de.lmu.ifi.dbs.elki.result with type arguments of type NumberVector

Modifier and Type	Method and Description
static ScalesResult	ResultUtil.getScalesResult(Relation<? extends NumberVector<?>> rel) Get (or create) a scales result for a relation.

Constructor parameters in de.lmu.ifi.dbs.elki.result with type arguments of type NumberVector

Constructor and Description
ScalesResult(Relation<? extends NumberVector<?>> relation) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.utilities

Methods in de.lmu.ifi.dbs.elki.utilities with type parameters of type NumberVector

Modifier and Type	Method and Description
static <NV extends NumberVector<?>> Pair<NV,NV>	DatabaseUtil.computeMinMax(Relation<NV> relation) Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static <V extends NumberVector<?>> double	DatabaseUtil.exactMedian(Relation<V> relation, DBIDs ids, int dimension) Returns the median of a data set in the given dimension.
static <V extends NumberVector<?>> double	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<?>> Relation<V>	DatabaseUtil.relationUglyVectorCast(Relation<T> database) An ugly vector type cast unavoidable in some situations due to Generics.
static <V extends NumberVector<?>,T extends NumberVector<?>> Relation<V>	DatabaseUtil.relationUglyVectorCast(Relation<T> database) An ugly vector type cast unavoidable in some situations due to Generics.

Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type NumberVector

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.

Method parameters in de.lmu.ifi.dbs.elki.utilities with type arguments of type NumberVector

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.

Uses of NumberVector in de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike

Methods in de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike with parameters of type NumberVector

Modifier and Type	Method and Description
N	NumberVectorAdapter.get(NumberVector<N> array, int off) Deprecated.
byte	NumberVectorAdapter.getByte(NumberVector<N> array, int off)
double	NumberVectorAdapter.getDouble(NumberVector<N> array, int off)
float	NumberVectorAdapter.getFloat(NumberVector<N> array, int off)
int	NumberVectorAdapter.getInteger(NumberVector<N> array, int off)
long	NumberVectorAdapter.getLong(NumberVector<N> array, int off)
short	NumberVectorAdapter.getShort(NumberVector<N> array, int off)
static <N extends Number> NumberVectorAdapter<N>	ArrayLikeUtil.numberVectorAdapter(NumberVector<N> prototype) Get the static instance.
int	NumberVectorAdapter.size(NumberVector<N> array)
static <N extends Number> double[]	ArrayLikeUtil.toPrimitiveDoubleArray(NumberVector<N> obj) Convert a number vector to double[].
static <N extends Number> float[]	ArrayLikeUtil.toPrimitiveFloatArray(NumberVector<N> obj) Convert a number vector to float[].
static <N extends Number> int[]	ArrayLikeUtil.toPrimitiveIntegerArray(NumberVector<N> obj) Convert a number vector to int[].

Uses of NumberVector in de.lmu.ifi.dbs.elki.utilities.referencepoints

Classes in de.lmu.ifi.dbs.elki.utilities.referencepoints with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AxisBasedReferencePoints<V extends NumberVector<?>> Strategy to pick reference points by placing them on the axis ends.
static class	AxisBasedReferencePoints.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	FullDatabaseReferencePoints<O extends NumberVector<?>> Strategy to use the complete database as reference points.
class	GridBasedReferencePoints<V extends NumberVector<?>> Grid-based strategy to pick reference points.
static class	GridBasedReferencePoints.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	RandomGeneratedReferencePoints<V extends NumberVector<?>> Reference points generated randomly within the used data space.
static class	RandomGeneratedReferencePoints.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	RandomSampleReferencePoints<V extends NumberVector<?>> Random-Sampling strategy for picking reference points.
static class	RandomSampleReferencePoints.Parameterizer<V extends NumberVector<?>> Parameterization class.
class	StarBasedReferencePoints<V extends NumberVector<?>> Star-based strategy to pick reference points.
static class	StarBasedReferencePoints.Parameterizer<V extends NumberVector<?>> Parameterization class.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.projections

Methods in de.lmu.ifi.dbs.elki.visualization.projections with type parameters of type NumberVector

Modifier and Type	Method and Description
<NV extends NumberVector<?>> NV	FullProjection.projectRelativeRenderToDataSpace(Vector v, NumberVector.Factory<NV,?> prototype) Project a relative vector from rendering space to data space.
<NV extends NumberVector<?>> NV	AbstractFullProjection.projectRelativeRenderToDataSpace(Vector v, NumberVector.Factory<NV,?> prototype) Project a relative vector from rendering space to data space.
<NV extends NumberVector<?>> NV	FullProjection.projectRelativeScaledToDataSpace(Vector v, NumberVector.Factory<NV,?> prototype) Project a relative vector from scaled space to data space.
<NV extends NumberVector<?>> NV	AbstractFullProjection.projectRelativeScaledToDataSpace(Vector v, NumberVector.Factory<NV,?> prototype) Project a relative vector from scaled space to data space.
<NV extends NumberVector<?>> NV	FullProjection.projectRenderToDataSpace(Vector v, NumberVector.Factory<NV,?> prototype) Project a vector from rendering space to data space.
<NV extends NumberVector<?>> NV	AbstractFullProjection.projectRenderToDataSpace(Vector v, NumberVector.Factory<NV,?> prototype) Project a vector from rendering space to data space.
<NV extends NumberVector<?>> NV	FullProjection.projectScaledToDataSpace(Vector v, NumberVector.Factory<NV,?> factory) Project a vector from scaled space to data space.
<NV extends NumberVector<?>> NV	AbstractFullProjection.projectScaledToDataSpace(Vector v, NumberVector.Factory<NV,?> factory) Project a vector from scaled space to data space.

Methods in de.lmu.ifi.dbs.elki.visualization.projections with parameters of type NumberVector

Modifier and Type	Method and Description
double[]	SimpleParallel.fastProjectDataToRenderSpace(NumberVector<?> data)
double[]	Simple2D.fastProjectDataToRenderSpace(NumberVector<?> data)
double[]	Projection2D.fastProjectDataToRenderSpace(NumberVector<?> data) Project a data vector from data space to rendering space.
double[]	AffineProjection.fastProjectDataToRenderSpace(NumberVector<?> data)
double[]	ProjectionParallel.fastProjectDataToRenderSpace(NumberVector<?> v) Fast project a vector from data to render space
double	Simple1D.fastProjectDataToRenderSpace(NumberVector<?> data)
double	Projection1D.fastProjectDataToRenderSpace(NumberVector<?> data) Project a data vector from data space to rendering space.
double[]	Simple2D.fastProjectDataToScaledSpace(NumberVector<?> data)
double[]	Projection2D.fastProjectDataToScaledSpace(NumberVector<?> data) Project a data vector from data space to scaled space.
double[]	AffineProjection.fastProjectDataToScaledSpace(NumberVector<?> data)
double[]	Simple2D.fastProjectRelativeDataToRenderSpace(NumberVector<?> data)
double[]	Projection2D.fastProjectRelativeDataToRenderSpace(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.
Vector	FullProjection.projectDataToRenderSpace(NumberVector<?> data) Project a data vector from data space to rendering space.
Vector	AbstractFullProjection.projectDataToRenderSpace(NumberVector<?> data) Project a data vector from data space to rendering space.
Vector	FullProjection.projectDataToScaledSpace(NumberVector<?> data) Project a data vector from data space to scaled space.
Vector	AbstractFullProjection.projectDataToScaledSpace(NumberVector<?> data) Project a data vector from data space to scaled space.
Vector	FullProjection.projectRelativeDataToRenderSpace(NumberVector<?> data) Project a relative data vector from data space to rendering space.
Vector	AbstractFullProjection.projectRelativeDataToRenderSpace(NumberVector<?> data) Project a relative data vector from data space to rendering space.
Vector	FullProjection.projectRelativeDataToScaledSpace(NumberVector<?> data) Project a relative data vector from data space to scaled space.
Vector	AbstractFullProjection.projectRelativeDataToScaledSpace(NumberVector<?> data) Project a relative data vector from data space to scaled space.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.projector

Classes in de.lmu.ifi.dbs.elki.visualization.projector with type parameters of type NumberVector

Modifier and Type	Class and Description
class	HistogramProjector<V extends NumberVector<?>> ScatterPlotProjector is responsible for producing a set of scatterplot visualizations.
class	ParallelPlotProjector<V extends NumberVector<?>> ParallelPlotProjector is responsible for producing a parallel axes visualization.
class	ScatterPlotProjector<V extends NumberVector<?>> ScatterPlotProjector is responsible for producing a set of scatterplot visualizations.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.svg

Methods in de.lmu.ifi.dbs.elki.visualization.svg with type parameters of type NumberVector

Modifier and Type	Method and Description
static <V extends NumberVector<?>> Element	SVGHyperCube.drawFilled(SVGPlot svgp, String cls, Projection2D proj, V min, V max) Filled hypercube.
static <V extends NumberVector<?>> Element	SVGHyperCube.drawFrame(SVGPlot svgp, Projection2D proj, V min, V max) Wireframe hypercube.

Methods in de.lmu.ifi.dbs.elki.visualization.svg with parameters of type NumberVector

Modifier and Type	Method and Description
static <D extends NumberDistance<?,?>> Element	SVGHyperSphere.drawCross(SVGPlot svgp, Projection2D proj, NumberVector<?> mid, D rad) Wireframe "cross" hypersphere
static Element	SVGHyperSphere.drawEuclidean(SVGPlot svgp, Projection2D proj, NumberVector<?> mid, double radius) Wireframe "euclidean" hypersphere
static Element	SVGHyperSphere.drawLp(SVGPlot svgp, Projection2D proj, NumberVector<?> mid, double radius, double p) Wireframe "Lp" hypersphere
static Element	SVGHyperSphere.drawManhattan(SVGPlot svgp, Projection2D proj, NumberVector<?> mid, double radius) Wireframe "manhattan" hypersphere

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.visualizers.histogram

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.histogram with type parameters of type NumberVector

Modifier and Type	Class and Description
class	ColoredHistogramVisualizer.Instance<NV extends NumberVector<?>> Instance

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel with type parameters of type NumberVector

Modifier and Type	Class and Description
class	AbstractParallelVisualization<NV extends NumberVector<?>> Abstract base class for parallel visualizations.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster with type parameters of type NumberVector

Modifier and Type	Field and Description
private Clustering<MeanModel<? extends NumberVector<?>>>	ClusterParallelMeanVisualization.Instance.clustering The result we visualize.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster that return types with arguments of type NumberVector

Modifier and Type	Method and Description
private static Clustering<MeanModel<? extends NumberVector<?>>>	ClusterParallelMeanVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot with type parameters of type NumberVector

Modifier and Type	Field and Description
protected Relation<? extends NumberVector<?>>	AbstractScatterplotVisualization.rel The representation we visualize
protected ReferencePointsResult<? extends NumberVector<?>>	ReferencePointsVisualization.Instance.result Serves reference points.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster with type parameters of type NumberVector

Modifier and Type	Class and Description
class	EMClusterVisualization.Instance<NV extends NumberVector<?>> Instance.

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster with type parameters of type NumberVector

Modifier and Type	Method and Description
private static <NV extends NumberVector<?>> Clustering<MeanModel<NV>>	EMClusterVisualization.findMeanModel(Clustering<?> c) Test if the given clustering has a mean model.

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection with type parameters of type NumberVector

Modifier and Type	Field and Description
private AbstractMaterializeKNNPreprocessor<? extends NumberVector<?>,D,?>	DistanceFunctionVisualization.Instance.result The selection result we work on

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection with parameters of type NumberVector

Modifier and Type	Method and Description
static Element	DistanceFunctionVisualization.drawCosine(SVGPlot svgp, Projection2D proj, NumberVector<?> mid, double angle) Visualizes Cosine and ArcCosine distance functions

Uses of NumberVector in tutorial.clustering

Classes in tutorial.clustering with type parameters of type NumberVector

Modifier and Type	Class and Description
class	SameSizeKMeansAlgorithm<V extends NumberVector<?>> K-means variation that produces equally sized clusters.
static class	SameSizeKMeansAlgorithm.Parameterizer<V extends NumberVector<?>> Parameterization class.

Fields in tutorial.clustering with type parameters of type NumberVector

Modifier and Type	Field and Description
protected PrimitiveDoubleDistanceFunction<? super NumberVector<?>>	SameSizeKMeansAlgorithm.Parameterizer.distanceFunction Distance function

Methods in tutorial.clustering that return types with arguments of type NumberVector

Modifier and Type	Method and Description
protected List<? extends NumberVector<?>>	SameSizeKMeansAlgorithm.refineResult(Relation<V> relation, List<? extends NumberVector<?>> means, List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, ArrayModifiableDBIDs tids) Perform k-means style iterations to improve the clustering result.

Method parameters in tutorial.clustering with type arguments of type NumberVector

Modifier and Type	Method and Description
protected WritableDataStore<SameSizeKMeansAlgorithm.Meta>	SameSizeKMeansAlgorithm.initializeMeta(Relation<V> relation, List<? extends NumberVector<?>> means) Initialize the metadata storage.
protected List<? extends NumberVector<?>>	SameSizeKMeansAlgorithm.refineResult(Relation<V> relation, List<? extends NumberVector<?>> means, List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, ArrayModifiableDBIDs tids) Perform k-means style iterations to improve the clustering result.
protected void	SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation, List<? extends NumberVector<?>> means, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, PrimitiveDoubleDistanceFunction<NumberVector<?>> df) Compute the distances of each object to all means.
protected void	SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation, List<? extends NumberVector<?>> means, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, PrimitiveDoubleDistanceFunction<NumberVector<?>> df) Compute the distances of each object to all means.

Constructor parameters in tutorial.clustering with type arguments of type NumberVector

Constructor and Description
SameSizeKMeansAlgorithm(PrimitiveDoubleDistanceFunction<? super NumberVector<?>> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.

Uses of NumberVector in tutorial.distancefunction

Methods in tutorial.distancefunction that return types with arguments of type NumberVector

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector<?>>	TutorialDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector<?>>	MultiLPNorm.getInputTypeRestriction()

Methods in tutorial.distancefunction with parameters of type NumberVector

Modifier and Type	Method and Description
double	TutorialDistanceFunction.doubleDistance(NumberVector<?> o1, NumberVector<?> o2)
double	TutorialDistanceFunction.doubleDistance(NumberVector<?> o1, NumberVector<?> o2)
double	MultiLPNorm.doubleDistance(NumberVector<?> o1, NumberVector<?> o2)
double	MultiLPNorm.doubleDistance(NumberVector<?> o1, NumberVector<?> o2)