Uses of Interface
elki.data.NumberVector

Packages that use NumberVector

Package	Description
elki.algorithm	Miscellaneous algorithms.
elki.algorithm.statistics	Statistical analysis algorithms.
elki.application.benchmark	Benchmarking pseudo algorithms.
elki.application.greedyensemble	Greedy ensembles for outlier detection.
elki.application.statistics	Applications to compute some basic data set statistics.
elki.clustering	Clustering algorithms.
elki.clustering.biclustering	Biclustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.correlation.cash	Helper classes for the CASH algorithm.
elki.clustering.dbscan	DBSCAN and its generalizations.
elki.clustering.dbscan.predicates	Neighbor and core predicated for Generalized DBSCAN.
elki.clustering.em	Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.em.models
elki.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
elki.clustering.hierarchical.birch	BIRCH clustering.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.quality	Quality measures for k-Means results.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.
elki.clustering.kmedoids.initialization
elki.clustering.onedimensional	Clustering algorithms for one-dimensional data.
elki.clustering.optics	OPTICS family of clustering algorithms.
elki.clustering.subspace	Axis-parallel subspace clustering algorithms.
elki.clustering.subspace.clique	Helper classes for the CLIQUE algorithm.
elki.clustering.svm
elki.clustering.uncertain	Clustering algorithms for uncertain data.
elki.data	Basic classes for different data types, database object types and label types.
elki.data.model	Cluster models classes for various algorithms.
elki.data.projection	Data projections.
elki.data.projection.random	Random projection families.
elki.data.type	Data type information, also used for type restrictions.
elki.data.uncertain	Uncertain data objects.
elki.database.query.distance	Prepared queries for distances.
elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
elki.database.query.range	Prepared queries for ε-range queries, that return all objects within the radius ε.
elki.database.relation	Relations, materialized and virtual (views).
elki.datasource.filter	Data filtering, in particular for normalization and projection.
elki.datasource.filter.cleaning	Filters for data cleaning.
elki.datasource.filter.normalization.columnwise	Normalizations operating on columns / variates; where each column is treated independently.
elki.datasource.filter.normalization.instancewise	Instancewise normalization, where each instance is normalized independently.
elki.datasource.filter.transform	Data space transformations.
elki.datasource.filter.typeconversions	Filters to perform data type conversions.
elki.datasource.parser	Parsers for different file formats and data types.
elki.distance	Distance functions for use within ELKI.
elki.distance.colorhistogram	Distance functions for color histograms.
elki.distance.correlation	Distance functions using correlations.
elki.distance.geo	Geographic (earth) distance functions.
elki.distance.histogram	Distance functions for one-dimensional histograms.
elki.distance.minkowski	Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.
elki.distance.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
elki.distance.set	Distance functions for binary and set type data.
elki.distance.subspace	Distance functions based on subspaces.
elki.distance.timeseries	Distance functions designed for time series.
elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
elki.evaluation.scores.adapter	Adapter classes for ranking and scoring measures.
elki.index.invertedlist	Indexes using inverted lists.
elki.index.lsh.hashfamilies	Hash function families for LSH.
elki.index.lsh.hashfunctions	Hash functions for LSH.
elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
elki.index.projected	Projected indexes for data.
elki.index.tree.betula	BETULA clustering by aggregating the data into cluster features.
elki.index.tree.betula.distance	Distance functions for BETULA and BIRCH.
elki.index.tree.betula.features	Different variants of Betula and BIRCH cluster features.
elki.index.tree.metrical.vptree
elki.index.tree.spatial	Tree-based index structures for spatial indexing.
elki.index.tree.spatial.kd	K-d-tree and variants.
elki.index.tree.spatial.kd.split
elki.index.tree.spatial.rstarvariants	R*-tree and variants.
elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree.
elki.index.tree.spatial.rstarvariants.flat	FlatRStarTree.
elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree.
elki.index.tree.spatial.rstarvariants.rstar	RStarTree.
elki.index.vafile	Vector Approximation File.
elki.math	Mathematical operations and utilities used throughout the framework.
elki.math.linearalgebra	The linear algebra package provides classes and computational methods for operations on matrices and vectors.
elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and eigenvector processing.
elki.math.spacefillingcurves	Space filling curves.
elki.math.statistics.intrinsicdimensionality	Methods for estimating the intrinsic dimensionality.
elki.outlier	Outlier detection algorithms.
elki.outlier.anglebased	Angle-based outlier detection algorithms.
elki.outlier.clustering	Clustering based outlier detection.
elki.outlier.density	Density-based outlier detection algorithms.
elki.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
elki.outlier.lof	LOF family of outlier detection algorithms.
elki.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
elki.outlier.spatial	Spatial outlier detection algorithms.
elki.outlier.subspace	Subspace outlier detection methods.
elki.outlier.svm	Support-Vector-Machines for outlier detection.
elki.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
elki.result	Result types, representation and handling.
elki.similarity	Similarity functions.
elki.similarity.kernel	Kernel functions.
elki.timeseries	Algorithms for change point detection in time series.
elki.utilities.datastructures.arraylike	Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
elki.utilities.referencepoints	Package containing strategies to obtain reference points.
elki.visualization.parallel3d	3DPC: 3D parallel coordinate plot visualization for ELKI.
elki.visualization.parallel3d.layout	Layouting algorithms for 3D parallel coordinate plots.
elki.visualization.projections	Visualization projections.
elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations.
elki.visualization.svg	Base SVG functionality (generation, markers, thumbnails, export, ...).
elki.visualization.visualizers.histogram	Visualizers based on 1D projected histograms.
elki.visualization.visualizers.scatterplot	Visualizers based on scatterplots.
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 elki.algorithm

Classes in elki.algorithm with type parameters of type NumberVector

Modifier and Type	Class	Description
class	DependencyDerivator<V extends NumberVector>	Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
static class	DependencyDerivator.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.algorithm.statistics

Classes in elki.algorithm.statistics with type parameters of type NumberVector

Modifier and Type	Class	Description
class	EvaluateRankingQuality<V extends NumberVector>	Evaluate a distance function with respect to kNN queries.
static class	EvaluateRankingQuality.Par<V extends NumberVector>	Parameterization class.

Fields in elki.algorithm.statistics with type parameters of type NumberVector

Modifier and Type	Field	Description
protected NumberVectorDistance<? super NumberVector>	HopkinsStatisticClusteringTendency.distance	Distance function used.
protected NumberVectorDistance<? super NumberVector>	HopkinsStatisticClusteringTendency.Par.distance	The distance function to use.

Method parameters in elki.algorithm.statistics with type arguments of type NumberVector

Modifier and Type	Method	Description
protected double	HopkinsStatisticClusteringTendency.computeNNForRealData(KNNSearcher<DBIDRef> knnQuery, Relation<NumberVector> relation, int dim)	Search nearest neighbors for real data members.
protected double	HopkinsStatisticClusteringTendency.computeNNForUniformData(KNNSearcher<NumberVector> knnQuery, double[] min, double[] extend)	Search nearest neighbors for artificial, uniform data.
protected void	HopkinsStatisticClusteringTendency.initializeDataExtends(Relation<NumberVector> relation, int dim, double[] min, double[] extend)	Initialize the uniform sampling area.
private ScalesResult	AddSingleScale.run(Relation<? extends NumberVector> rel)	Add scales to a single vector relation.
private ScalesResult	AddUniformScale.run(Relation<? extends NumberVector> rel)	Add scales to a single vector relation.
java.lang.Double	HopkinsStatisticClusteringTendency.run(Relation<NumberVector> relation)	Compute the Hopkins statistic for a vector relation.

Constructor parameters in elki.algorithm.statistics with type arguments of type NumberVector

Constructor	Description
HopkinsStatisticClusteringTendency(NumberVectorDistance<? super NumberVector> distance, int samplesize, RandomFactory random, int rep, int k, double[] minima, double[] maxima)	Constructor.

Uses of NumberVector in elki.application.benchmark

Classes in elki.application.benchmark with type parameters of type NumberVector

Modifier and Type	Class	Description
class	RangeQueryBenchmark<O extends NumberVector>	Benchmarking algorithm that computes a range query for each point.
static class	RangeQueryBenchmark.Par<O extends NumberVector>	Parameterization class

Uses of NumberVector in elki.application.greedyensemble

Classes in elki.application.greedyensemble with type parameters of type NumberVector

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

Fields in elki.application.greedyensemble declared as NumberVector

Modifier and Type	Field	Description
(package private) NumberVector	EvaluatePrecomputedOutlierScores.positive	Vector of positive values.

Methods in elki.application.greedyensemble that return types with arguments of type NumberVector

Modifier and Type	Method	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 PrimitiveDistance<NumberVector>	GreedyEnsembleExperiment.getDistance(double[] estimated_weights)

Methods in elki.application.greedyensemble with parameters of type NumberVector

Modifier and Type	Method	Description
private boolean	EvaluatePrecomputedOutlierScores.checkForNaNs(NumberVector vec)	Check for NaN values.
private void	EvaluatePrecomputedOutlierScores.processRow(java.io.PrintStream fout, NumberVector vec, java.lang.String label)
protected void	GreedyEnsembleExperiment.singleEnsemble(double[] ensemble, NumberVector vec)	Build a single-element "ensemble".

Method parameters in elki.application.greedyensemble with type arguments of type NumberVector

Modifier and Type	Method	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 elki.application.statistics

Classes in elki.application.statistics with type parameters of type NumberVector

Modifier and Type	Class	Description
class	RangeQuerySelectivity<V extends NumberVector>	Evaluate the range query selectivity.
static class	RangeQuerySelectivity.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.clustering

Classes in elki.clustering with type parameters of type NumberVector

Modifier and Type	Class	Description
class	NaiveMeanShiftClustering<V extends NumberVector>	Mean-shift based clustering algorithm.
static class	NaiveMeanShiftClustering.Par<V extends NumberVector>	Parameterizer.

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

Modifier and Type	Method	Description
Clustering<MeanModel>	BetulaLeafPreClustering.run(Relation<NumberVector> relation)	Run the clustering algorithm.

Uses of NumberVector in elki.clustering.biclustering

Fields in elki.clustering.biclustering with type parameters of type NumberVector

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	AbstractBiclustering.relation	Relation we use.

Method parameters in elki.clustering.biclustering with type arguments of type NumberVector

Modifier and Type	Method	Description
Clustering<M>	AbstractBiclustering.run(Relation<? extends NumberVector> relation)	Prepares the algorithm for running on a specific database.

Uses of NumberVector in elki.clustering.correlation

Fields in elki.clustering.correlation with type parameters of type NumberVector

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	HiCO.Instance.relation	Data relation.

Method parameters in elki.clustering.correlation with type arguments of type NumberVector

Modifier and Type	Method	Description
private void	ORCLUS.assign(Relation<? extends NumberVector> database, java.util.List<ORCLUS.ORCLUSCluster> clusters)	Creates a partitioning of the database by assigning each object to its closest seed.
private java.util.List<java.util.List<Cluster<CorrelationModel>>>	ERiC.extractCorrelationClusters(Clustering<Model> dbscanResult, Relation<? extends NumberVector> relation, int dimensionality, ERiCNeighborPredicate.Instance npred)	Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
private double[][]	ORCLUS.findBasis(Relation<? extends NumberVector> database, ORCLUS.ORCLUSCluster cluster, int dim)	Finds the basis of the subspace of dimensionality dim for the specified cluster.
private LMCLUS.Separation	LMCLUS.findSeparation(Relation<? extends NumberVector> relation, DBIDs currentids, int dimension, java.util.Random r)	This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.
private java.util.List<ORCLUS.ORCLUSCluster>	ORCLUS.initialSeeds(Relation<? extends NumberVector> database, int k)	Initializes the list of seeds wit a random sample of size k.
private void	ORCLUS.merge(Relation<? extends NumberVector> relation, java.util.List<ORCLUS.ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress)	Reduces the number of seeds to k_new
private Relation<ParameterizationFunction>	CASH.preprocess(Relation<? extends NumberVector> vrel)	Preprocess the dataset, precomputing the parameterization functions.
private ORCLUS.ProjectedEnergy	ORCLUS.projectedEnergy(Relation<? extends NumberVector> relation, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster c_j, int i, int j, int dim)	Computes the projected energy of the specified clusters.
Clustering<Model>	CASH.run(Relation<? extends NumberVector> rel)	Run CASH on the relation.
Clustering<DimensionModel>	COPAC.run(Database database, Relation<? extends NumberVector> relation)	Run the COPAC algorithm.
Clustering<CorrelationModel>	ERiC.run(Database database, Relation<? extends NumberVector> relation)	Performs the ERiC algorithm on the given database.
ClusterOrder	HiCO.run(Relation<? extends NumberVector> relation)	Run the HiCO algorithm.
Clustering<Model>	LMCLUS.run(Relation<? extends NumberVector> relation)	The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	ORCLUS.run(Relation<? extends NumberVector> relation)	Performs the ORCLUS algorithm on the given database.
private ORCLUS.ORCLUSCluster	ORCLUS.union(Relation<? extends NumberVector> relation, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster c2, int dim)	Returns the union of the two specified clusters.

Constructor parameters in elki.clustering.correlation with type arguments of type NumberVector

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

Uses of NumberVector in elki.clustering.correlation.cash

Fields in elki.clustering.correlation.cash declared as NumberVector

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

Constructors in elki.clustering.correlation.cash with parameters of type NumberVector

Constructor	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 elki.clustering.dbscan

Classes in elki.clustering.dbscan with type parameters of type NumberVector

Modifier and Type	Class	Description
class	GriDBSCAN<V extends NumberVector>	Using Grid for Accelerating Density-Based Clustering.
protected static class	GriDBSCAN.Instance<V extends NumberVector>	Instance, for a single run.
static class	GriDBSCAN.Par<O extends NumberVector>	Parameterization class.
class	LSDBC<O extends NumberVector>	Locally Scaled Density Based Clustering.
static class	LSDBC.Par<O extends NumberVector>	Parameterization class

Uses of NumberVector in elki.clustering.dbscan.predicates

Fields in elki.clustering.dbscan.predicates with type parameters of type NumberVector

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	ERiCNeighborPredicate.Instance.relation	Vector data relation.

Methods in elki.clustering.dbscan.predicates with parameters of type NumberVector

Modifier and Type	Method	Description
boolean	ERiCNeighborPredicate.Instance.strongNeighbors(NumberVector v1, NumberVector v2, PCAFilteredResult pca1, PCAFilteredResult pca2)	Computes the distance between two given DatabaseObjects according to this distance function.

Method parameters in elki.clustering.dbscan.predicates with type arguments of type NumberVector

Modifier and Type	Method	Description
protected COPACNeighborPredicate.COPACModel	COPACNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList knnneighbors, Relation<? extends NumberVector> relation)	COPAC model computation
protected PreDeConNeighborPredicate.PreDeConModel	FourCNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends NumberVector> relation)
protected PreDeConNeighborPredicate.PreDeConModel	PreDeConNeighborPredicate.computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends NumberVector> relation)
COPACNeighborPredicate.Instance	COPACNeighborPredicate.instantiate(Relation<? extends NumberVector> relation)	Full instantiation method.
ERiCNeighborPredicate.Instance	ERiCNeighborPredicate.instantiate(Relation<? extends NumberVector> relation)	Full instantiation interface.

Constructor parameters in elki.clustering.dbscan.predicates with type arguments of type NumberVector

Constructor	Description
Instance(DBIDs ids, DataStore<PCAFilteredResult> storage, Relation<? extends NumberVector> relation)	Constructor.

Uses of NumberVector in elki.clustering.em

Method parameters in elki.clustering.em with type arguments of type NumberVector

Modifier and Type	Method	Description
private void	KDTreeEM.KDTree.aggregateStats(Relation<? extends NumberVector> relation, DBIDArrayIter iter, int dim)	Aggregate the statistics for a leaf node.
private double[]	KDTreeEM.analyseDimWidth(Relation<? extends NumberVector> relation)	Helper method to retrieve the widths of all data in all dimensions.
double	BetulaGMM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, java.util.List<? extends BetulaClusterModel> models, 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.
private void	KDTreeEM.KDTree.computeBoundingBox(Relation<? extends NumberVector> relation, DBIDArrayIter iter)	Compute the bounding box.
Clustering<EMModel>	BetulaGMM.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<EMModel>	KDTreeEM.run(Relation<? extends NumberVector> relation)	Calculates the EM Clustering with the given values by calling makeStats and calculation the new models from the given results

Constructor parameters in elki.clustering.em with type arguments of type NumberVector

Constructor	Description
KDTree(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs sorted, int left, int right, double[] dimWidth, double mbw)	Constructor for a KDTree with statistics needed for KDTreeEM calculation.

Uses of NumberVector in elki.clustering.em.models

Methods in elki.clustering.em.models with parameters of type NumberVector

Modifier and Type	Method	Description
double	DiagonalGaussianModel.estimateLogDensity(NumberVector vec)
double	MultivariateGaussianModel.estimateLogDensity(NumberVector vec)
double	SphericalGaussianModel.estimateLogDensity(NumberVector vec)
double	TextbookMultivariateGaussianModel.estimateLogDensity(NumberVector vec)
double	TextbookSphericalGaussianModel.estimateLogDensity(NumberVector vec)
double	TwoPassMultivariateGaussianModel.estimateLogDensity(NumberVector vec)
void	TwoPassMultivariateGaussianModel.firstPassE(NumberVector vec, double wei)	First pass: update the mean only.
double	DiagonalGaussianModel.mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
double	MultivariateGaussianModel.mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
double	SphericalGaussianModel.mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
double	TextbookMultivariateGaussianModel.mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
double	TextbookSphericalGaussianModel.mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
double	TwoPassMultivariateGaussianModel.mahalanobisDistance(NumberVector vec)	Compute the Mahalanobis distance from the centroid for a given vector.
void	DiagonalGaussianModel.updateE(NumberVector vec, double wei)
void	MultivariateGaussianModel.updateE(NumberVector vec, double wei)
void	SphericalGaussianModel.updateE(NumberVector vec, double wei)
void	TextbookMultivariateGaussianModel.updateE(NumberVector vec, double wei)
void	TextbookSphericalGaussianModel.updateE(NumberVector vec, double wei)
void	TwoPassMultivariateGaussianModel.updateE(NumberVector vec, double wei)	Second pass: compute the covariance matrix.

Method parameters in elki.clustering.em.models with type arguments of type NumberVector

Modifier and Type	Method	Description
java.util.List<DiagonalGaussianModel>	DiagonalGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<MultivariateGaussianModel>	MultivariateGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<SphericalGaussianModel>	SphericalGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TextbookMultivariateGaussianModel>	TextbookMultivariateGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TextbookSphericalGaussianModel>	TextbookSphericalGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TwoPassMultivariateGaussianModel>	TwoPassMultivariateGaussianModelFactory.buildInitialModels(Relation<? extends NumberVector> relation, int k)

Uses of NumberVector in elki.clustering.hierarchical

Classes in elki.clustering.hierarchical with type parameters of type NumberVector

Modifier and Type	Class	Description
class	LinearMemoryNNChain<O extends NumberVector>	NNchain clustering algorithm with linear memory, for particular linkages (that can be aggregated) and numerical vector data only.
static class	LinearMemoryNNChain.Instance<O extends NumberVector>	Main worker instance of NNChain.
static class	LinearMemoryNNChain.Par<O extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.clustering.hierarchical.birch

Methods in elki.clustering.hierarchical.birch with parameters of type NumberVector

Modifier and Type	Method	Description
protected void	ClusteringFeature.addToStatistics(NumberVector nv)	Add a number vector to the current node.
protected double	BIRCHLloydKMeans.distance(NumberVector x, double[] y)	Compute a distance (and count the distance computations).
private ClusteringFeature	CFTree.findLeaf(CFTree.TreeNode node, NumberVector nv)	Find the leaf of a cluster, to get the final cluster assignment.
ClusteringFeature	CFTree.findLeaf(NumberVector nv)	Find the leaf of a cluster, to get the final cluster assignment.
private CFTree.TreeNode	CFTree.insert(CFTree.TreeNode node, NumberVector nv)	Recursive insertion.
void	CFTree.insert(NumberVector nv)	Insert a data point into the tree.
double	BIRCHAbsorptionCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)	Quality of a CF when adding a data point
double	DiameterCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)
double	EuclideanDistanceCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)
double	RadiusCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)
double	AverageInterclusterDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	AverageIntraclusterDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	BIRCHDistance.squaredDistance(NumberVector v, ClusteringFeature cf)	Distance of a vector to a clustering feature.
double	CentroidEuclideanDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	CentroidManhattanDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	VarianceIncreaseDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
static double	ClusteringFeature.sumOfSquares(NumberVector v)	Compute the sum of squares of a vector.

Method parameters in elki.clustering.hierarchical.birch with type arguments of type NumberVector

Modifier and Type	Method	Description
CFTree	CFTree.Factory.newTree(DBIDs ids, Relation<? extends NumberVector> relation)	Make a new tree.
Clustering<MeanModel>	BIRCHLeafClustering.run(Relation<NumberVector> relation)	Run the clustering algorithm.
Clustering<KMeansModel>	BIRCHLloydKMeans.run(Relation<NumberVector> relation)	Run the clustering algorithm.

Uses of NumberVector in elki.clustering.kmeans

Classes in elki.clustering.kmeans with type parameters of type NumberVector

Modifier and Type	Class	Description
class	AbstractKMeans<V extends NumberVector,M extends Model>	Abstract base class for k-means implementations.
static class	AbstractKMeans.Par<V extends NumberVector>	Parameterization class.
class	AnnulusKMeans<V extends NumberVector>	Annulus k-means algorithm.
static class	AnnulusKMeans.Par<V extends NumberVector>	Parameterization class.
class	BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>	Run K-Means multiple times, and keep the best run.
static class	BestOfMultipleKMeans.Par<V extends NumberVector,M extends MeanModel>	Parameterization class.
class	BisectingKMeans<V extends NumberVector,M extends MeanModel>	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	BisectingKMeans.Par<V extends NumberVector,M extends MeanModel>	Parameterization class.
class	CompareMeans<V extends NumberVector>	Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
static class	CompareMeans.Par<V extends NumberVector>	Parameterization class.
class	ElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
static class	ElkanKMeans.Par<V extends NumberVector>	Parameterization class.
class	ExponionKMeans<V extends NumberVector>	Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
static class	ExponionKMeans.Par<V extends NumberVector>	Parameterization class.
class	FuzzyCMeans<V extends NumberVector>	Fuzzy Clustering developed by Dunn and revisited by Bezdek
class	GMeans<V extends NumberVector,M extends MeanModel>	G-Means extends K-Means and estimates the number of centers with Anderson Darling Test. Implemented as specialization of XMeans.
static class	GMeans.Par<V extends NumberVector,M extends MeanModel>	Parameterization class.
class	HamerlyKMeans<V extends NumberVector>	Hamerly's fast k-means by exploiting the triangle inequality.
static class	HamerlyKMeans.Par<V extends NumberVector>	Parameterization class.
class	HartiganWongKMeans<V extends NumberVector>	Hartigan and Wong k-means clustering.
static class	HartiganWongKMeans.Parameterizer<V extends NumberVector>	Parameterization class.
class	KDTreeFilteringKMeans<V extends NumberVector>	Filtering or "blacklisting" K-means with k-d-tree acceleration.
static class	KDTreeFilteringKMeans.Par<V extends NumberVector>	Parameterization class.
class	KDTreePruningKMeans<V extends NumberVector>	Pruning K-means with k-d-tree acceleration.
static class	KDTreePruningKMeans.Par<V extends NumberVector>	Parameterization class.
interface	KMeans<V extends NumberVector,M extends Model>	Some constants and options shared among kmeans family algorithms.
class	KMeansMinusMinus<V extends NumberVector>	k-means--: A Unified Approach to Clustering and Outlier Detection.
static class	KMeansMinusMinus.Par<V extends NumberVector>	Parameterization class.
class	KMediansLloyd<V extends NumberVector>	k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see PAM instead).
static class	KMediansLloyd.Par<V extends NumberVector>	Parameterization class.
class	LloydKMeans<V extends NumberVector>	The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
static class	LloydKMeans.Par<V extends NumberVector>	Parameterization class.
class	MacQueenKMeans<V extends NumberVector>	The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
static class	MacQueenKMeans.Par<V extends NumberVector>	Parameterization class.
class	ShallotKMeans<V extends NumberVector>	Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
static class	ShallotKMeans.Par<V extends NumberVector>	Parameterization class.
class	SimplifiedElkanKMeans<V extends NumberVector>	Simplified version of Elkan's k-means by exploiting the triangle inequality.
static class	SimplifiedElkanKMeans.Par<V extends NumberVector>	Parameterization class.
class	SingleAssignmentKMeans<V extends NumberVector>	Pseudo-k-means variations, that assigns each object to the nearest center.
static class	SingleAssignmentKMeans.Par<V extends NumberVector>	Parameterization class.
class	SortMeans<V extends NumberVector>	Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
static class	SortMeans.Par<V extends NumberVector>	Parameterization class.
class	XMeans<V extends NumberVector,M extends MeanModel>	X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
static class	XMeans.Par<V extends NumberVector,M extends MeanModel>	Parameterization class.
class	YinYangKMeans<V extends NumberVector>	Yin-Yang k-Means Clustering.
static class	YinYangKMeans.Par<V extends NumberVector>	Parameterization class.

Fields in elki.clustering.kmeans with type parameters of type NumberVector

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	AbstractKMeans.Instance.relation	Data relation.

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

Modifier and Type	Method	Description
FuzzyCMeans<NumberVector>	FuzzyCMeans.Par.make()

Methods in elki.clustering.kmeans with parameters of type NumberVector

Modifier and Type	Method	Description
private double	HartiganWongKMeans.Instance.cacheR1(DBIDIter it, NumberVector vec, int l1)	Compute and cache the R1 value.
private static void	AbstractKMeans.densePlusEquals(double[] sum, NumberVector vec)	Similar to VMath.plusEquals, but accepts a number vector.
private static void	AbstractKMeans.densePlusMinusEquals(double[] add, double[] sub, NumberVector vec)	Add to one, remove from another.
protected double	AbstractKMeans.Instance.distance(NumberVector x, double[] y)	Compute the squared distance (and count the distance computations).
protected double	AbstractKMeans.Instance.distance(NumberVector x, NumberVector y)	Compute the squared distance (and count the distance computations).
private double	BetulaLloydKMeans.distance(NumberVector x, double[] y)	Updates statistics and calculates distance between two Objects based on selected criteria.
protected static void	AbstractKMeans.incrementalUpdateMean(double[] mean, NumberVector vec, int newsize, double op)	Compute an incremental update for the mean.
static void	AbstractKMeans.minusEquals(double[] sum, NumberVector vec)	Similar to VMath.minusEquals, but accepts a number vector.
static void	AbstractKMeans.plusEquals(double[] sum, NumberVector vec)	Similar to VMath.plusEquals, but accepts a number vector.
static void	AbstractKMeans.plusMinusEquals(double[] add, double[] sub, NumberVector vec)	Add to one, remove from another.
protected double	AbstractKMeans.Instance.sqrtdistance(NumberVector x, double[] y)	Compute the distance (and count the distance computations).
protected double	AbstractKMeans.Instance.sqrtdistance(NumberVector x, NumberVector y)	Compute the distance (and count the distance computations).
private void	HartiganWongKMeans.Instance.transfer(DBIDRef it, NumberVector vec, int l1, int l2)	Transfer a point from one cluster to another.
private boolean	MacQueenKMeans.Instance.updateMeanAndAssignment(int minIndex, NumberVector fv, DBIDIter iditer)	Try to update the cluster assignment.

Method parameters in elki.clustering.kmeans with type arguments of type NumberVector

Modifier and Type	Method	Description
Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult(boolean varstat, Relation<? extends NumberVector> relation)	Build the result, recomputing the cluster variance if varstat is set to true.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeBoundedMidpoint(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using bounded midpoint splitting.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeMedian(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using median splitting.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeMidpoint(Relation<? extends NumberVector> relation, int left, int right)	Build the k-d-tree using midpoint splitting.
protected KDTreePruningKMeans.KDNode	KDTreePruningKMeans.Instance.buildTreeSSQ(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using a variance-based splitting strategy.
private static double[][]	AbstractKMeans.denseMeans(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected static double[][]	AbstractKMeans.means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected double[][]	KMediansLloyd.Instance.medians(java.util.List<? extends DBIDs> clusters, double[][] medians, Relation<? extends NumberVector> relation)	Returns the median vectors of the given clusters in the given database.
protected void	AbstractKMeans.Instance.recomputeVariance(Relation<? extends NumberVector> relation)	Recompute the cluster variances.
Clustering<KMeansModel>	BetulaLloydKMeans.run(Relation<NumberVector> relation)	Run the clustering algorithm.

Constructor parameters in elki.clustering.kmeans with type arguments of type NumberVector

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means, int t)	Constructor.
KDNode(Relation<? extends NumberVector> relation, DBIDArrayIter iter, int start, int end)	Constructor.

Uses of NumberVector in elki.clustering.kmeans.initialization

Classes in elki.clustering.kmeans.initialization with type parameters of type NumberVector

Modifier and Type	Class	Description
static class	FirstK.Par<V extends NumberVector>	Parameterization class.
class	SampleKMeans<V extends NumberVector>	Initialize k-means by running k-means on a sample of the data set only.
static class	SampleKMeans.Par<V extends NumberVector>	Parameterization class.

Fields in elki.clustering.kmeans.initialization with type parameters of type NumberVector

Modifier and Type	Field	Description
protected Relation<? extends NumberVector>	KMC2.Instance.relation	Data relation.
protected Relation<? extends NumberVector>	KMeansPlusPlus.NumberVectorInstance.relation	Data relation.
protected Relation<? extends NumberVector>	SphericalKMeansPlusPlus.Instance.relation	Data relation.

Methods in elki.clustering.kmeans.initialization with parameters of type NumberVector

Modifier and Type	Method	Description
protected double	KMC2.Instance.distance(NumberVector a, DBIDRef b)	Compute the distance of two objects.
protected double	KMeansPlusPlus.NumberVectorInstance.distance(NumberVector a, DBIDRef b)
protected double	KMC2.Instance.initialWeights(NumberVector first)	Initialize the weight list.
protected double	SphericalAFKMC2.Instance.initialWeights(NumberVector first)
protected double	SphericalKMeansPlusPlus.Instance.initialWeights(NumberVector first)	Initialize the weight list.
protected double	SphericalAFKMC2.Instance.similarity(NumberVector a, DBIDRef b)	Compute the distance of two objects.
protected double	SphericalKMeansPlusPlus.Instance.similarity(NumberVector a, DBIDRef b)	Compute the distance of two objects.
protected double	SphericalKMeansPlusPlus.Instance.updateWeights(NumberVector latest)	Update the weight list.

Method parameters in elki.clustering.kmeans.initialization with type arguments of type NumberVector

Modifier and Type	Method	Description
double[][]	AFKMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FarthestPoints.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FarthestSumPoints.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FirstK.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	KMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	KMeansInitialization.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)	Choose initial means
double[][]	KMeansPlusPlus.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Ostrovsky.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Predefined.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomlyChosen.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomNormalGenerated.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomUniformGenerated.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SampleKMeans.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SphericalAFKMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SphericalKMeansPlusPlus.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
protected void	KMC2.Instance.chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)	Choose remaining means, weighted by distance.
protected void	KMeansPlusPlus.NumberVectorInstance.chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)	Choose remaining means, weighted by distance.
protected void	SphericalKMeansPlusPlus.Instance.chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)	Choose remaining means, weighted by distance.
protected double	KMC2.Instance.distance(DBIDRef cand, java.util.List<NumberVector> means)	Minimum distance to the current means.
protected double	SphericalAFKMC2.Instance.distance(DBIDRef cand, java.util.List<NumberVector> means)
double[][]	Ostrovsky.NumberVectorInstance.run(Relation<? extends NumberVector> relation, int k)
static double[][]	AbstractKMeansInitialization.unboxVectors(java.util.List<? extends NumberVector> means)	Unbox database means to primitive means.

Constructor parameters in elki.clustering.kmeans.initialization with type arguments of type NumberVector

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, int m, double alpha, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, double alpha, RandomFactory rnd)	Constructor.
NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)	Constructor.
NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)	Constructor.

Uses of NumberVector in elki.clustering.kmeans.parallel

Classes in elki.clustering.kmeans.parallel with type parameters of type NumberVector

Modifier and Type	Class	Description
class	KMeansProcessor<V extends NumberVector>	Parallel k-means implementation.
static class	KMeansProcessor.Instance<V extends NumberVector>	Instance to process part of the data set, for a single iteration.
class	ParallelLloydKMeans<V extends NumberVector>	Parallel implementation of k-Means clustering.
static class	ParallelLloydKMeans.Par<V extends NumberVector>	Parameterization class

Uses of NumberVector in elki.clustering.kmeans.quality

Classes in elki.clustering.kmeans.quality with type parameters of type NumberVector

Modifier and Type	Class	Description
class	AbstractKMeansQualityMeasure<O extends NumberVector>	Base class for evaluating clusterings by information criteria (such as AIC or BIC).
interface	KMeansQualityMeasure<O extends NumberVector>	Interface for computing the quality of a K-Means clustering.

Methods in elki.clustering.kmeans.quality with type parameters of type NumberVector

Modifier and Type	Method	Description
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistance.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVariance.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)

Method parameters in elki.clustering.kmeans.quality with type arguments of type NumberVector

Modifier and Type	Method	Description
static double	AbstractKMeansQualityMeasure.logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionXMeans.logLikelihoodXMeans(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering)	Compute the number of free parameters.
static double	AbstractKMeansQualityMeasure.varianceContributionOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistance<?> distance, Relation<? extends NumberVector> relation)	Variance contribution of a single cluster.

Uses of NumberVector in elki.clustering.kmeans.spherical

Classes in elki.clustering.kmeans.spherical with type parameters of type NumberVector

Modifier and Type	Class	Description
class	EuclideanSphericalElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
static class	EuclideanSphericalElkanKMeans.Par<V extends NumberVector>	Parameterization class.
class	EuclideanSphericalHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
static class	EuclideanSphericalHamerlyKMeans.Par<V extends NumberVector>	Parameterization class.
class	EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
static class	EuclideanSphericalSimplifiedElkanKMeans.Par<V extends NumberVector>	Parameterization class.
class	SphericalElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
static class	SphericalElkanKMeans.Par<V extends NumberVector>	Parameterization class.
class	SphericalHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
static class	SphericalHamerlyKMeans.Par<V extends NumberVector>	Parameterization class.
class	SphericalKMeans<V extends NumberVector>	The standard spherical k-means algorithm.
static class	SphericalKMeans.Par<V extends NumberVector>	Parameterization class.
class	SphericalSimplifiedElkanKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
static class	SphericalSimplifiedElkanKMeans.Par<V extends NumberVector>	Parameterization class.
class	SphericalSimplifiedHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
static class	SphericalSimplifiedHamerlyKMeans.Par<V extends NumberVector>	Parameterization class.
class	SphericalSingleAssignmentKMeans<V extends NumberVector>	Pseudo-k-Means variations, that assigns each object to the nearest center.
static class	SphericalSingleAssignmentKMeans.Par<V extends NumberVector>	Parameterization class.

Methods in elki.clustering.kmeans.spherical with parameters of type NumberVector

Modifier and Type	Method	Description
protected double	SphericalKMeans.Instance.distance(NumberVector x, double[] y)
protected double	SphericalKMeans.Instance.distance(NumberVector x, NumberVector y)
protected double	SphericalKMeans.Instance.similarity(NumberVector vec1, double[] vec2)	Compute the similarity of two objects (and count this operation).
protected double	SphericalKMeans.Instance.sqrtdistance(NumberVector x, double[] y)
protected double	SphericalKMeans.Instance.sqrtdistance(NumberVector x, NumberVector y)

Method parameters in elki.clustering.kmeans.spherical with type arguments of type NumberVector

Modifier and Type	Method	Description
protected static double[][]	SphericalKMeans.Instance.means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)	Returns the mean vectors of the given clusters in the given database.
protected void	SphericalKMeans.Instance.recomputeVariance(Relation<? extends NumberVector> relation)

Constructor parameters in elki.clustering.kmeans.spherical with type arguments of type NumberVector

Constructor	Description
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, double[][] means)	Constructor.

Uses of NumberVector in elki.clustering.kmedoids.initialization

Method parameters in elki.clustering.kmedoids.initialization with type arguments of type NumberVector

Modifier and Type	Method	Description
double[][]	BUILD.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	LAB.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	ParkJun.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)

Uses of NumberVector in elki.clustering.onedimensional

Method parameters in elki.clustering.onedimensional with type arguments of type NumberVector

Modifier and Type	Method	Description
Clustering<ClusterModel>	KNNKernelDensityMinimaClustering.run(Relation<? extends NumberVector> relation)	Run the clustering algorithm on a data relation.

Uses of NumberVector in elki.clustering.optics

Classes in elki.clustering.optics with type parameters of type NumberVector

Modifier and Type	Class	Description
class	DeLiClu<V extends NumberVector>	DeliClu: Density-Based Hierarchical Clustering
static class	DeLiClu.Par<V extends NumberVector>	Parameterization class.
class	FastOPTICS<V extends NumberVector>	FastOPTICS algorithm (Fast approximation of OPTICS)
static class	FastOPTICS.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.clustering.subspace

Classes in elki.clustering.subspace with type parameters of type NumberVector

Modifier and Type	Class	Description
class	SUBCLU<V extends NumberVector>	Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.
static class	SUBCLU.Par<V extends NumberVector>	Parameterization class.

Fields in elki.clustering.subspace with type parameters of type NumberVector

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	DiSH.Instance.relation	Data relation.
private Relation<? extends NumberVector>	HiSC.Instance.relation	Data relation.

Methods in elki.clustering.subspace with type parameters of type NumberVector

Modifier and Type	Method	Description
<V extends NumberVector> Clustering<SubspaceModel>	PROCLUS.run(Relation<V> relation)	Performs the PROCLUS algorithm on the given database.

Methods in elki.clustering.subspace with parameters of type NumberVector

Modifier and Type	Method	Description
private double	PROCLUS.manhattanSegmentalDistance(NumberVector o1, double[] o2, long[] dimensions)	Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions.
private double	PROCLUS.manhattanSegmentalDistance(NumberVector o1, NumberVector o2, long[] dimensions)	Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions.
private int	DiSH.subspaceDimensionality(NumberVector v1, NumberVector v2, long[] pv1, long[] pv2, long[] commonPreferenceVector)	Compute the common subspace dimensionality of two vectors.
private void	CLIQUE.updateMinMax(NumberVector featureVector, double[] minima, double[] maxima)	Updates the minima and maxima array according to the specified feature vector.
protected static double	DiSH.weightedDistance(NumberVector v1, NumberVector v2, long[] weightVector)	Computes the weighted distance between the two specified vectors according to the given preference vector.
double	HiSC.weightedDistance(NumberVector v1, NumberVector v2, long[] weightVector)	Computes the weighted distance between the two specified vectors according to the given preference vector.

Method parameters in elki.clustering.subspace with type arguments of type NumberVector

Modifier and Type	Method	Description
private java.util.ArrayList<PROCLUS.PROCLUSCluster>	PROCLUS.assignPoints(ArrayDBIDs m_current, long[][] dimensions, Relation<? extends NumberVector> database)	Assigns the objects to the clusters.
private void	P3C.assignUnassigned(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, ModifiableDBIDs unassigned)	Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
private double	PROCLUS.avgDistance(double[] centroid, DBIDs objectIDs, Relation<? extends NumberVector> database, int dimension)	Computes the average distance of the objects to the centroid along the specified dimension.
private void	DiSH.buildHierarchy(Relation<? extends NumberVector> database, Clustering<SubspaceModel> clustering, java.util.List<Cluster<SubspaceModel>> clusters, int dimensionality)	Builds the cluster hierarchy.
private void	DiSH.checkClusters(Relation<? extends NumberVector> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)	Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private Clustering<SubspaceModel>	DiSH.computeClusters(Relation<? extends NumberVector> database, DiSH.DiSHClusterOrder clusterOrder)	Computes the hierarchical clusters according to the cluster order.
private void	P3C.computeFuzzyMembership(Relation<? extends NumberVector> relation, java.util.ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, int dim)	Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
protected boolean	DOC.dimensionIsRelevant(int dimension, Relation<? extends NumberVector> relation, DBIDs points)	Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).
private double	PROCLUS.evaluateClusters(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, long[][] dimensions, Relation<? extends NumberVector> database)	Evaluates the quality of the clusters.
private it.unimi.dsi.fastutil.objects.Object2ObjectOpenCustomHashMap<long[],java.util.List<ArrayModifiableDBIDs>>	DiSH.extractClusters(Relation<? extends NumberVector> relation, DiSH.DiSHClusterOrder clusterOrder)	Extracts the clusters from the cluster order.
private java.util.List<PROCLUS.PROCLUSCluster>	PROCLUS.finalAssignment(java.util.List<Pair<double[],long[]>> dimensions, Relation<? extends NumberVector> database)	Refinement step to assign the objects to the final clusters.
private java.util.List<CLIQUESubspace>	CLIQUE.findDenseSubspaceCandidates(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces)	Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.
private java.util.List<CLIQUESubspace>	CLIQUE.findDenseSubspaces(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces)	Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.
private long[][]	PROCLUS.findDimensions(ArrayDBIDs medoids, Relation<? extends NumberVector> relation, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)	Determines the set of correlated dimensions for each medoid in the specified medoid set.
private long[][]	PROCLUS.findDimensions(ArrayDBIDs medoids, Relation<? extends NumberVector> relation, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)	Determines the set of correlated dimensions for each medoid in the specified medoid set.
private java.util.List<Pair<double[],long[]>>	PROCLUS.findDimensions(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, Relation<? extends NumberVector> database)	Refinement step that determines the set of correlated dimensions for each cluster centroid.
protected DBIDs	DOC.findNeighbors(DBIDRef q, long[] nD, ArrayModifiableDBIDs S, Relation<? extends NumberVector> relation)	Find the neighbors of point q in the given subspace
private java.util.List<CLIQUESubspace>	CLIQUE.findOneDimensionalDenseSubspaceCandidates(Relation<? extends NumberVector> database)	Determines the one-dimensional dense subspace candidates by making a pass over the database.
private java.util.List<CLIQUESubspace>	CLIQUE.findOneDimensionalDenseSubspaces(Relation<? extends NumberVector> database)	Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
private void	P3C.findOutliers(Relation<? extends NumberVector> relation, java.util.List<MultivariateGaussianModel> models, java.util.ArrayList<P3C.ClusterCandidate> clusterCandidates, ModifiableDBIDs noise)	Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.
private Pair<long[],ArrayModifiableDBIDs>	DiSH.findParent(Relation<? extends NumberVector> relation, Pair<long[],ArrayModifiableDBIDs> child, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)	Returns the parent of the specified cluster
private DataStore<DBIDs>	PROCLUS.getLocalities(DBIDs medoids, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)	Computes the localities of the specified medoids: for each medoid m the objects in the sphere centered at m with radius minDist are determined, where minDist is the minimum distance between medoid m and any other medoid m_i.
private ArrayDBIDs	PROCLUS.greedy(DistanceQuery<? extends NumberVector> distance, DBIDs sampleSet, int m, java.util.Random random)	Returns a piercing set of k medoids from the specified sample set.
private java.util.Collection<CLIQUEUnit>	CLIQUE.initOneDimensionalUnits(Relation<? extends NumberVector> database)	Initializes and returns the one dimensional units.
private boolean	DiSH.isParent(Relation<? extends NumberVector> relation, Cluster<SubspaceModel> parent, It<Cluster<SubspaceModel>> iter, int db_dim)	Returns true, if the specified parent cluster is a parent of one child of the children clusters.
protected Cluster<SubspaceModel>	DOC.makeCluster(Relation<? extends NumberVector> relation, DBIDs C, long[] D)	Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
private SetDBIDs[][]	P3C.partitionData(Relation<? extends NumberVector> relation, int bins)	Partition the data set into bins bins in each dimension independently.
Clustering<SubspaceModel>	CLIQUE.run(Relation<? extends NumberVector> relation)	Performs the CLIQUE algorithm on the given database.
Clustering<SubspaceModel>	DiSH.run(Relation<? extends NumberVector> relation)	Performs the DiSH algorithm on the given database.
Clustering<SubspaceModel>	DOC.run(Relation<? extends NumberVector> relation)	Performs the DOC or FastDOC (as configured) algorithm.
ClusterOrder	HiSC.run(Relation<? extends NumberVector> relation)	Run the HiSC algorithm
Clustering<SubspaceModel>	P3C.run(Relation<? extends NumberVector> relation)	Performs the P3C algorithm on the given Database.
protected Cluster<SubspaceModel>	DOC.runDOC(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize)	Performs a single run of DOC, finding a single cluster.
protected Cluster<SubspaceModel>	FastDOC.runDOC(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize)	Performs a single run of FastDOC, finding a single cluster.
private java.util.List<Cluster<SubspaceModel>>	DiSH.sortClusters(Relation<? extends NumberVector> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)	Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

Constructor parameters in elki.clustering.subspace with type arguments of type NumberVector

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

Uses of NumberVector in elki.clustering.subspace.clique

Methods in elki.clustering.subspace.clique with parameters of type NumberVector

Modifier and Type	Method	Description
boolean	CLIQUEUnit.addFeatureVector(DBIDRef id, NumberVector vector)	Adds the id of the specified feature vector to this unit, if this unit contains the feature vector.
boolean	CLIQUEUnit.contains(NumberVector vector)	Returns true, if the intervals of this unit contain the specified feature vector.

Uses of NumberVector in elki.clustering.svm

Fields in elki.clustering.svm with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) PrimitiveSimilarity<? super NumberVector>	SupportVectorClustering.kernel	Kernel function.
protected PrimitiveSimilarity<? super NumberVector>	SupportVectorClustering.Par.kernel	Kernel in use.

Methods in elki.clustering.svm with parameters of type NumberVector

Modifier and Type	Method	Description
private boolean	SupportVectorClustering.accept(NumberVector cur, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_square)	evaluate if a point cur is inside the sphere in kernel space.

Method parameters in elki.clustering.svm with type arguments of type NumberVector

Modifier and Type	Method	Description
private boolean	SupportVectorClustering.accept(NumberVector cur, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_square)	evaluate if a point cur is inside the sphere in kernel space.
private double	SupportVectorClustering.calcfixedpart(RegressionModel model, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim)	calculate fixed part of model evaluation
private boolean	SupportVectorClustering.checkConnectivity(Relation<NumberVector> relation, double[] start, DBIDRef destRef, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_squared)	Checks if the connecting line between start and dest lies inside the kernel space sphere.
private boolean	SupportVectorClustering.checkConnectivity(Relation<NumberVector> relation, double[] start, DBIDRef destRef, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_squared)	Checks if the connecting line between start and dest lies inside the kernel space sphere.
Clustering<Model>	SupportVectorClustering.run(Relation<NumberVector> relation)	perform clustering

Constructor parameters in elki.clustering.svm with type arguments of type NumberVector

Constructor	Description
SupportVectorClustering(PrimitiveSimilarity<? super NumberVector> kernel, double C)	Constructor.

Uses of NumberVector in elki.clustering.uncertain

Methods in elki.clustering.uncertain with parameters of type NumberVector

Modifier and Type	Method	Description
protected double	UKMeans.getExpectedRepDistance(NumberVector rep, DiscreteUncertainObject uo)	Get expected distance between a Vector and an uncertain object

Constructor parameters in elki.clustering.uncertain with type arguments of type NumberVector

Constructor	Description
CKMeans(NumberVectorDistance<? super NumberVector> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor that uses Lloyd's k-means algorithm.

Uses of NumberVector in elki.data

Classes in elki.data with type parameters of type NumberVector

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

Subinterfaces of NumberVector in elki.data

Modifier and Type	Interface	Description
interface	SparseNumberVector	Combines the SparseFeatureVector and NumberVector.

Classes in elki.data that implement NumberVector

Modifier and Type	Class	Description
class	BitVector	Vector using a dense bit set encoding, based on long[] storage.
class	ByteVector	Vector using byte[] storage.
class	DoubleVector	Vector type using double[] storage for real numbers.
class	FloatVector	Vector type using float[] storage, thus needing approximately half as much memory as DoubleVector.
class	IntegerVector	Vector type using int[] storage.
class	OneDimensionalDoubleVector	Specialized class implementing a one-dimensional double vector without using an array.
class	ShortVector	Vector type using short[] storage.
class	SparseByteVector	Sparse vector type, using byte[] for storing the values, and int[] for storing the indexes, approximately 5 bytes per non-zero value (limited to -128..+127).
class	SparseDoubleVector	Sparse vector type, using double[] for storing the values, and int[] for storing the indexes, approximately 12 bytes per non-zero value.
class	SparseFloatVector	Sparse vector type, using float[] for storing the values, and int[] for storing the indexes, approximately 8 bytes per non-zero value.
class	SparseIntegerVector	Sparse vector type, using int[] for storing the values, and int[] for storing the indexes, approximately 8 bytes per non-zero integer value.
class	SparseShortVector	Sparse vector type, using short[] for storing the values, and int[] for storing the indexes, approximately 6 bytes per non-zero value.

Fields in elki.data with type parameters of type NumberVector

Modifier and Type	Field	Description
private Relation<? extends NumberVector>	VectorUtil.SortDBIDsBySingleDimension.data	The relation to sort.
static VectorFieldTypeInformation<NumberVector>	NumberVector.FIELD	Input type for algorithms that require number vector fields.
static VectorFieldTypeInformation<NumberVector>	NumberVector.FIELD_1D	Type request for two-dimensional number vectors
static VectorFieldTypeInformation<NumberVector>	NumberVector.FIELD_2D	Type request for two-dimensional number vectors
static VectorTypeInformation<NumberVector>	NumberVector.VARIABLE_LENGTH	Number vectors of variable length.

Methods in elki.data with type parameters of type NumberVector

Modifier and Type	Method	Description
static <V extends NumberVector> V	VectorUtil.project(V v, long[] selectedAttributes, NumberVector.Factory<V> factory)	Project a number vector to 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, java.util.Random r)	Produce a new vector based on random numbers in [0:1].

Methods in elki.data with parameters of type NumberVector

Modifier and Type	Method	Description
static double	VectorUtil.angle(NumberVector v1, NumberVector v2, NumberVector o)	Compute the angle between two vectors with respect to a reference point.
static double	VectorUtil.angleDense(NumberVector v1, NumberVector v2)	Compute the absolute cosine of the angle between two dense vectors.
static double	VectorUtil.angleSparseDense(SparseNumberVector v1, NumberVector v2)	Compute the angle for a sparse and a dense vector.
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.dot(NumberVector v1, double[] v2)	Compute the dot product of the angle between two vectors.
static double	VectorUtil.dot(NumberVector v1, NumberVector v2)	Compute the dot product of the angle between two vectors.
static double	VectorUtil.dotDense(NumberVector v1, double[] v2)	Compute the dot product of two dense vectors.
static double	VectorUtil.dotDense(NumberVector v1, NumberVector v2)	Compute the dot product of two dense vectors.
static double	VectorUtil.dotSparseDense(SparseNumberVector v1, NumberVector v2)	Compute the dot product for a sparse and a dense vector.
default V	NumberVector.Factory.newNumberVector(NumberVector values)	Returns a new NumberVector of N for the given values.

Constructor parameters in elki.data with type arguments of type NumberVector

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

Uses of NumberVector in elki.data.model

Methods in elki.data.model with type parameters of type NumberVector

Modifier and Type	Method	Description
static <V extends NumberVector> V	ModelUtil.getPrototype(Model model, Relation<? extends V> relation, NumberVector.Factory<V> factory)	Get (and convert!)
static <V extends NumberVector> V	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends V> relation, DBIDs ids, NumberVector.Factory<V> factory)	Get the representative vector for a cluster model, or compute the centroid.

Methods in elki.data.model that return NumberVector

Modifier and Type	Method	Description
static NumberVector	ModelUtil.getPrototype(Model model, Relation<? extends NumberVector> relation)	Get the representative vector for a cluster model.
static NumberVector	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids)	Get the representative vector for a cluster model, or compute the centroid.

Methods in elki.data.model with parameters of type NumberVector

Modifier and Type	Method	Description
double[]	CorrelationAnalysisSolution.dataVector(NumberVector p)	Returns the data vectors after projection.
double[]	CorrelationAnalysisSolution.errorVector(NumberVector p)	Returns the error vectors after projection.
double	CorrelationAnalysisSolution.squaredDistance(NumberVector p)	Returns the distance of NumberVector p from the hyperplane underlying this solution.

Method parameters in elki.data.model with type arguments of type NumberVector

Modifier and Type	Method	Description
static NumberVector	ModelUtil.getPrototype(Model model, Relation<? extends NumberVector> relation)	Get the representative vector for a cluster model.
static NumberVector	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids)	Get the representative vector for a cluster model, or compute the centroid.

Constructor parameters in elki.data.model with type arguments of type NumberVector

Constructor	Description
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<? extends NumberVector> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid)	Provides a new CorrelationAnalysisSolution holding the specified matrix.
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<? extends NumberVector> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid, java.text.NumberFormat nf)	Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.

Uses of NumberVector in elki.data.projection

Classes in elki.data.projection with type parameters of type NumberVector

Modifier and Type	Class	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.Par<V extends NumberVector>	Parameterization class.
class	RandomProjection<V extends NumberVector>	Randomized projections of the data.

Methods in elki.data.projection that return types with arguments of type NumberVector

Modifier and Type	Method	Description
LatLngToECEFProjection<NumberVector>	LatLngToECEFProjection.Par.make()
LngLatToECEFProjection<NumberVector>	LngLatToECEFProjection.Par.make()
RandomProjection<NumberVector>	RandomProjection.Par.make()

Methods in elki.data.projection with parameters of type NumberVector

Modifier and Type	Method	Description
java.lang.Double	FeatureSelection.ProjectedNumberFeatureVectorAdapter.get(NumberVector array, int off)
double	FeatureSelection.ProjectedNumberFeatureVectorAdapter.getDouble(NumberVector array, int off)
long	FeatureSelection.ProjectedNumberFeatureVectorAdapter.getLong(NumberVector array, int off)
int	FeatureSelection.ProjectedNumberFeatureVectorAdapter.size(NumberVector array)

Uses of NumberVector in elki.data.projection.random

Methods in elki.data.projection.random with parameters of type NumberVector

Modifier and Type	Method	Description
double[]	AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector in)
double[]	AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector in, double[] ret)
double[]	RandomProjectionFamily.Projection.project(NumberVector in)	Project a single vector.
double[]	RandomProjectionFamily.Projection.project(NumberVector in, double[] buffer)	Project a single vector, into the given buffer.
double[]	RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector in)
double[]	RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector in, double[] buffer)
double[]	SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.project(NumberVector in)
double[]	SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.project(NumberVector vec, double[] ret)
private double[]	SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.projectDense(NumberVector in, double[] ret)	Slower version, for dense multiplication.

Uses of NumberVector in elki.data.type

Fields in elki.data.type with type parameters of type NumberVector

Modifier and Type	Field	Description
static MultivariateSeriesTypeInformation<NumberVector>	TypeUtil.MULTIVARIATE_SERIES	Type request for multivariate time series.
static VectorFieldTypeInformation<NumberVector>	TypeUtil.NUMBER_VECTOR_FIELD	Input type for algorithms that require number vector fields.
static VectorFieldTypeInformation<? super NumberVector>	TypeUtil.NUMBER_VECTOR_FIELD_1D	Type request for two-dimensional number vectors
static VectorFieldTypeInformation<? super NumberVector>	TypeUtil.NUMBER_VECTOR_FIELD_2D	Type request for two-dimensional number vectors
static VectorTypeInformation<NumberVector>	TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH	Number vectors of variable length.

Uses of NumberVector in elki.data.uncertain

Methods in elki.data.uncertain with parameters of type NumberVector

Modifier and Type	Method	Description
protected static HyperBoundingBox	AbstractUncertainObject.computeBounds(NumberVector[] samples)	Compute the bounding box for some samples.

Uses of NumberVector in elki.database.query.distance

Classes in elki.database.query.distance with type parameters of type NumberVector

Modifier and Type	Class	Description
class	LinearScanEuclideanPrioritySearcher<Q,O extends NumberVector>	Default linear scan search class, for Euclidean distance.
static class	LinearScanEuclideanPrioritySearcher.ByDBID<O extends NumberVector>	Search by DBID.
static class	LinearScanEuclideanPrioritySearcher.ByObject<O extends NumberVector>	Search by Object.

Fields in elki.database.query.distance declared as NumberVector

Modifier and Type	Field	Description
private O	LinearScanEuclideanPrioritySearcher.query	Current query object.

Uses of NumberVector in elki.database.query.knn

Classes in elki.database.query.knn with type parameters of type NumberVector

Modifier and Type	Class	Description
class	LinearScanEuclideanKNNByObject<O extends NumberVector>	Instance of this query for a particular database.

Uses of NumberVector in elki.database.query.range

Classes in elki.database.query.range with type parameters of type NumberVector

Modifier and Type	Class	Description
class	LinearScanEuclideanRangeByObject<O extends NumberVector>	Optimized linear scan for Euclidean distance range queries.

Uses of NumberVector in elki.database.relation

Methods in elki.database.relation with type parameters of type NumberVector

Modifier and Type	Method	Description
static <V extends NumberVector> NumberVector.Factory<V>	RelationUtil.getNumberVectorFactory(Relation<V> relation)	Get the number vector factory of a database relation.
static <V extends NumberVector,T extends NumberVector> Relation<V>	RelationUtil.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>	RelationUtil.relationUglyVectorCast(Relation<T> database)	An ugly vector type cast unavoidable in some situations due to Generics.

Method parameters in elki.database.relation with type arguments of type NumberVector

Modifier and Type	Method	Description
static double[][]	RelationUtil.computeMinMax(Relation<? extends NumberVector> relation)	Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static double[][]	RelationUtil.relationAsMatrix(Relation<? extends NumberVector> relation, ArrayDBIDs ids)	Copy a relation into a double matrix.

Uses of NumberVector in elki.datasource.filter

Classes in elki.datasource.filter with type parameters of type NumberVector

Modifier and Type	Class	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.

Methods in elki.datasource.filter with type parameters of type NumberVector

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

Uses of NumberVector in elki.datasource.filter.cleaning

Classes in elki.datasource.filter.cleaning with type parameters of type NumberVector

Modifier and Type	Class	Description
class	VectorDimensionalityFilter<V extends NumberVector>	Filter to remove all vectors that do not have the desired dimensionality.
static class	VectorDimensionalityFilter.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.datasource.filter.normalization.columnwise

Classes in elki.datasource.filter.normalization.columnwise with type parameters of type NumberVector

Modifier and Type	Class	Description
class	AttributeWiseBetaNormalization<V extends NumberVector>	Project the data using a Beta distribution.
static class	AttributeWiseBetaNormalization.Par<V extends NumberVector>	Parameterization class.
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.Par<V extends NumberVector>	Parameterization class.
class	AttributeWiseMADNormalization<V extends NumberVector>	Median Absolute Deviation is used for scaling the data set as follows:
class	AttributeWiseMeanNormalization<V extends NumberVector>	Normalization designed for data with a meaningful zero: The 0 is retained, and the data is linearly scaled to have a mean of 1, by projection with f(x) = x / mean(X).
class	AttributeWiseMinMaxNormalization<V extends NumberVector>	Class to perform and undo a normalization on real vectors with respect to a given minimum and maximum in each dimension.
static class	AttributeWiseMinMaxNormalization.Par<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.Par<V extends NumberVector>	Parameterization class.

Fields in elki.datasource.filter.normalization.columnwise with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) java.util.List<? extends NumberVector>	IntegerRankTieNormalization.Sorter.col	Column to use for sorting.

Method parameters in elki.datasource.filter.normalization.columnwise with type arguments of type NumberVector

Modifier and Type	Method	Description
java.lang.Double	AttributeWiseCDFNormalization.Adapter.get(java.util.List<? extends NumberVector> array, int off)
double	AttributeWiseCDFNormalization.Adapter.getDouble(java.util.List<? extends NumberVector> array, int off)
long	AttributeWiseCDFNormalization.Adapter.getLong(java.util.List<? extends NumberVector> array, int off)
void	IntegerRankTieNormalization.Sorter.setup(java.util.List<? extends NumberVector> col, int dim)	Configure the sorting class.
int	AttributeWiseCDFNormalization.Adapter.size(java.util.List<? extends NumberVector> array)

Uses of NumberVector in elki.datasource.filter.normalization.instancewise

Classes in elki.datasource.filter.normalization.instancewise with type parameters of type NumberVector

Modifier and Type	Class	Description
class	HellingerHistogramNormalization<V extends NumberVector>	Normalize histograms by scaling them to unit absolute sum, then taking the square root of the absolute value in each attribute, times the normalization constant \(1/\sqrt{2}\).
class	InstanceLogRankNormalization<V extends NumberVector>	Normalize vectors such that the smallest value of each instance is 0, the largest is 1, but using \( \log_2(1+x) \).
class	InstanceMeanVarianceNormalization<V extends NumberVector>	Normalize vectors such that they have zero mean and unit variance.
static class	InstanceMeanVarianceNormalization.Par<V extends NumberVector>	Parameterization class.
class	InstanceMinMaxNormalization<V extends NumberVector>	Normalize vectors with respect to a given minimum and maximum in each dimension.
static class	InstanceMinMaxNormalization.Par<V extends NumberVector>	Parameterization class.
class	InstanceRankNormalization<V extends NumberVector>	Normalize vectors such that the smallest value of each instance is 0, the largest is 1.
class	LengthNormalization<V extends NumberVector>	Class to perform a normalization on vectors to norm 1.
static class	LengthNormalization.Par<V extends NumberVector>	Parameterization class.
class	Log1PlusNormalization<V extends NumberVector>	Normalize the data set by applying \( \frac{\log(1+|x|b)}{\log 1+b} \) to any value.
static class	Log1PlusNormalization.Par<V extends NumberVector>	Parameterization class.

Fields in elki.datasource.filter.normalization.instancewise with type parameters of type NumberVector

Modifier and Type	Field	Description
static HellingerHistogramNormalization<NumberVector>	HellingerHistogramNormalization.STATIC	Static instance.
static Log1PlusNormalization<NumberVector>	Log1PlusNormalization.STATIC	Static instance.

Methods in elki.datasource.filter.normalization.instancewise that return types with arguments of type NumberVector

Modifier and Type	Method	Description
HellingerHistogramNormalization<NumberVector>	HellingerHistogramNormalization.Par.make()
InstanceLogRankNormalization<NumberVector>	InstanceLogRankNormalization.Par.make()
InstanceRankNormalization<NumberVector>	InstanceRankNormalization.Par.make()

Uses of NumberVector in elki.datasource.filter.transform

Classes in elki.datasource.filter.transform with type parameters of type NumberVector

Modifier and Type	Class	Description
class	AbstractSupervisedProjectionVectorFilter<V extends NumberVector>	Base class for supervised projection methods.
static class	AbstractSupervisedProjectionVectorFilter.Par<V extends NumberVector>	Parameterization class.
class	ClassicMultidimensionalScalingTransform<I,O extends NumberVector>	Rescale the data set using multidimensional scaling, MDS.
static class	ClassicMultidimensionalScalingTransform.Par<I,O extends NumberVector>	Parameterization class.
class	FastMultidimensionalScalingTransform<I,O extends NumberVector>	Rescale the data set using multidimensional scaling, MDS.
static class	FastMultidimensionalScalingTransform.Par<I,O extends NumberVector>	Parameterization class.
class	GlobalPrincipalComponentAnalysisTransform<O extends NumberVector>	Apply Principal Component Analysis (PCA) to the data set.
static class	GlobalPrincipalComponentAnalysisTransform.Par<O extends NumberVector>	Parameterization class.
class	HistogramJitterFilter<V extends NumberVector>	Add jitter, preserving the histogram properties (same sum, nonnegative).
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.Par<V extends NumberVector>	Parameterization class.
class	LinearDiscriminantAnalysisFilter<V extends NumberVector>	Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
static class	LinearDiscriminantAnalysisFilter.Par<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.Par<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.
class	PerturbationFilter<V extends NumberVector>	A filter to perturb the values by adding micro-noise.
static class	PerturbationFilter.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.datasource.filter.typeconversions

Classes in elki.datasource.filter.typeconversions with type parameters of type NumberVector

Modifier and Type	Class	Description
class	SplitNumberVectorFilter<V extends NumberVector>	Split an existing column into two types.
static class	SplitNumberVectorFilter.Par<V extends NumberVector>	Parameterization class.

Methods in elki.datasource.filter.typeconversions that return types with arguments of type NumberVector

Modifier and Type	Method	Description
protected SimpleTypeInformation<? super NumberVector>	UncertainSplitFilter.getInputTypeRestriction()
protected SimpleTypeInformation<? super NumberVector>	WeightedUncertainSplitFilter.getInputTypeRestriction()

Methods in elki.datasource.filter.typeconversions with parameters of type NumberVector

Modifier and Type	Method	Description
protected UnweightedDiscreteUncertainObject	UncertainSplitFilter.filterSingleObject(NumberVector vec)
protected WeightedDiscreteUncertainObject	WeightedUncertainSplitFilter.filterSingleObject(NumberVector vec)

Method parameters in elki.datasource.filter.typeconversions with type arguments of type NumberVector

Modifier and Type	Method	Description
protected SimpleTypeInformation<UnweightedDiscreteUncertainObject>	UncertainSplitFilter.convertedType(SimpleTypeInformation<NumberVector> in)
protected SimpleTypeInformation<WeightedDiscreteUncertainObject>	WeightedUncertainSplitFilter.convertedType(SimpleTypeInformation<NumberVector> in)

Uses of NumberVector in elki.datasource.parser

Classes in elki.datasource.parser with type parameters of type NumberVector

Modifier and Type	Class	Description
class	CategorialDataAsNumberVectorParser<V extends NumberVector>	A very simple parser for categorial data, which will then be encoded as numbers.
static class	CategorialDataAsNumberVectorParser.Par<V extends NumberVector>	Parameterization class.
class	NumberVectorLabelParser<V extends NumberVector>	Parser for a simple CSV type of format, with columns separated by the given pattern (default: whitespace).
static class	NumberVectorLabelParser.Par<V extends NumberVector>	Parameterization class.

Fields in elki.datasource.parser declared as NumberVector

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

Uses of NumberVector in elki.distance

Methods in elki.distance that return types with arguments of type NumberVector

Modifier and Type	Method	Description
SimpleTypeInformation<? super NumberVector>	AbstractNumberVectorDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ArcCosineDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ArcCosineUnitlengthDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	CanberraDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ClarkDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	CosineDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	CosineUnitlengthDistance.getInputTypeRestriction()
VectorFieldTypeInformation<? super NumberVector>	MatrixWeightedQuadraticDistance.getInputTypeRestriction()

Methods in elki.distance with parameters of type NumberVector

Modifier and Type	Method	Description
static int	AbstractNumberVectorDistance.dimensionality(NumberVector o1, NumberVector o2)	Get the common dimensionality of the two objects.
static int	AbstractNumberVectorDistance.dimensionality(NumberVector o1, NumberVector o2, int expect)	Get the common dimensionality of the two objects.
double	ArcCosineDistance.distance(NumberVector v1, NumberVector v2)
double	ArcCosineUnitlengthDistance.distance(NumberVector v1, NumberVector v2)
double	BrayCurtisDistance.distance(NumberVector v1, NumberVector v2)
double	CanberraDistance.distance(NumberVector v1, NumberVector v2)
double	ClarkDistance.distance(NumberVector v1, NumberVector v2)
double	CosineDistance.distance(NumberVector v1, NumberVector v2)
double	CosineUnitlengthDistance.distance(NumberVector v1, NumberVector v2)
double	MahalanobisDistance.distance(NumberVector o1, NumberVector o2)
double	MatrixWeightedQuadraticDistance.distance(NumberVector o1, NumberVector o2)
double	NumberVectorDistance.distance(NumberVector o1, NumberVector o2)	Computes the distance between two given vectors according to this distance function.
double	SqrtCosineDistance.distance(NumberVector v1, NumberVector v2)
double	SqrtCosineUnitlengthDistance.distance(NumberVector v1, NumberVector v2)
double	WeightedCanberraDistance.distance(NumberVector v1, NumberVector v2)
double	MahalanobisDistance.norm(NumberVector obj)
double	MatrixWeightedQuadraticDistance.norm(NumberVector obj)

Uses of NumberVector in elki.distance.colorhistogram

Methods in elki.distance.colorhistogram with parameters of type NumberVector

Modifier and Type	Method	Description
double	HistogramIntersectionDistance.distance(NumberVector v1, NumberVector v2)

Uses of NumberVector in elki.distance.correlation

Methods in elki.distance.correlation with parameters of type NumberVector

Modifier and Type	Method	Description
double	AbsolutePearsonCorrelationDistance.distance(NumberVector v1, NumberVector v2)	Computes the absolute Pearson correlation distance for two given feature vectors.
double	AbsoluteUncenteredCorrelationDistance.distance(NumberVector v1, NumberVector v2)
double	PearsonCorrelationDistance.distance(NumberVector v1, NumberVector v2)	Computes the Pearson correlation distance for two given feature vectors.
double	SquaredPearsonCorrelationDistance.distance(NumberVector v1, NumberVector v2)
double	SquaredUncenteredCorrelationDistance.distance(NumberVector v1, NumberVector v2)
double	UncenteredCorrelationDistance.distance(NumberVector v1, NumberVector v2)	Computes the Pearson correlation distance for two given feature vectors.
double	WeightedPearsonCorrelationDistance.distance(NumberVector v1, NumberVector v2)	Computes the Pearson correlation distance for two given feature vectors.
double	WeightedSquaredPearsonCorrelationDistance.distance(NumberVector v1, NumberVector v2)
static double	UncenteredCorrelationDistance.uncenteredCorrelation(NumberVector x, NumberVector y)	Compute the uncentered correlation of two vectors.

Uses of NumberVector in elki.distance.geo

Methods in elki.distance.geo that return types with arguments of type NumberVector

Modifier and Type	Method	Description
SimpleTypeInformation<? super NumberVector>	DimensionSelectingLatLngDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	LatLngDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	LngLatDistance.getInputTypeRestriction()

Methods in elki.distance.geo with parameters of type NumberVector

Modifier and Type	Method	Description
double	DimensionSelectingLatLngDistance.distance(NumberVector o1, NumberVector o2)
double	LatLngDistance.distance(NumberVector o1, NumberVector o2)
double	LngLatDistance.distance(NumberVector o1, NumberVector o2)

Uses of NumberVector in elki.distance.histogram

Methods in elki.distance.histogram with parameters of type NumberVector

Modifier and Type	Method	Description
double	HistogramMatchDistance.distance(NumberVector v1, NumberVector v2)
double	KolmogorovSmirnovDistance.distance(NumberVector v1, NumberVector v2)

Uses of NumberVector in elki.distance.minkowski

Methods in elki.distance.minkowski that return types with arguments of type NumberVector

Modifier and Type	Method	Description
SimpleTypeInformation<? super NumberVector>	LPNormDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	SquaredEuclideanDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	WeightedLPNormDistance.getInputTypeRestriction()

Methods in elki.distance.minkowski with parameters of type NumberVector

Modifier and Type	Method	Description
double	EuclideanDistance.distance(NumberVector v1, NumberVector v2)
double	LPIntegerNormDistance.distance(NumberVector v1, NumberVector v2)
double	LPNormDistance.distance(NumberVector v1, NumberVector v2)
double	ManhattanDistance.distance(NumberVector v1, NumberVector v2)
double	MaximumDistance.distance(NumberVector v1, NumberVector v2)
double	MinimumDistance.distance(NumberVector v1, NumberVector v2)
double	SquaredEuclideanDistance.distance(NumberVector v1, NumberVector v2)
double	WeightedEuclideanDistance.distance(NumberVector v1, NumberVector v2)
double	WeightedLPNormDistance.distance(NumberVector v1, NumberVector v2)
double	WeightedManhattanDistance.distance(NumberVector v1, NumberVector v2)
double	WeightedMaximumDistance.distance(NumberVector v1, NumberVector v2)
double	WeightedSquaredEuclideanDistance.distance(NumberVector v1, NumberVector v2)
double	EuclideanDistance.norm(NumberVector v)
double	LPIntegerNormDistance.norm(NumberVector v)
double	LPNormDistance.norm(NumberVector v)
double	ManhattanDistance.norm(NumberVector v)
double	MaximumDistance.norm(NumberVector v)
double	MinimumDistance.norm(NumberVector v)
double	SquaredEuclideanDistance.norm(NumberVector v)
double	WeightedEuclideanDistance.norm(NumberVector v)
double	WeightedLPNormDistance.norm(NumberVector v)
double	WeightedManhattanDistance.norm(NumberVector v)
double	WeightedMaximumDistance.norm(NumberVector v)
double	WeightedSquaredEuclideanDistance.norm(NumberVector obj)
private double	EuclideanDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	LPIntegerNormDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)	Compute unscaled distance in a range of dimensions.
private double	LPNormDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)	Compute unscaled distance in a range of dimensions.
private double	ManhattanDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	MaximumDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	SquaredEuclideanDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedEuclideanDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedLPNormDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedManhattanDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedMaximumDistance.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	EuclideanDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	LPIntegerNormDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)	Compute unscaled distance in a range of dimensions.
private double	LPNormDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)	Compute unscaled distance in a range of dimensions.
private double	ManhattanDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	MaximumDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	SquaredEuclideanDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedEuclideanDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedLPNormDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedManhattanDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedMaximumDistance.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	EuclideanDistance.preNorm(NumberVector v, int start, int end)
private double	LPIntegerNormDistance.preNorm(NumberVector v, int start, int end)	Compute unscaled norm in a range of dimensions.
private double	LPNormDistance.preNorm(NumberVector v, int start, int end)	Compute unscaled norm in a range of dimensions.
private double	ManhattanDistance.preNorm(NumberVector v, int start, int end)
private double	MaximumDistance.preNorm(NumberVector v, int start, int end)
private double	SquaredEuclideanDistance.preNorm(NumberVector v, int start, int end)
private double	WeightedEuclideanDistance.preNorm(NumberVector v, int start, int end)
private double	WeightedLPNormDistance.preNorm(NumberVector v, int start, int end)
private double	WeightedManhattanDistance.preNorm(NumberVector v, int start, int end)
private double	WeightedMaximumDistance.preNorm(NumberVector v, int start, int end)

Uses of NumberVector in elki.distance.probabilistic

Methods in elki.distance.probabilistic with type parameters of type NumberVector

Modifier and Type	Method	Description
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	HellingerDistance.instantiate(Relation<T> database)

Methods in elki.distance.probabilistic that return types with arguments of type NumberVector

Modifier and Type	Method	Description
SimpleTypeInformation<? super NumberVector>	ChiSquaredDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	FisherRaoDistance.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	HellingerDistance.getInputTypeRestriction()

Methods in elki.distance.probabilistic with parameters of type NumberVector

Modifier and Type	Method	Description
double	ChiDistance.distance(NumberVector v1, NumberVector v2)
double	ChiSquaredDistance.distance(NumberVector v1, NumberVector v2)
double	FisherRaoDistance.distance(NumberVector fv1, NumberVector fv2)
double	HellingerDistance.distance(NumberVector fv1, NumberVector fv2)
double	JeffreyDivergenceDistance.distance(NumberVector v1, NumberVector v2)
double	JensenShannonDivergenceDistance.distance(NumberVector v1, NumberVector v2)
double	KullbackLeiblerDivergenceAsymmetricDistance.distance(NumberVector v1, NumberVector v2)
double	KullbackLeiblerDivergenceReverseAsymmetricDistance.distance(NumberVector v1, NumberVector v2)
double	SqrtJensenShannonDivergenceDistance.distance(NumberVector v1, NumberVector v2)
double	TriangularDiscriminationDistance.distance(NumberVector v1, NumberVector v2)
double	TriangularDistance.distance(NumberVector v1, NumberVector v2)
double	HellingerDistance.similarity(NumberVector o1, NumberVector o2)

Uses of NumberVector in elki.distance.set

Methods in elki.distance.set with parameters of type NumberVector

Modifier and Type	Method	Description
double	HammingDistance.distance(NumberVector o1, NumberVector o2)
double	JaccardSimilarityDistance.distance(NumberVector o1, NumberVector o2)
private double	HammingDistance.hammingDistanceNumberVector(NumberVector o1, NumberVector o2)	Version for number vectors.
static double	JaccardSimilarityDistance.similarityNumberVector(NumberVector o1, NumberVector o2)	Compute Jaccard similarity for two number vectors.

Uses of NumberVector in elki.distance.subspace

Methods in elki.distance.subspace with type parameters of type NumberVector

Modifier and Type	Method	Description
<T extends NumberVector> SpatialPrimitiveDistanceQuery<T>	SubspaceLPNormDistance.instantiate(Relation<T> database)

Methods in elki.distance.subspace that return types with arguments of type NumberVector

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

Methods in elki.distance.subspace with parameters of type NumberVector

Modifier and Type	Method	Description
double	OnedimensionalDistance.distance(NumberVector v1, NumberVector v2)
double	SubspaceEuclideanDistance.distance(NumberVector v1, NumberVector v2)	Constructor.
double	SubspaceLPNormDistance.distance(NumberVector v1, NumberVector v2)
double	SubspaceManhattanDistance.distance(NumberVector v1, NumberVector v2)
double	SubspaceMaximumDistance.distance(NumberVector v1, NumberVector v2)
protected double	SubspaceEuclideanDistance.minDistObject(SpatialComparable mbr, NumberVector v)
protected double	SubspaceLPNormDistance.minDistObject(SpatialComparable mbr, NumberVector v)
protected double	SubspaceManhattanDistance.minDistObject(SpatialComparable mbr, NumberVector v)
protected double	SubspaceMaximumDistance.minDistObject(SpatialComparable mbr, NumberVector v)
double	OnedimensionalDistance.norm(NumberVector obj)
double	SubspaceEuclideanDistance.norm(NumberVector obj)
double	SubspaceLPNormDistance.norm(NumberVector obj)
double	SubspaceManhattanDistance.norm(NumberVector obj)
double	SubspaceMaximumDistance.norm(NumberVector obj)

Uses of NumberVector in elki.distance.timeseries

Methods in elki.distance.timeseries that return types with arguments of type NumberVector

Modifier and Type	Method	Description
VectorTypeInformation<? super NumberVector>	AbstractEditDistance.getInputTypeRestriction()
VectorTypeInformation<? super NumberVector>	LCSSDistance.getInputTypeRestriction()

Methods in elki.distance.timeseries with parameters of type NumberVector

Modifier and Type	Method	Description
protected double	DerivativeDTWDistance.derivative(int i, NumberVector v)	Given a NumberVector and the position of an element, approximates the gradient of given element.
double	DerivativeDTWDistance.distance(NumberVector v1, NumberVector v2)
double	DTWDistance.distance(NumberVector v1, NumberVector v2)
double	EDRDistance.distance(NumberVector v1, NumberVector v2)
double	ERPDistance.distance(NumberVector v1, NumberVector v2)
double	LCSSDistance.distance(NumberVector v1, NumberVector v2)
protected void	DerivativeDTWDistance.firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2)
protected void	DTWDistance.firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2)	Fill the first row.
protected void	ERPDistance.firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2)
double	LCSSDistance.getRange(NumberVector v1, int dim1, NumberVector v2, int dim2)

Uses of NumberVector in elki.evaluation.clustering.internal

Fields in elki.evaluation.clustering.internal with type parameters of type NumberVector

Modifier and Type	Field	Description
private PrimitiveDistance<? super NumberVector>	ConcordantPairsGammaTau.distance	Distance function to use.
private PrimitiveDistance<NumberVector>	ConcordantPairsGammaTau.Par.distance	Distance function to use.

Methods in elki.evaluation.clustering.internal with parameters of type NumberVector

Modifier and Type	Method	Description
static int	SimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Compute centroids.
static int	VarianceRatioCriterion.globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Update the global centroid.
double[]	DaviesBouldinIndex.withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)

Method parameters in elki.evaluation.clustering.internal with type arguments of type NumberVector

Modifier and Type	Method	Description
static int	SimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Compute centroids.
protected double[]	ConcordantPairsGammaTau.computeWithinDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, int withinPairs)
double	ClusterRadius.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	ConcordantPairsGammaTau.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	DaviesBouldinIndex.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	PBMIndex.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	SimplifiedSilhouette.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	SquaredErrors.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
double	VarianceRatioCriterion.evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
static int	VarianceRatioCriterion.globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Update the global centroid.
double[]	DaviesBouldinIndex.withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)

Constructor parameters in elki.evaluation.clustering.internal with type arguments of type NumberVector

Constructor	Description
ConcordantPairsGammaTau(PrimitiveDistance<? super NumberVector> distance, NoiseHandling noiseHandling)	Constructor.

Uses of NumberVector in elki.evaluation.scores.adapter

Fields in elki.evaluation.scores.adapter declared as NumberVector

Modifier and Type	Field	Description
protected NumberVector	AbstractVectorIter.positive	Vector of positive examples.
protected NumberVector	AbstractVectorIter.vec	Data vector.

Constructors in elki.evaluation.scores.adapter with parameters of type NumberVector

Constructor	Description
AbstractVectorIter(NumberVector positive, NumberVector vec)	Constructor.
DecreasingVectorIter(NumberVector positive, NumberVector vec)	Constructor.
IncreasingVectorIter(NumberVector positive, NumberVector vec)	Constructor.

Uses of NumberVector in elki.index.invertedlist

Classes in elki.index.invertedlist with type parameters of type NumberVector

Modifier and Type	Class	Description
class	InMemoryInvertedIndex<V extends NumberVector>	Simple index using inverted lists, for cosine distance only.
static class	InMemoryInvertedIndex.Factory<V extends NumberVector>	Index factory
static class	InMemoryInvertedIndex.Factory.Par<V extends NumberVector>	Parameterizer for inverted list index.

Methods in elki.index.invertedlist with parameters of type NumberVector

Modifier and Type	Method	Description
private double	InMemoryInvertedIndex.naiveQueryDense(NumberVector obj, WritableDoubleDataStore scores, HashSetModifiableDBIDs cands)	Query the most similar objects, dense version.

Uses of NumberVector in elki.index.lsh.hashfamilies

Methods in elki.index.lsh.hashfamilies that return types with arguments of type NumberVector

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

Method parameters in elki.index.lsh.hashfamilies with type arguments of type NumberVector

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

Uses of NumberVector in elki.index.lsh.hashfunctions

Methods in elki.index.lsh.hashfunctions with parameters of type NumberVector

Modifier and Type	Method	Description
int	CosineLocalitySensitiveHashFunction.hashObject(NumberVector obj)
int	CosineLocalitySensitiveHashFunction.hashObject(NumberVector obj, double[] buf)
int	MultipleProjectionsLocalitySensitiveHashFunction.hashObject(NumberVector vec)
int	MultipleProjectionsLocalitySensitiveHashFunction.hashObject(NumberVector vec, double[] buf)

Uses of NumberVector in elki.index.preprocessed.fastoptics

Fields in elki.index.preprocessed.fastoptics with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) Relation<? extends NumberVector>	RandomProjectedNeighborsAndDensities.points	entire point set

Method parameters in elki.index.preprocessed.fastoptics with type arguments of type NumberVector

Modifier and Type	Method	Description
void	RandomProjectedNeighborsAndDensities.computeSetsBounds(Relation<? extends NumberVector> points, int minSplitSize, DBIDs ptList)	Create random projections, project points and put points into sets of size about minSplitSize/2

Uses of NumberVector in elki.index.preprocessed.knn

Classes in elki.index.preprocessed.knn with type parameters of type NumberVector

Modifier and Type	Class	Description
class	MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,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,N extends Node<E>,E extends MTreeEntry>	The parameterizable factory.
static class	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.Par<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>	Parameterization class.
class	NaiveProjectedKNNPreprocessor<O extends NumberVector>	Compute the approximate k nearest neighbors using 1 dimensional projections.
static class	NaiveProjectedKNNPreprocessor.Factory<V extends NumberVector>	Index factory class
class	SpacefillingKNNPreprocessor<O extends NumberVector>	Compute the nearest neighbors approximatively using space filling curves.
static class	SpacefillingKNNPreprocessor.Factory<V extends NumberVector>	Index factory class
class	SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>	Compute the nearest neighbors approximatively using space filling curves.
static class	SpacefillingMaterializeKNNPreprocessor.Factory<V extends NumberVector>	Index factory class
static class	SpacefillingMaterializeKNNPreprocessor.Factory.Par<V extends NumberVector>	Parameterization class.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector>	A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.

Fields in elki.index.preprocessed.knn with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) java.util.List<java.util.List<SpatialPair<DBID,NumberVector>>>	SpacefillingKNNPreprocessor.curves	Curve storage

Methods in elki.index.preprocessed.knn that return types with arguments of type NumberVector

Modifier and Type	Method	Description
SpatialApproximationMaterializeKNNPreprocessor<NumberVector>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation)

Method parameters in elki.index.preprocessed.knn with type arguments of type NumberVector

Modifier and Type	Method	Description
SpatialApproximationMaterializeKNNPreprocessor<NumberVector>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector> relation)

Constructor parameters in elki.index.preprocessed.knn with type arguments of type NumberVector

Constructor	Description
Factory(int k, Distance<? super NumberVector> distance)	Constructor.

Uses of NumberVector in elki.index.projected

Classes in elki.index.projected with type parameters of type NumberVector

Modifier and Type	Class	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.Par<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.Par<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.Par<O extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.index.tree.betula

Methods in elki.index.tree.betula with parameters of type NumberVector

Modifier and Type	Method	Description
L	CFTree.findLeaf(NumberVector nv)	Find the leaf of a cluster, to get the final cluster assignment.
private L	CFTree.findLeaf(CFNode<L> node, NumberVector nv)	Find the leaf of a cluster, to get the final cluster assignment.
void	CFTree.insert(NumberVector nv, DBIDRef dbid)	Insert a data point into the tree.
private CFNode<L>	CFTree.insert(CFNode<L> node, NumberVector nv, DBIDRef dbid)	Recursive insertion.
private double	CFTree.sqabsorption(NumberVector nv, ClusterFeature cf)	Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
private double	CFTree.sqdistance(NumberVector nv, ClusterFeature cf)	Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.

Method parameters in elki.index.tree.betula with type arguments of type NumberVector

Modifier and Type	Method	Description
CFTree<L>	CFTree.Factory.newTree(DBIDs ids, Relation<? extends NumberVector> relation, boolean storeIds)	Make a new tree.

Uses of NumberVector in elki.index.tree.betula.distance

Methods in elki.index.tree.betula.distance with parameters of type NumberVector

Modifier and Type	Method	Description
double	AverageInterclusterDistance.squaredDistance(NumberVector nv, ClusterFeature cf)
double	AverageIntraclusterDistance.squaredDistance(NumberVector nv, ClusterFeature cf1)
double	BIRCHAverageInterclusterDistance.squaredDistance(NumberVector v, ClusterFeature ocf)
double	BIRCHAverageIntraclusterDistance.squaredDistance(NumberVector v, ClusterFeature ocf)
double	BIRCHRadiusDistance.squaredDistance(NumberVector n, ClusterFeature ocf)
double	BIRCHVarianceIncreaseDistance.squaredDistance(NumberVector v, ClusterFeature ocf)
double	CentroidEuclideanDistance.squaredDistance(NumberVector v, ClusterFeature cf)
double	CentroidManhattanDistance.squaredDistance(NumberVector v, ClusterFeature cf)
double	CFDistance.squaredDistance(NumberVector v, ClusterFeature cf)	Distance of a vector to a clustering feature.
double	RadiusDistance.squaredDistance(NumberVector nv, ClusterFeature cf1)
double	VarianceIncreaseDistance.squaredDistance(NumberVector nv, ClusterFeature cf)

Uses of NumberVector in elki.index.tree.betula.features

Subinterfaces of NumberVector in elki.index.tree.betula.features

Modifier and Type	Interface	Description
interface	ClusterFeature	Interface for basic ClusteringFeature functions

Classes in elki.index.tree.betula.features that implement NumberVector

Modifier and Type	Class	Description
class	BIRCHCF	Clustering Feature of BIRCH, only for comparison
class	VIIFeature	Clustering Feature of stable BIRCH with a single variance per cluster feature
class	VVIFeature	Clustering Feature of stable BIRCH with variance per dimension
class	VVVFeature	Clustering Feature of stable BIRCH with covariance instead of variance

Methods in elki.index.tree.betula.features with parameters of type NumberVector

Modifier and Type	Method	Description
void	BIRCHCF.addToStatistics(NumberVector nv)
void	ClusterFeature.addToStatistics(NumberVector nv)	Add NumberVector to CF
void	VIIFeature.addToStatistics(NumberVector nv)
void	VVIFeature.addToStatistics(NumberVector nv)
void	VVVFeature.addToStatistics(NumberVector nv)
static double	BIRCHCF.sumOfSquares(NumberVector v)	Compute the sum of squares of a vector.

Uses of NumberVector in elki.index.tree.metrical.vptree

Classes in elki.index.tree.metrical.vptree with type parameters of type NumberVector

Modifier and Type	Class	Description
static class	GNAT.Factory<O extends NumberVector>	Index Factory
static class	GNAT.Factory.Par<O extends NumberVector>	Parameterization class.
static class	VPTree.Factory<O extends NumberVector>	Index factory for the VP-Tree
static class	VPTree.Factory.Par<O extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.index.tree.spatial

Classes in elki.index.tree.spatial that implement NumberVector

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

Constructors in elki.index.tree.spatial with parameters of type NumberVector

Constructor	Description
SpatialPointLeafEntry(DBID id, NumberVector vector)	Constructor from number vector.

Uses of NumberVector in elki.index.tree.spatial.kd

Classes in elki.index.tree.spatial.kd with type parameters of type NumberVector

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

Fields in elki.index.tree.spatial.kd declared as NumberVector

Modifier and Type	Field	Description
private O	MemoryKDTree.KDTreePrioritySearcher.query	Current query object.
private O	MinimalisticMemoryKDTree.KDTreePrioritySearcher.query	Current query object.
private O	SmallMemoryKDTree.KDTreePrioritySearcher.query	Current query object.

Methods in elki.index.tree.spatial.kd with parameters of type NumberVector

Modifier and Type	Method	Description
double	PartialEuclideanDistance.distance(NumberVector a, NumberVector b)
double	PartialLPNormDistance.distance(NumberVector a, NumberVector b)
double	PartialManhattanDistance.distance(NumberVector a, NumberVector b)
double	PartialSquaredEuclideanDistance.distance(NumberVector a, NumberVector b)

Method parameters in elki.index.tree.spatial.kd with type arguments of type NumberVector

Modifier and Type	Method	Description
java.lang.Object	MemoryKDTree.buildTree(Relation<? extends NumberVector> relation, int left, int right, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d tree.

Constructor parameters in elki.index.tree.spatial.kd with type arguments of type NumberVector

Constructor	Description
CountSortAccesses(Counter objaccess, Relation<? extends NumberVector> data)	Constructor.

Uses of NumberVector in elki.index.tree.spatial.kd.split

Method parameters in elki.index.tree.spatial.kd.split with type arguments of type NumberVector

Modifier and Type	Method	Description
SplitStrategy.Info	BoundedMidpointSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	LeastOneDimSSQSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	LeastSSQSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MeanVarianceSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MedianSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MedianVarianceSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	MidpointSplit.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info	SplitStrategy.findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)	Build the k-d-tree using midpoint splitting.
(package private) static double[]	SplitStrategy.Util.minmaxRange(int dims, Relation<? extends NumberVector> relation, DBIDArrayIter iter, int left, int right)	Find the minimum and maximum in each dimension of a range of values.
(package private) static int	SplitStrategy.Util.pivot(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int dim, int left, int right, double mid)	Pivot an interval.
(package private) static double[]	SplitStrategy.Util.sumvar(Relation<? extends NumberVector> relation, int dims, DBIDArrayMIter iter, int left, int right)	Compute the sum and sum-of-squares (for variance).

Uses of NumberVector in elki.index.tree.spatial.rstarvariants

Classes in elki.index.tree.spatial.rstarvariants with type parameters of type NumberVector

Modifier and Type	Class	Description
class	AbstractRStarTreeFactory<O extends NumberVector,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,S extends RTreeSettings>	Abstract factory for R*-Tree based trees.
static class	AbstractRStarTreeFactory.Par<O extends NumberVector,S extends RTreeSettings>	Parameterization class.

Uses of NumberVector in elki.index.tree.spatial.rstarvariants.deliclu

Classes in elki.index.tree.spatial.rstarvariants.deliclu with type parameters of type NumberVector

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

Classes in elki.index.tree.spatial.rstarvariants.deliclu that implement NumberVector

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

Constructors in elki.index.tree.spatial.rstarvariants.deliclu with parameters of type NumberVector

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

Uses of NumberVector in elki.index.tree.spatial.rstarvariants.flat

Classes in elki.index.tree.spatial.rstarvariants.flat with type parameters of type NumberVector

Modifier and Type	Class	Description
class	FlatRStarTreeFactory<O extends NumberVector>	Factory for flat R*-Trees.
static class	FlatRStarTreeFactory.Par<O extends NumberVector>	Parameterization class.
class	FlatRStarTreeIndex<O extends NumberVector>	The common use of the flat rstar tree: indexing number vectors.

Uses of NumberVector in elki.index.tree.spatial.rstarvariants.query

Classes in elki.index.tree.spatial.rstarvariants.query with type parameters of type NumberVector

Modifier and Type	Class	Description
class	EuclideanRStarTreeKNNQuery<O extends NumberVector>	Instance of a KNN query for a particular spatial index.
class	EuclideanRStarTreeRangeQuery<O extends NumberVector>	Instance of a range query for a particular spatial index.

Uses of NumberVector in elki.index.tree.spatial.rstarvariants.rdknn

Classes in elki.index.tree.spatial.rstarvariants.rdknn with type parameters of type NumberVector

Modifier and Type	Class	Description
class	RdKNNTree<O extends NumberVector>	RDkNNTree is a spatial index structure based on the concepts of the R*-Tree supporting efficient processing of reverse k nearest neighbor queries.
class	RdKNNTreeFactory<O extends NumberVector>	Factory for RdKNN R*-Trees.
static class	RdKNNTreeFactory.Par<O extends NumberVector>	Parameterization class.

Classes in elki.index.tree.spatial.rstarvariants.rdknn that implement NumberVector

Modifier and Type	Class	Description
class	RdKNNLeafEntry	Represents an entry in a leaf node of an RdKNN-Tree.

Fields in elki.index.tree.spatial.rstarvariants.rdknn with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) SpatialPrimitiveDistance<NumberVector>	RdkNNSettings.distance	The distance function.

Constructors in elki.index.tree.spatial.rstarvariants.rdknn with parameters of type NumberVector

Constructor	Description
RdKNNLeafEntry(DBID id, NumberVector vector, double knnDistance)	Constructs a new RDkNNLeafEntry object with the given parameters.

Constructor parameters in elki.index.tree.spatial.rstarvariants.rdknn with type arguments of type NumberVector

Constructor	Description
RdkNNSettings(int k_max, SpatialPrimitiveDistance<NumberVector> distance)	Constructor.

Uses of NumberVector in elki.index.tree.spatial.rstarvariants.rstar

Classes in elki.index.tree.spatial.rstarvariants.rstar with type parameters of type NumberVector

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

Uses of NumberVector in elki.index.vafile

Classes in elki.index.vafile with type parameters of type NumberVector

Modifier and Type	Class	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)
static class	VAFile.Factory<V extends NumberVector>	Index factory class.

Methods in elki.index.vafile with parameters of type NumberVector

Modifier and Type	Method	Description
protected static VectorApproximation	PartialVAFile.calculatePartialApproximation(NumberVector dv, java.util.List<DoubleObjPair<DAFile>> daFiles)	Calculate partial vector approximation.
protected static void	PartialVAFile.calculateSelectivityCoeffs(java.util.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.

Constructors in elki.index.vafile with parameters of type NumberVector

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

Constructor parameters in elki.index.vafile with type arguments of type NumberVector

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

Uses of NumberVector in elki.math

Methods in elki.math with parameters of type NumberVector

Modifier and Type	Method	Description
static double	PearsonCorrelation.coefficient(NumberVector x, NumberVector y)	Compute the Pearson product-moment correlation coefficient for two NumberVectors.
static double	PearsonCorrelation.weightedCoefficient(NumberVector x, NumberVector y, double[] weights)	Compute the Pearson product-moment correlation coefficient for two NumberVectors.
static double	PearsonCorrelation.weightedCoefficient(NumberVector x, NumberVector y, NumberVector weights)	Compute the Pearson product-moment correlation coefficient,

Method parameters in elki.math with type arguments of type NumberVector

Modifier and Type	Method	Description
static MeanVariance[]	MeanVariance.of(Relation<? extends NumberVector> relation)	Compute the variances of a relation.

Uses of NumberVector in elki.math.linearalgebra

Classes in elki.math.linearalgebra that implement NumberVector

Modifier and Type	Class	Description
class	Centroid	Class to compute the centroid of some data.
class	ProjectedCentroid	Centroid only using a subset of dimensions.

Methods in elki.math.linearalgebra with type parameters of type NumberVector

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

Methods in elki.math.linearalgebra with parameters of type NumberVector

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

Method parameters in elki.math.linearalgebra with type arguments of type NumberVector

Modifier and Type	Method	Description
static Centroid	Centroid.make(Relation<? extends NumberVector> relation, DBIDs ids)	Static constructor from an existing relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector> relation)	Static Constructor from a full relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector> relation, DBIDs ids)	Static Constructor from a full relation.
static ProjectedCentroid	ProjectedCentroid.make(long[] dims, Relation<? extends NumberVector> relation)	Static Constructor from a relation.
static ProjectedCentroid	ProjectedCentroid.make(long[] dims, Relation<? extends NumberVector> relation, DBIDs ids)	Static Constructor from a relation.

Uses of NumberVector in elki.math.linearalgebra.pca

Fields in elki.math.linearalgebra.pca with type parameters of type NumberVector

Modifier and Type	Field	Description
private PrimitiveDistance<? super NumberVector>	WeightedCovarianceMatrixBuilder.weightDistance	Holds the distance function used for weight calculation.

Method parameters in elki.math.linearalgebra.pca with type arguments of type NumberVector

Modifier and Type	Method	Description
PCAResult	AutotuningPCA.processIds(DBIDs ids, Relation<? extends NumberVector> database)
double[][]	CovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database)	Compute covariance matrix for a collection of database IDs.
PCAResult	PCARunner.processIds(DBIDs ids, Relation<? extends NumberVector> database)	Run PCA on a collection of database IDs.
double[][]	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)
double[][]	StandardCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database)	Compute Covariance Matrix for a collection of database IDs.
double[][]	WeightedCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)	Weighted Covariance Matrix for a set of IDs.
PCAResult	AutotuningPCA.processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)
PCAResult	PCARunner.processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)	Run PCA on a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database)	Compute covariance matrix for a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)	Compute covariance matrix for a QueryResult collection.
double[][]	WeightedCovarianceMatrixBuilder.processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)	Compute Covariance Matrix for a QueryResult Collection.
default double[][]	CovarianceMatrixBuilder.processRelation(Relation<? extends NumberVector> relation)	Compute covariance matrix for a complete relation.

Uses of NumberVector in elki.math.spacefillingcurves

Methods in elki.math.spacefillingcurves with parameters of type NumberVector

Modifier and Type	Method	Description
byte[]	ZCurveTransformer.asByteArray(NumberVector vector)	Transform a single vector.

Constructor parameters in elki.math.spacefillingcurves with type arguments of type NumberVector

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

Uses of NumberVector in elki.math.statistics.intrinsicdimensionality

Method parameters in elki.math.statistics.intrinsicdimensionality with type arguments of type NumberVector

Modifier and Type	Method	Description
protected double	LPCAEstimator.estimate(DBIDs ids, Relation<? extends NumberVector> relation)	Returns an ID estimate based on the specified filter for the given point DBID set and relation.
double	LPCAEstimator.estimate(KNNSearcher<DBIDRef> knnq, DistanceQuery<? extends NumberVector> distq, DBIDRef cur, int k)
double	LPCAEstimator.estimate(RangeSearcher<DBIDRef> rnq, DistanceQuery<? extends NumberVector> distq, DBIDRef cur, double range)

Uses of NumberVector in elki.outlier

Classes in elki.outlier with type parameters of type NumberVector

Modifier and Type	Class	Description
class	COP<V extends NumberVector>	Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces
static class	COP.Par<V extends NumberVector>	Parameterization class.
class	SimpleCOP<V extends NumberVector>	Algorithm to compute local correlation outlier probability.
static class	SimpleCOP.Par<V extends NumberVector>	Parameterization class.

Method parameters in elki.outlier with type arguments of type NumberVector

Modifier and Type	Method	Description
private static void	COP.computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids)	Recompute the centroid of a set.
private double	GaussianUniformMixture.loglikelihoodNormal(DBIDs objids, SetDBIDs anomalous, CovarianceMatrix builder, Relation<? extends NumberVector> relation)	Computes the loglikelihood of all normal objects.
OutlierResult	GaussianModel.run(Relation<? extends NumberVector> relation)	Run the algorithm
OutlierResult	GaussianUniformMixture.run(Relation<? extends NumberVector> relation)	Run the algorithm

Uses of NumberVector in elki.outlier.anglebased

Classes in elki.outlier.anglebased with type parameters of type NumberVector

Modifier and Type	Class	Description
class	ABOD<V extends NumberVector>	Angle-Based Outlier Detection / Angle-Based Outlier Factor.
static class	ABOD.Par<V extends NumberVector>	Parameterization class.
class	FastABOD<V extends NumberVector>	Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
static class	FastABOD.Par<V extends NumberVector>	Parameterization class.
class	LBABOD<V extends NumberVector>	LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
static class	LBABOD.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.outlier.clustering

Classes in elki.outlier.clustering with type parameters of type NumberVector

Modifier and Type	Class	Description
class	CBLOF<O extends NumberVector>	Cluster-based local outlier factor (CBLOF).
static class	CBLOF.Par<O extends NumberVector>	Parameterization class.
class	EMOutlier<V extends NumberVector>	Outlier detection algorithm using EM Clustering.
static class	EMOutlier.Par<V extends NumberVector>	Parameterization class.
class	KMeansOutlierDetection<O extends NumberVector>	Outlier detection by using k-means clustering.
static class	KMeansOutlierDetection.Par<O extends NumberVector>	Parameterizer.

Methods in elki.outlier.clustering with parameters of type NumberVector

Modifier and Type	Method	Description
private double	CBLOF.computeLargeClusterCBLOF(O obj, NumberVectorDistance<? super O> distance, NumberVector clusterMean, Cluster<MeanModel> cluster)

Method parameters in elki.outlier.clustering with type arguments of type NumberVector

Modifier and Type	Method	Description
private double	CBLOF.computeSmallClusterCBLOF(O obj, NumberVectorDistance<? super O> distance, java.util.List<NumberVector> largeClusterMeans, Cluster<MeanModel> cluster)
OutlierResult	DBSCANOutlierDetection.run(Database db, Relation<? extends NumberVector> relation)	Runs the algorithm in the timed evaluation part.
OutlierResult	GLOSH.run(Database db, Relation<? extends NumberVector> relation)

Uses of NumberVector in elki.outlier.density

Fields in elki.outlier.density with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) Relation<? extends NumberVector>	IsolationForest.ForestBuilder.relation	Data relation to use

Methods in elki.outlier.density with parameters of type NumberVector

Modifier and Type	Method	Description
protected double	IsolationForest.isolationScore(IsolationForest.Node n, NumberVector v)	Search a vector in the tree, return depth (path length)

Method parameters in elki.outlier.density with type arguments of type NumberVector

Modifier and Type	Method	Description
private java.util.List<HySortOD.Hypercube>	HySortOD.getSortedHypercubes(Relation<? extends NumberVector> relation)	Create and sort hypercubes considering their coordinates.
OutlierResult	HySortOD.run(Database db, Relation<? extends NumberVector> relation)
OutlierResult	IsolationForest.run(Relation<? extends NumberVector> relation)	Run the isolation forest algorithm.

Constructors in elki.outlier.density with parameters of type NumberVector

Constructor	Description
Hypercube(NumberVector values, double length)	Hypercube constructor.

Constructor parameters in elki.outlier.density with type arguments of type NumberVector

Constructor	Description
ForestBuilder(Relation<? extends NumberVector> relation, int subsampleSize, java.util.Random random)	Constructor for the tree builder.

Uses of NumberVector in elki.outlier.distance

Classes in elki.outlier.distance with type parameters of type NumberVector

Modifier and Type	Class	Description
class	HilOut<O extends NumberVector>	Fast Outlier Detection in High Dimensional Spaces
static class	HilOut.Par<O extends NumberVector>	Parameterization class

Fields in elki.outlier.distance with type parameters of type NumberVector

Modifier and Type	Field	Description
protected NumberVectorDistance<? super NumberVector>	ReferenceBasedOutlierDetection.distance	Distance function used.
protected NumberVectorDistance<? super NumberVector>	ReferenceBasedOutlierDetection.Par.distance	The distance function to use.

Methods in elki.outlier.distance with parameters of type NumberVector

Modifier and Type	Method	Description
protected DoubleDBIDList	ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)	Computes for each object the distance to one reference point.
private double	HilOut.HilbertFeatures.getDimForObject(NumberVector obj, int dim)	Get the (projected) position of the object in dimension dim.

Method parameters in elki.outlier.distance with type arguments of type NumberVector

Modifier and Type	Method	Description
protected DoubleDBIDList	ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)	Computes for each object the distance to one reference point.
protected DoubleDBIDList	ReferenceBasedOutlierDetection.computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)	Computes for each object the distance to one reference point.
OutlierResult	ReferenceBasedOutlierDetection.run(Relation<? extends NumberVector> relation)	Run the algorithm on the given relation.

Constructor parameters in elki.outlier.distance with type arguments of type NumberVector

Constructor	Description
ReferenceBasedOutlierDetection(int k, NumberVectorDistance<? super NumberVector> distance, ReferencePointsHeuristic refp)	Constructor with parameters.

Uses of NumberVector in elki.outlier.lof

Classes in elki.outlier.lof with type parameters of type NumberVector

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

Classes in elki.outlier.lof that implement NumberVector

Modifier and Type	Class	Description
(package private) static class	ALOCI.Node	Node of the ALOCI Quadtree

Fields in elki.outlier.lof with type parameters of type NumberVector

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

Methods in elki.outlier.lof with parameters of type NumberVector

Modifier and Type	Method	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 elki.outlier.lof with type arguments of type NumberVector

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

Uses of NumberVector in elki.outlier.meta

Method parameters in elki.outlier.meta with type arguments of type NumberVector

Modifier and Type	Method	Description
private java.util.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, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random)	Calculates the actual contrast of a given subspace.
private java.util.Set<HiCS.HiCSSubspace>	HiCS.calculateSubspaces(Relation<? extends NumberVector> relation, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random)	Identifies high contrast subspaces in a given full-dimensional database.
OutlierResult	FeatureBagging.run(Relation<NumberVector> relation)	Run the algorithm on a data set.
OutlierResult	HiCS.run(Relation<? extends NumberVector> relation)	Perform HiCS on a given database.

Uses of NumberVector in elki.outlier.spatial

Classes in elki.outlier.spatial with type parameters of type NumberVector

Modifier and Type	Class	Description
class	CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>	GLS-Backward Search is a statistical approach to detecting spatial outliers.
static class	CTLuGLSBackwardSearchAlgorithm.Par<V extends NumberVector>	Parameterization class
class	CTLuMeanMultipleAttributes<N,O extends NumberVector>	Mean Approach is used to discover spatial outliers with multiple attributes.
static class	CTLuMeanMultipleAttributes.Par<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.Par<N,O extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.outlier.subspace

Classes in elki.outlier.subspace with type parameters of type NumberVector

Modifier and Type	Class	Description
class	SOD<V extends NumberVector>	Subspace Outlier Degree: Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data.
static class	SOD.Par<V extends NumberVector>	Parameterization class.

Fields in elki.outlier.subspace with type parameters of type NumberVector

Modifier and Type	Field	Description
(package private) Relation<? extends NumberVector>	OUTRES.KernelDensityEstimator.relation	Relation to retrieve data from

Method parameters in elki.outlier.subspace with type arguments of type NumberVector

Modifier and Type	Method	Description
protected java.util.ArrayList<java.util.ArrayList<DBIDs>>	AbstractAggarwalYuOutlier.buildRanges(Relation<? extends NumberVector> relation)	Grid discretization of the data: Each attribute of data is divided into phi equi-depth ranges. Each range contains a fraction f=1/phi of the records.
private static double[]	SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation, double[] center, DBIDs neighborhood)	Compute the per-dimension variances for the given neighborhood and center.
private DoubleDBIDList	OUTRES.initialRange(DBIDRef obj, DBIDs cands, PrimitiveDistance<? super NumberVector> df, double eps, OUTRES.KernelDensityEstimator kernel, ModifiableDoubleDBIDList n)	Initial range query.
OutlierResult	AggarwalYuEvolutionary.run(Relation<? extends NumberVector> relation)	Performs the evolutionary algorithm on the given database.
OutlierResult	AggarwalYuNaive.run(Relation<? extends NumberVector> relation)	Run the algorithm on the given relation.
OutlierResult	OUTRES.run(Relation<? extends NumberVector> relation)	Main loop for OUTRES
private DoubleDBIDList	OUTRES.subsetNeighborhoodQuery(DoubleDBIDList neighc, DBIDRef dbid, PrimitiveDistance<? super NumberVector> df, double adjustedEps, OUTRES.KernelDensityEstimator kernel, ModifiableDoubleDBIDList n)	Refine neighbors within a subset.

Constructor parameters in elki.outlier.subspace with type arguments of type NumberVector

Constructor	Description
EvolutionarySearch(Relation<? extends NumberVector> relation, java.util.ArrayList<java.util.ArrayList<DBIDs>> ranges, java.util.Random random)	Constructor.
KernelDensityEstimator(Relation<? extends NumberVector> relation, double eps)	Constructor.

Uses of NumberVector in elki.outlier.svm

Classes in elki.outlier.svm with type parameters of type NumberVector

Modifier and Type	Class	Description
class	LibSVMOneClassOutlierDetection<V extends NumberVector>	Outlier-detection using one-class support vector machines.
static class	LibSVMOneClassOutlierDetection.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in elki.outlier.trivial

Method parameters in elki.outlier.trivial with type arguments of type NumberVector

Modifier and Type	Method	Description
OutlierResult	TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector> relation)	Run the actual algorithm.

Uses of NumberVector in elki.result

Method parameters in elki.result with type arguments of type NumberVector

Modifier and Type	Method	Description
private DoubleObjPair<Polygon>	KMLOutputHandler.buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords)	Recursively step through the clusters to build the hulls.

Uses of NumberVector in elki.similarity

Methods in elki.similarity with type parameters of type NumberVector

Modifier and Type	Method	Description
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	Kulczynski1Similarity.instantiate(Relation<T> database)

Methods in elki.similarity that return types with arguments of type NumberVector

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

Methods in elki.similarity with parameters of type NumberVector

Modifier and Type	Method	Description
double	Kulczynski1Similarity.distance(NumberVector v1, NumberVector v2)
double	Kulczynski1Similarity.similarity(NumberVector v1, NumberVector v2)
double	Kulczynski2Similarity.similarity(NumberVector v1, NumberVector v2)

Uses of NumberVector in elki.similarity.kernel

Methods in elki.similarity.kernel with type parameters of type NumberVector

Modifier and Type	Method	Description
<T extends NumberVector> DistanceSimilarityQuery<T>	PolynomialKernel.instantiate(Relation<T> database)

Methods in elki.similarity.kernel with parameters of type NumberVector

Modifier and Type	Method	Description
double	LinearKernel.distance(NumberVector fv1, NumberVector fv2)
double	PolynomialKernel.distance(NumberVector fv1, NumberVector fv2)
double	LaplaceKernel.similarity(NumberVector o1, NumberVector o2)
double	LinearKernel.similarity(NumberVector o1, NumberVector o2)
double	PolynomialKernel.similarity(NumberVector o1, NumberVector o2)
double	RadialBasisFunctionKernel.similarity(NumberVector o1, NumberVector o2)
double	RationalQuadraticKernel.similarity(NumberVector o1, NumberVector o2)
double	SigmoidKernel.similarity(NumberVector o1, NumberVector o2)

Uses of NumberVector in elki.timeseries

Methods in elki.timeseries with parameters of type NumberVector

Modifier and Type	Method	Description
private double	SigniTrendChangeDetection.Instance.processRow(DBIDRef iter, NumberVector row, ChangePoints changepoints)	Process one row, assuming a constant time interval.

Method parameters in elki.timeseries with type arguments of type NumberVector

Modifier and Type	Method	Description
ChangePoints	SigniTrendChangeDetection.Instance.run(Relation<NumberVector> relation)	Process a relation.
ChangePoints	SigniTrendChangeDetection.run(Relation<NumberVector> relation)	Executes Signi-Trend for given relation

Uses of NumberVector in elki.utilities.datastructures.arraylike

Methods in elki.utilities.datastructures.arraylike with parameters of type NumberVector

Modifier and Type	Method	Description
java.lang.Number	NumberVectorAdapter.get(NumberVector array, int off)	Deprecated.
byte	NumberVectorAdapter.getByte(NumberVector array, int off)
double	NumberVectorAdapter.getDouble(NumberVector array, int off)
float	NumberVectorAdapter.getFloat(NumberVector array, int off)
int	NumberVectorAdapter.getInteger(NumberVector array, int off)
long	NumberVectorAdapter.getLong(NumberVector array, int off)
short	NumberVectorAdapter.getShort(NumberVector array, int off)
int	NumberVectorAdapter.size(NumberVector array)
static double[]	ArrayLikeUtil.toPrimitiveDoubleArray(NumberVector obj)	Convert a number vector to double[].
static float[]	ArrayLikeUtil.toPrimitiveFloatArray(NumberVector obj)	Convert a number vector to float[].
static int[]	ArrayLikeUtil.toPrimitiveIntegerArray(NumberVector obj)	Convert a number vector to int[].

Uses of NumberVector in elki.utilities.referencepoints

Methods in elki.utilities.referencepoints that return types with arguments of type NumberVector

Modifier and Type	Method	Description
java.util.Collection<? extends NumberVector>	AxisBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	FullDatabaseReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	GridBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomGeneratedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomSampleReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	ReferencePointsHeuristic.getReferencePoints(Relation<? extends NumberVector> db)	Get the reference points for the given database.
java.util.Collection<? extends NumberVector>	StarBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)

Method parameters in elki.utilities.referencepoints with type arguments of type NumberVector

Modifier and Type	Method	Description
java.util.Collection<? extends NumberVector>	AxisBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	FullDatabaseReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	GridBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomGeneratedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	RandomSampleReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>	ReferencePointsHeuristic.getReferencePoints(Relation<? extends NumberVector> db)	Get the reference points for the given database.
java.util.Collection<? extends NumberVector>	StarBasedReferencePoints.getReferencePoints(Relation<? extends NumberVector> db)

Uses of NumberVector in elki.visualization.parallel3d

Classes in elki.visualization.parallel3d with type parameters of type NumberVector

Modifier and Type	Class	Description
class	OpenGL3DParallelCoordinates<O extends NumberVector>	Simple JOGL2 based parallel coordinates visualization.
static class	OpenGL3DParallelCoordinates.Instance<O extends NumberVector>	Visualizer instance.
static class	OpenGL3DParallelCoordinates.Par<O extends NumberVector>	Parameterization class.
class	Parallel3DRenderer<O extends NumberVector>	Renderer for 3D parallel plots.

Uses of NumberVector in elki.visualization.parallel3d.layout

Method parameters in elki.visualization.parallel3d.layout with type arguments of type NumberVector

Modifier and Type	Method	Description
static double[]	AbstractLayout3DPC.computeSimilarityMatrix(Dependence sim, Relation<? extends NumberVector> rel)	Compute a column-wise dependency matrix for the given relation.
Layout	AbstractLayout3DPC.layout(Relation<? extends NumberVector> rel)

Uses of NumberVector in elki.visualization.projections

Methods in elki.visualization.projections with type parameters of type NumberVector

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

Methods in elki.visualization.projections with parameters of type NumberVector

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

Uses of NumberVector in elki.visualization.projector

Classes in elki.visualization.projector with type parameters of type NumberVector

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

Uses of NumberVector in elki.visualization.svg

Methods in elki.visualization.svg with parameters of type NumberVector

Modifier and Type	Method	Description
static org.w3c.dom.Element	SVGHyperSphere.drawCross(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius)	Wireframe "cross" hypersphere
static org.w3c.dom.Element	SVGHyperSphere.drawEuclidean(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius)	Wireframe "euclidean" hypersphere
static org.w3c.dom.Element	SVGHyperCube.drawFilled(SVGPlot svgp, java.lang.String cls, Projection2D proj, NumberVector min, NumberVector max)	Filled hypercube.
static org.w3c.dom.Element	SVGHyperCube.drawFrame(SVGPlot svgp, Projection2D proj, NumberVector min, NumberVector max)	Wireframe hypercube.
static org.w3c.dom.Element	SVGHyperSphere.drawLp(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius, double p)	Wireframe "Lp" hypersphere
static org.w3c.dom.Element	SVGHyperSphere.drawManhattan(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius)	Wireframe "manhattan" hypersphere
private static java.util.ArrayList<double[]>	SVGHyperCube.getVisibleEdges(Projection2D proj, NumberVector s_min, NumberVector s_max)	Get the visible (non-0) edges of a hypercube

Uses of NumberVector in elki.visualization.visualizers.histogram

Classes in elki.visualization.visualizers.histogram with type parameters of type NumberVector

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

Uses of NumberVector in elki.visualization.visualizers.scatterplot

Fields in elki.visualization.visualizers.scatterplot with type parameters of type NumberVector

Modifier and Type	Field	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 elki.visualization.visualizers.scatterplot.selection

Fields in elki.visualization.visualizers.scatterplot.selection with type parameters of type NumberVector

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

Methods in elki.visualization.visualizers.scatterplot.selection with parameters of type NumberVector

Modifier and Type	Method	Description
static org.w3c.dom.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	Description
class	SameSizeKMeans<V extends NumberVector>	K-means variation that produces equally sized clusters.
static class	SameSizeKMeans.Par<V extends NumberVector>	Parameterization class.

Uses of NumberVector in tutorial.distancefunction

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

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

Methods in tutorial.distancefunction with parameters of type NumberVector

Modifier and Type	Method	Description
double	MultiLPNorm.distance(NumberVector o1, NumberVector o2)
double	TutorialDistance.distance(NumberVector o1, NumberVector o2)