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

Packages that use NumberVector

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.benchmark	Benchmarking pseudo algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Biclustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch	BIRCH clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional	Clustering algorithms for one-dimensional data.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique	Helper classes for the CLIQUE algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased	Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods Methods that detect outliers in subspaces (projections) of the data set.
de.lmu.ifi.dbs.elki.algorithm.outlier.svm	Support-Vector-Machines for outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms.
de.lmu.ifi.dbs.elki.algorithm.timeseries	Algorithms for change point detection in time series.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms
de.lmu.ifi.dbs.elki.data.projection	Data projections
de.lmu.ifi.dbs.elki.data.projection.random	Random projection families
de.lmu.ifi.dbs.elki.data.type	Data type information, also used for type restrictions
de.lmu.ifi.dbs.elki.data.uncertain	Uncertain data objects.
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries
de.lmu.ifi.dbs.elki.database.query.range	Prepared queries for ε-range queries, that return all objects within the radius ε
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views)
de.lmu.ifi.dbs.elki.datasource.filter	Data filtering, in particular for normalization and projection
de.lmu.ifi.dbs.elki.datasource.filter.cleaning	Filters for data cleaning.
de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise	Normalizations operating on columns / variates; where each column is treated independently.
de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise	Instancewise normalization, where each instance is normalized independently.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations
de.lmu.ifi.dbs.elki.datasource.filter.typeconversions	Filters to perform data type conversions.
de.lmu.ifi.dbs.elki.datasource.parser	Parsers for different file formats and data types The general use-case for any parser is to create objects out of an InputStream (e.g. by reading a data file).
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram	Distance functions using correlations
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation	Distance functions using correlations
de.lmu.ifi.dbs.elki.distance.distancefunction.geo	Geographic (earth) distance functions
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram	Distance functions for one-dimensional histograms.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski	Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
de.lmu.ifi.dbs.elki.distance.distancefunction.set	Distance functions for binary and set type data.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries	Distance functions designed for time series Note that some regular distance functions (e.g., Euclidean) are also used on time series.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.scores.adapter	Adapter classes for ranking and scoring measures.
de.lmu.ifi.dbs.elki.index.invertedlist	Indexes using inverted lists.
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions	Hash functions for LSH
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA
de.lmu.ifi.dbs.elki.index.preprocessed.preference	Indexes storing preference vectors
de.lmu.ifi.dbs.elki.index.projected	Projected indexes for data
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing
de.lmu.ifi.dbs.elki.index.tree.spatial.kd	K-d-tree and variants
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	R*-Tree and variants
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat	FlatRStarTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework
de.lmu.ifi.dbs.elki.math.linearalgebra	The linear algebra package provides classes and computational methods for operations on matrices and vectors.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike	Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
de.lmu.ifi.dbs.elki.utilities.referencepoints	Package containing strategies to obtain reference points Shared code for various algorithms that use reference points
de.lmu.ifi.dbs.elki.visualization.parallel3d	3DPC: 3D parallel coordinate plot visualization for ELKI.
de.lmu.ifi.dbs.elki.visualization.parallel3d.layout	Layouting algorithms for 3D parallel coordinate plots.
de.lmu.ifi.dbs.elki.visualization.projections	Visualization projections
de.lmu.ifi.dbs.elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations
de.lmu.ifi.dbs.elki.visualization.svg	Base SVG functionality (generation, markers, thumbnails, export, ...)
de.lmu.ifi.dbs.elki.visualization.visualizers.histogram	Visualizers based on 1D projected histograms
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot	Visualizers based on scatterplots
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection	Visualizers for object selection based on 2D projections
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation
tutorial.distancefunction	Classes from the tutorial on implementing distance functions

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

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

Modifier and Type	Class and Description
class	DependencyDerivator<V extends NumberVector> Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
static class	DependencyDerivator.Parameterizer<V extends NumberVector> Parameterization class.
class	DummyAlgorithm<O extends NumberVector> Dummy algorithm, which just iterates over all points once, doing a 10NN query each.
class	KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> Joins in a given spatial database to each object its k-nearest neighbors.
static class	KNNJoin.Parameterizer<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> Parameterization class.

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	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.Parameterizer<O extends NumberVector> Parameterization class.
class	NaiveMeanShiftClustering<V extends NumberVector> Mean-shift based clustering algorithm.
static class	NaiveMeanShiftClustering.Parameterizer<V extends NumberVector> Parameterizer.

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

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

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

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

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

Modifier and Type	Class and Description
class	CASH<V extends NumberVector> The CASH algorithm is a subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector> COPAC is an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.
static class	COPAC.Parameterizer<V extends NumberVector> Parameterization class.
class	ERiC<V extends NumberVector> Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.
static class	ERiC.Parameterizer<V extends NumberVector> Parameterization class.
class	FourC<V extends NumberVector> 4C identifies local subgroups of data objects sharing a uniform correlation.
static class	FourC.Parameterizer<O extends NumberVector> Parameterization class.
class	HiCO<V extends NumberVector> Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
static class	HiCO.Parameterizer<V extends NumberVector> Parameterization class.
class	ORCLUS<V extends NumberVector> ORCLUS: Arbitrarily ORiented projected CLUSter generation.
static class	ORCLUS.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	AbstractEMModelFactory<V extends NumberVector,M extends MeanModel> Abstract base class for initializing EM.
static class	AbstractEMModelFactory.Parameterizer<V extends NumberVector> Parameterization class.
class	DiagonalGaussianModelFactory<V extends NumberVector> Factory for EM with multivariate gaussian models using diagonal matrixes.
static class	DiagonalGaussianModelFactory.Parameterizer<V extends NumberVector> Parameterization class
class	EM<V extends NumberVector,M extends MeanModel> Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
static class	EM.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.
interface	EMClusterModelFactory<V extends NumberVector,M extends MeanModel> Factory for initializing the EM models.
class	MultivariateGaussianModelFactory<V extends NumberVector> Factory for EM with multivariate Gaussian models (with covariance; also known as Gaussian Mixture Modeling, GMM).
static class	MultivariateGaussianModelFactory.Parameterizer<V extends NumberVector> Parameterization class
class	SphericalGaussianModelFactory<V extends NumberVector> Factory for EM with multivariate gaussian models using a single variance.
static class	SphericalGaussianModelFactory.Parameterizer<V extends NumberVector> Parameterization class
class	TextbookMultivariateGaussianModelFactory<V extends NumberVector> Factory for EM with multivariate Gaussian model, using the textbook algorithm.
static class	TextbookMultivariateGaussianModelFactory.Parameterizer<V extends NumberVector> Parameterization class
class	TwoPassMultivariateGaussianModelFactory<V extends NumberVector> Factory for EM with multivariate Gaussian models (with covariance; also known as Gaussian Mixture Modeling, GMM).
static class	TwoPassMultivariateGaussianModelFactory.Parameterizer<V extends NumberVector> Parameterization class

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

Modifier and Type	Method and Description
double	MultivariateGaussianModel.estimateLogDensity(NumberVector vec)
double	EMClusterModel.estimateLogDensity(NumberVector vec) Estimate the log likelihood of a vector.
double	DiagonalGaussianModel.estimateLogDensity(NumberVector vec)
double	SphericalGaussianModel.estimateLogDensity(NumberVector vec)
double	TextbookMultivariateGaussianModel.estimateLogDensity(NumberVector vec)
double	TwoPassMultivariateGaussianModel.estimateLogDensity(NumberVector vec)
default void	EMClusterModel.firstPassE(NumberVector vec, double weight) First run in the E step.
void	TwoPassMultivariateGaussianModel.firstPassE(NumberVector vec, double wei) First pass: update the mean only.
double	MultivariateGaussianModel.mahalanobisDistance(NumberVector vec) Compute the Mahalanobis distance from the centroid for a given vector.
double	DiagonalGaussianModel.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	TwoPassMultivariateGaussianModel.mahalanobisDistance(NumberVector vec) Compute the Mahalanobis distance from the centroid for a given vector.
void	MultivariateGaussianModel.updateE(NumberVector vec, double wei)
void	EMClusterModel.updateE(NumberVector vec, double weight) Process one data point in the E step
void	DiagonalGaussianModel.updateE(NumberVector vec, double wei)
void	SphericalGaussianModel.updateE(NumberVector vec, double wei)
void	TextbookMultivariateGaussianModel.updateE(NumberVector vec, double wei)
void	TwoPassMultivariateGaussianModel.updateE(NumberVector vec, double wei) Second pass: compute the covariance matrix.

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

Modifier and Type	Method and Description
static double	EM.assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, java.util.List<? extends EMClusterModel<?>> 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.
static void	EM.recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<? extends EMClusterModel<?>> models, double prior) Recompute the covariance matrixes.

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

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

Modifier and Type	Class and Description
class	COPACNeighborPredicate<V extends NumberVector> COPAC neighborhood predicate.
static class	COPACNeighborPredicate.Parameterizer<V extends NumberVector> Parameterization class.
class	ERiCNeighborPredicate<V extends NumberVector> ERiC neighborhood predicate.
static class	ERiCNeighborPredicate.Parameterizer<V extends NumberVector> Parameterization class.
class	FourCNeighborPredicate<V extends NumberVector> 4C identifies local subgroups of data objects sharing a uniform correlation.
static class	FourCNeighborPredicate.Parameterizer<O extends NumberVector> Parameterization class.
class	LSDBC<O extends NumberVector> Locally Scaled Density Based Clustering.
static class	LSDBC.Parameterizer<O extends NumberVector> Parameterization class
class	PreDeConNeighborPredicate<V extends NumberVector> Neighborhood predicate used by PreDeCon.
static class	PreDeConNeighborPredicate.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

Modifier and Type	Method and 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.

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

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

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch with parameters of type NumberVector

Modifier and Type	Method and Description
protected void	ClusteringFeature.addToStatistics(NumberVector nv) Add a number vector to the current node.
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	DiameterCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)
double	EuclideanDistanceCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)
double	RadiusCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n)
double	BIRCHAbsorptionCriterion.squaredCriterion(ClusteringFeature f1, NumberVector n) Quality of a CF when adding a data point
double	CentroidEuclideanDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	BIRCHDistance.squaredDistance(NumberVector v, ClusteringFeature cf) Distance of a vector to a clustering feature.
double	AverageIntraclusterDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	VarianceIncreaseDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	CentroidManhattanDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
double	AverageInterclusterDistance.squaredDistance(NumberVector v, ClusteringFeature cf)
static double	ClusteringFeature.sumOfSquares(NumberVector v) Compute the sum of squares of a vector.

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

Modifier and Type	Method and 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.

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

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

Modifier and Type	Class and Description
class	AbstractKMeans<V extends NumberVector,M extends Model> Abstract base class for k-means implementations.
static class	AbstractKMeans.Parameterizer<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.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.
interface	KMeans<V extends NumberVector,M extends Model> Some constants and options shared among kmeans family algorithms.
class	KMeansAnnulus<V extends NumberVector> Annulus k-means algorithm.
static class	KMeansAnnulus.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansBisecting<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	KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.
class	KMeansCompare<V extends NumberVector> Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
static class	KMeansCompare.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
static class	KMeansElkan.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansExponion<V extends NumberVector> Newlings's exponion k-means algorithm, exploiting the triangle inequality.
static class	KMeansExponion.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
static class	KMeansHamerly.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
static class	KMeansLloyd.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
static class	KMeansMacQueen.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansMinusMinus<V extends NumberVector> k-means--: A Unified Approach to Clustering and Outlier Detection.
static class	KMeansMinusMinus.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansSimplifiedElkan<V extends NumberVector> Simplified version of Elkan's k-means by exploiting the triangle inequality.
static class	KMeansSimplifiedElkan.Parameterizer<V extends NumberVector> Parameterization class.
class	KMeansSort<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	KMeansSort.Parameterizer<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 KMedoidsPAM instead).
static class	KMediansLloyd.Parameterizer<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.Parameterizer<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.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.

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

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

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

Modifier and Type	Method and Description
protected double	AbstractKMeans.Instance.distance(NumberVector x, NumberVector y) Compute a distance (and count the distance computations).
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.
private boolean	KMeansMacQueen.Instance.updateMeanAndAssignment(int minIndex, NumberVector fv, DBIDIter iditer) Try to update the cluster assignment.

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

Modifier and Type	Method and Description
protected Clustering<KMeansModel>	AbstractKMeans.Instance.buildResult(boolean varstat, Relation<? extends NumberVector> relation) Build the result, recomputing the cluster variance if varstat is set to true.
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.

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

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

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

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

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

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

Modifier and Type	Method and Description
(package private) static double	KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights, DBIDs ids, NumberVector first, DistanceQuery<? super NumberVector> distQ) Initialize the weight list.
private static double	KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights, DBIDs ids, NumberVector latest, DistanceQuery<? super NumberVector> distQ) Update the weight list.

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

Modifier and Type	Method and Description
double[][]	PAMInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomUniformGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	KMeansInitialization.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction) Choose initial means
double[][]	ParkInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FarthestSumPointsInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	PredefinedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	LABInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	OstrovskyInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomNormalGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
(package private) static void	KMeansPlusPlusInitialMeans.chooseRemaining(Relation<? extends NumberVector> relation, DBIDs ids, DistanceQuery<NumberVector> distQ, int k, java.util.List<NumberVector> means, WritableDoubleDataStore weights, double weightsum, java.util.Random random) Choose remaining means, weighted by distance.
(package private) static void	KMeansPlusPlusInitialMeans.chooseRemaining(Relation<? extends NumberVector> relation, DBIDs ids, DistanceQuery<NumberVector> distQ, int k, java.util.List<NumberVector> means, WritableDoubleDataStore weights, double weightsum, java.util.Random random) Choose remaining means, weighted by distance.
(package private) static void	KMeansPlusPlusInitialMeans.chooseRemaining(Relation<? extends NumberVector> relation, DBIDs ids, DistanceQuery<NumberVector> distQ, int k, java.util.List<NumberVector> means, WritableDoubleDataStore weights, double weightsum, java.util.Random random) Choose remaining means, weighted by distance.
(package private) static double	KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights, DBIDs ids, NumberVector first, DistanceQuery<? super NumberVector> distQ) Initialize the weight list.
static double[][]	AbstractKMeansInitialization.unboxVectors(java.util.List<? extends NumberVector> means) Unbox database means to primitive means.
private static double	KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights, DBIDs ids, NumberVector latest, DistanceQuery<? super NumberVector> distQ) Update the weight list.

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

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

Modifier and Type	Class and 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.Parameterizer<V extends NumberVector> Parameterization class

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

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

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with type parameters of type NumberVector

Modifier and Type	Method and Description
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static <V extends NumberVector> double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Variance contribution of a single cluster.

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

Modifier and Type	Method and Description
static int	AbstractKMeansQualityMeasure.numberOfFreeParameters(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering) Compute the number of free parameters.

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

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

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

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

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

Modifier and Type	Class and Description
class	DeLiClu<V extends NumberVector> DeliClu: Density-Based Hierarchical Clustering A hierarchical algorithm to find density-connected sets in a database, closely related to OPTICS but exploiting the structure of a R-tree for acceleration.
static class	DeLiClu.Parameterizer<V extends NumberVector> Parameterization class.
class	FastOPTICS<V extends NumberVector> FastOPTICS algorithm (Fast approximation of OPTICS) Note that this is not FOPTICS as in "Fuzzy OPTICS"!
static class	FastOPTICS.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

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

Modifier and Type	Method and 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.

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

Modifier and Type	Method and Description
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 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 java.util.Collection<CLIQUEUnit>	CLIQUE.initOneDimensionalUnits(Relation<? extends NumberVector> database) Initializes and returns the one dimensional units.
Clustering<SubspaceModel>	CLIQUE.run(Relation<? extends NumberVector> relation) Performs the CLIQUE algorithm on the given database.

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

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

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

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

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

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

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

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

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

Modifier and Type	Class and Description
class	COP<V extends NumberVector> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Reference: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces Proc.
static class	COP.Parameterizer<V extends NumberVector> Parameterization class.
class	GaussianModel<V extends NumberVector> Outlier detection based on the probability density of the single normal distribution.
static class	GaussianModel.Parameterizer<V extends NumberVector> Parameterization class.
class	GaussianUniformMixture<V extends NumberVector> Outlier detection algorithm using a mixture model approach.
static class	GaussianUniformMixture.Parameterizer<V extends NumberVector> Parameterization class.
class	SimpleCOP<V extends NumberVector> Algorithm to compute local correlation outlier probability.
static class	SimpleCOP.Parameterizer<V extends NumberVector> Parameterization class.

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

Modifier and Type	Method and Description
private static void	COP.computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids) Recompute the centroid of a set.

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
private double	CBLOF.computeSmallClusterCBLOF(O obj, NumberVectorDistanceFunction<? super O> distance, java.util.List<NumberVector> largeClusterMeans, Cluster<MeanModel> cluster)

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

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

Modifier and Type	Class and Description
class	HilOut<O extends NumberVector> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
static class	HilOut.Parameterizer<O extends NumberVector> Parameterization class

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

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.algorithm.outlier.distance with type arguments of type NumberVector

Modifier and Type	Method and 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(Database database, Relation<? extends NumberVector> relation) Run the algorithm on the given relation.

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

Constructor and Description
ReferenceBasedOutlierDetection(int k, NumberVectorDistanceFunction<? super NumberVector> distanceFunction, ReferencePointsHeuristic refp) Constructor with parameters.

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

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

Modifier and Type	Class and Description
class	ALOCI<O extends NumberVector> Fast Outlier Detection Using the "approximate Local Correlation Integral".
static class	ALOCI.Parameterizer<O extends NumberVector> Parameterization class.
class	LDF<O extends NumberVector> Outlier Detection with Kernel Density Functions.
static class	LDF.Parameterizer<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.Parameterizer<O extends NumberVector> Parameterization class.

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof that implement NumberVector

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
private 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(Database database, Relation<NumberVector> relation) Run the algorithm on a data set.

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

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

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

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

Modifier and Type	Method and Description
OutlierResult	CTLuScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Run the algorithm.
OutlierResult	CTLuMoranScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuMedianAlgorithm.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuZTestOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector> relationy) Run the algorithm
OutlierResult	CTLuRandomWalkEC.run(Relation<P> spatial, Relation<? extends NumberVector> relation) Run the algorithm.
private Pair<DBIDVar,java.lang.Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector> relationy) Run a single iteration of the GLS-SOD modeling step

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

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

Modifier and Type	Class and Description
class	AbstractAggarwalYuOutlier<V extends NumberVector> Abstract base class for the sparse-grid-cell based outlier detection of Aggarwal and Yu.
class	AggarwalYuEvolutionary<V extends NumberVector> Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
static class	AggarwalYuEvolutionary.Parameterizer<V extends NumberVector> Parameterization class.
class	AggarwalYuNaive<V extends NumberVector> BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
static class	AggarwalYuNaive.Parameterizer<V extends NumberVector> Parameterization class.
class	SOD<V extends NumberVector> Subspace Outlier Degree.
static class	SOD.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

Modifier and Type	Method and Description
private static double[]	SOD.computePerDimensionVariances(Relation<? extends NumberVector> relation, double[] center, DBIDs neighborhood) Compute the per-dimension variances for the given neighborhood and center.
private DoubleDBIDList	OUTRES.initialRange(DBIDRef obj, DBIDs cands, PrimitiveDistanceFunction<? super NumberVector> df, double eps, OUTRES.KernelDensityEstimator kernel, ModifiableDoubleDBIDList n) Initial range query.
OutlierResult	OUTRES.run(Relation<? extends NumberVector> relation) Main loop for OUTRES
private DoubleDBIDList	OUTRES.subsetNeighborhoodQuery(DoubleDBIDList neighc, DBIDRef dbid, PrimitiveDistanceFunction<? super NumberVector> df, double adjustedEps, OUTRES.KernelDensityEstimator kernel, ModifiableDoubleDBIDList n) Refine neighbors within a subset.

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

Constructor and Description
KernelDensityEstimator(Relation<? extends NumberVector> relation, double eps) Constructor.

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

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	EvaluateRankingQuality<V extends NumberVector> Evaluate a distance function with respect to kNN queries.
static class	EvaluateRankingQuality.Parameterizer<V extends NumberVector> Parameterization class.
class	RangeQuerySelectivity<V extends NumberVector> Evaluate the range query selectivity.
static class	RangeQuerySelectivity.Parameterizer<V extends NumberVector> Parameterization class.

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

Modifier and Type	Method and Description
protected double	HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery, Relation<NumberVector> relation, int dim) Search nearest neighbors for real data members.
protected double	HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery, Relation<NumberVector> relation, int dim) Search nearest neighbors for real data members.
protected double	HopkinsStatisticClusteringTendency.computeNNForUniformData(KNNQuery<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.
Result	HopkinsStatisticClusteringTendency.run(Database database, Relation<NumberVector> relation) Runs the algorithm in the timed evaluation part.
private ScalesResult	AddUniformScale.run(Relation<? extends NumberVector> rel) Add scales to a single vector relation.
private ScalesResult	AddSingleScale.run(Relation<? extends NumberVector> rel) Add scales to a single vector relation.

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

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
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 de.lmu.ifi.dbs.elki.application.greedyensemble with type arguments of type NumberVector

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

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

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

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

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

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

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

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.data with type parameters of type NumberVector

Modifier and Type	Field and Description
private Relation<? extends NumberVector>	VectorUtil.SortDBIDsBySingleDimension.data The relation to sort.
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 de.lmu.ifi.dbs.elki.data with type parameters of type NumberVector

Modifier and Type	Method and Description
static <V extends NumberVector> V	VectorUtil.project(V v, 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 de.lmu.ifi.dbs.elki.data with parameters of type NumberVector

Modifier and Type	Method and Description
static double	VectorUtil.angle(NumberVector v1, NumberVector v2, NumberVector o) Compute the angle between two vectors 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, NumberVector v2) Compute the dot product of the angle between two 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 de.lmu.ifi.dbs.elki.data with type arguments of type NumberVector

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

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

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

Modifier and Type	Class and Description
class	CorrelationAnalysisSolution<V extends NumberVector> A solution of correlation analysis is a matrix of equations describing the dependencies.

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

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.data.model that return NumberVector

Modifier and Type	Method and 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.

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

Modifier and Type	Method and 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.

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

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

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

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

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

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

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

Modifier and Type	Method and Description
double[]	RandomProjectionFamily.Projection.project(NumberVector in) Project a single vector.
double[]	AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector in)
double[]	RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector in)
double[]	SimplifiedRandomHyperplaneProjectionFamily.SignedProjection.project(NumberVector in)
double[]	RandomProjectionFamily.Projection.project(NumberVector in, double[] buffer) Project a single vector, into the given buffer.
double[]	AbstractRandomProjectionFamily.MatrixProjection.project(NumberVector in, double[] ret)
double[]	RandomSubsetProjectionFamily.SubsetProjection.project(NumberVector in, double[] buffer)
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 de.lmu.ifi.dbs.elki.data.type

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

Modifier and Type	Field and Description
static 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 de.lmu.ifi.dbs.elki.data.uncertain

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

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.database.query.knn

Classes in de.lmu.ifi.dbs.elki.database.query.knn with type parameters of type NumberVector

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.database.query.range

Classes in de.lmu.ifi.dbs.elki.database.query.range with type parameters of type NumberVector

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

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

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

Modifier and Type	Method and Description
static <V extends NumberVector> 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 de.lmu.ifi.dbs.elki.database.relation with type arguments of type NumberVector

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.datasource.filter

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

Modifier and Type	Class and Description
class	AbstractVectorConversionFilter<I,O extends NumberVector> Abstract class for filters that produce number vectors.
class	AbstractVectorStreamConversionFilter<I,O extends NumberVector> Abstract base class for streaming filters that produce vectors.

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	AttributeWiseBetaNormalization<V extends NumberVector> Project the data using a Beta distribution.
static class	AttributeWiseBetaNormalization.Parameterizer<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.Parameterizer<V extends NumberVector> Parameterization class.
class	AttributeWiseMADNormalization<V extends NumberVector> Median Absolute Deviation is used for scaling the data set as follows: First, the median, and median absolute deviation are computed in each axis.
class	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.Parameterizer<V extends NumberVector> Parameterization class.
class	AttributeWiseVarianceNormalization<V extends NumberVector> Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation in each dimension.
static class	AttributeWiseVarianceNormalization.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise

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

Modifier and Type	Class and 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.Parameterizer<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.Parameterizer<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.Parameterizer<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.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

Modifier and Type	Method and Description
protected HellingerHistogramNormalization<NumberVector>	HellingerHistogramNormalization.Parameterizer.makeInstance()
protected InstanceLogRankNormalization<NumberVector>	InstanceLogRankNormalization.Parameterizer.makeInstance()
protected InstanceRankNormalization<NumberVector>	InstanceRankNormalization.Parameterizer.makeInstance()

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

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

Modifier and Type	Class and Description
class	AbstractSupervisedProjectionVectorFilter<V extends NumberVector> Base class for supervised projection methods.
static class	AbstractSupervisedProjectionVectorFilter.Parameterizer<V extends NumberVector> Parameterization class.
class	ClassicMultidimensionalScalingTransform<I,O extends NumberVector> Rescale the data set using multidimensional scaling, MDS.
static class	ClassicMultidimensionalScalingTransform.Parameterizer<I,O extends NumberVector> Parameterization class.
class	FastMultidimensionalScalingTransform<I,O extends NumberVector> Rescale the data set using multidimensional scaling, MDS.
static class	FastMultidimensionalScalingTransform.Parameterizer<I,O extends NumberVector> Parameterization class.
class	GlobalPrincipalComponentAnalysisTransform<O extends NumberVector> Apply Principal Component Analysis (PCA) to the data set.
static class	GlobalPrincipalComponentAnalysisTransform.Parameterizer<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.Parameterizer<V extends NumberVector> Parameterization class.
class	LinearDiscriminantAnalysisFilter<V extends NumberVector> Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
static class	LinearDiscriminantAnalysisFilter.Parameterizer<V extends NumberVector> Parameterization class.
class	LngLatToECEFFilter<V extends NumberVector> Project a 2D data set (longitude, latitude) to a 3D coordinate system (X, Y, Z), such that Euclidean distance is line-of-sight.
static class	LngLatToECEFFilter.Parameterizer<V extends NumberVector> Parameterization class.
class	NumberVectorFeatureSelectionFilter<V extends NumberVector> Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes.
class	NumberVectorRandomFeatureSelectionFilter<V extends NumberVector> Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes.
class	PerturbationFilter<V extends NumberVector> A filter to perturb the values by adding micro-noise.
static class	PerturbationFilter.Parameterizer<V extends NumberVector> Parameterization class.

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

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

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

Methods in de.lmu.ifi.dbs.elki.datasource.filter.typeconversions that return types with arguments of type NumberVector

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector>	CanberraDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ArcCosineDistanceFunction.getInputTypeRestriction()
VectorFieldTypeInformation<? super NumberVector>	MatrixWeightedQuadraticDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	CosineUnitlengthDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	CosineDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	AbstractNumberVectorDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ClarkDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ArcCosineUnitlengthDistanceFunction.getInputTypeRestriction()

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

Modifier and Type	Method and Description
static int	AbstractNumberVectorDistanceFunction.dimensionality(NumberVector o1, NumberVector o2) Get the common dimensionality of the two objects.
static int	AbstractNumberVectorDistanceFunction.dimensionality(NumberVector o1, NumberVector o2, int expect) Get the common dimensionality of the two objects.
double	NumberVectorDistanceFunction.distance(NumberVector o1, NumberVector o2) Computes the distance between two given vectors according to this distance function.
double	CanberraDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	ArcCosineDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the cosine distance for two given feature vectors.
double	BrayCurtisDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	MatrixWeightedQuadraticDistanceFunction.distance(NumberVector o1, NumberVector o2)
double	CosineUnitlengthDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the cosine distance for two given feature vectors.
double	CosineDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the cosine distance for two given feature vectors.
double	ClarkDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedCanberraDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	MahalanobisDistanceFunction.distance(NumberVector o1, NumberVector o2)
double	ArcCosineUnitlengthDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the cosine distance for two given feature vectors.
double	MatrixWeightedQuadraticDistanceFunction.norm(NumberVector obj)
double	MahalanobisDistanceFunction.norm(NumberVector obj)

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

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

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

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

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

Modifier and Type	Method and Description
double	SquaredUncenteredCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	AbsolutePearsonCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the absolute Pearson correlation distance for two given feature vectors.
double	WeightedPearsonCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the Pearson correlation distance for two given feature vectors.
double	PearsonCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the Pearson correlation distance for two given feature vectors.
double	AbsoluteUncenteredCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedSquaredPearsonCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	UncenteredCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2) Computes the Pearson correlation distance for two given feature vectors.
double	SquaredPearsonCorrelationDistanceFunction.distance(NumberVector v1, NumberVector v2)
static double	UncenteredCorrelationDistanceFunction.uncenteredCorrelation(NumberVector x, NumberVector y) Compute the uncentered correlation of two vectors.

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

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

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

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

Modifier and Type	Method and Description
double	DimensionSelectingLatLngDistanceFunction.distance(NumberVector o1, NumberVector o2)
double	LatLngDistanceFunction.distance(NumberVector o1, NumberVector o2)
double	LngLatDistanceFunction.distance(NumberVector o1, NumberVector o2)

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

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

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

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

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

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

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

Modifier and Type	Method and Description
double	ManhattanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedLPNormDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	EuclideanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedEuclideanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedMaximumDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	MaximumDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	LPNormDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedManhattanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	LPIntegerNormDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	MinimumDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	WeightedSquaredEuclideanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	SquaredEuclideanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	ManhattanDistanceFunction.norm(NumberVector v)
double	WeightedLPNormDistanceFunction.norm(NumberVector v)
double	EuclideanDistanceFunction.norm(NumberVector v)
double	WeightedEuclideanDistanceFunction.norm(NumberVector v)
double	WeightedMaximumDistanceFunction.norm(NumberVector v)
double	MaximumDistanceFunction.norm(NumberVector v)
double	LPNormDistanceFunction.norm(NumberVector v)
double	WeightedManhattanDistanceFunction.norm(NumberVector v)
double	LPIntegerNormDistanceFunction.norm(NumberVector v)
double	MinimumDistanceFunction.norm(NumberVector v)
double	WeightedSquaredEuclideanDistanceFunction.norm(NumberVector obj)
double	SquaredEuclideanDistanceFunction.norm(NumberVector v)
private double	ManhattanDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedLPNormDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	EuclideanDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedEuclideanDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	WeightedMaximumDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	MaximumDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	LPNormDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end) Compute unscaled distance in a range of dimensions.
private double	WeightedManhattanDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	LPIntegerNormDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end) Compute unscaled distance in a range of dimensions.
private double	SquaredEuclideanDistanceFunction.preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double	ManhattanDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedLPNormDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	EuclideanDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedEuclideanDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	WeightedMaximumDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	MaximumDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	LPNormDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end) Compute unscaled distance in a range of dimensions.
private double	WeightedManhattanDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	LPIntegerNormDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end) Compute unscaled distance in a range of dimensions.
private double	SquaredEuclideanDistanceFunction.preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double	ManhattanDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	WeightedLPNormDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	EuclideanDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	WeightedEuclideanDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	WeightedMaximumDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	MaximumDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	LPNormDistanceFunction.preNorm(NumberVector v, int start, int end) Compute unscaled norm in a range of dimensions.
private double	WeightedManhattanDistanceFunction.preNorm(NumberVector v, int start, int end)
private double	LPIntegerNormDistanceFunction.preNorm(NumberVector v, int start, int end) Compute unscaled norm in a range of dimensions.
private double	SquaredEuclideanDistanceFunction.preNorm(NumberVector v, int start, int end)

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

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

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

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

Modifier and Type	Method and Description
SimpleTypeInformation<? super NumberVector>	FisherRaoDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	HellingerDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	ChiSquaredDistanceFunction.getInputTypeRestriction()

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

Modifier and Type	Method and Description
double	FisherRaoDistanceFunction.distance(NumberVector fv1, NumberVector fv2)
double	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	HellingerDistanceFunction.distance(NumberVector fv1, NumberVector fv2)
double	ChiDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	KullbackLeiblerDivergenceAsymmetricDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	JeffreyDivergenceDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	TriangularDiscriminationDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	ChiSquaredDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	TriangularDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	JensenShannonDivergenceDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	SqrtJensenShannonDivergenceDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	HellingerDistanceFunction.similarity(NumberVector o1, NumberVector o2)

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
double	SubspaceLPNormDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	SubspaceMaximumDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	OnedimensionalDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	SubspaceManhattanDistanceFunction.distance(NumberVector v1, NumberVector v2)
double	SubspaceEuclideanDistanceFunction.distance(NumberVector v1, NumberVector v2) Constructor.
protected double	SubspaceLPNormDistanceFunction.minDistObject(SpatialComparable mbr, NumberVector v)
protected double	SubspaceMaximumDistanceFunction.minDistObject(SpatialComparable mbr, NumberVector v)
protected double	SubspaceManhattanDistanceFunction.minDistObject(SpatialComparable mbr, NumberVector v)
protected double	SubspaceEuclideanDistanceFunction.minDistObject(SpatialComparable mbr, NumberVector v)
double	SubspaceLPNormDistanceFunction.norm(NumberVector obj)
double	SubspaceMaximumDistanceFunction.norm(NumberVector obj)
double	OnedimensionalDistanceFunction.norm(NumberVector obj)
double	SubspaceManhattanDistanceFunction.norm(NumberVector obj)
double	SubspaceEuclideanDistanceFunction.norm(NumberVector obj)

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

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

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

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

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

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

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

Modifier and Type	Method and Description
<T extends NumberVector> SpatialPrimitiveDistanceSimilarityQuery<T>	Kulczynski1SimilarityFunction.instantiate(Relation<T> database)

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

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

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

Modifier and Type	Method and Description
double	Kulczynski1SimilarityFunction.distance(NumberVector v1, NumberVector v2)
double	Kulczynski2SimilarityFunction.similarity(NumberVector v1, NumberVector v2)
double	Kulczynski1SimilarityFunction.similarity(NumberVector v1, NumberVector v2)

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

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

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

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

Modifier and Type	Method and Description
double	LinearKernelFunction.distance(NumberVector fv1, NumberVector fv2)
double	PolynomialKernelFunction.distance(NumberVector fv1, NumberVector fv2)
double	LaplaceKernelFunction.similarity(NumberVector o1, NumberVector o2)
double	RadialBasisFunctionKernelFunction.similarity(NumberVector o1, NumberVector o2)
double	LinearKernelFunction.similarity(NumberVector o1, NumberVector o2)
double	SigmoidKernelFunction.similarity(NumberVector o1, NumberVector o2)
double	RationalQuadraticKernelFunction.similarity(NumberVector o1, NumberVector o2)
double	PolynomialKernelFunction.similarity(NumberVector o1, NumberVector o2)

Uses of NumberVector in de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Fields in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with type parameters of type NumberVector

Modifier and Type	Field and Description
private PrimitiveDistanceFunction<NumberVector>	EvaluateConcordantPairs.Parameterizer.distance Distance function to use.
private PrimitiveDistanceFunction<? super NumberVector>	EvaluateConcordantPairs.distanceFunction Distance function to use.

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

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

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with parameters of type NumberVector

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

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

Modifier and Type	Method and Description
static int	EvaluateSimplifiedSilhouette.centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) Compute centroids.
protected double[]	EvaluateConcordantPairs.computeWithinDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, int withinPairs)
double	EvaluateSquaredErrors.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateConcordantPairs.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateVarianceRatioCriteria.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateSimplifiedSilhouette.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluatePBMIndex.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateDaviesBouldin.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
static int	EvaluateVarianceRatioCriteria.globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) Update the global centroid.
double[]	EvaluateDaviesBouldin.withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)

Constructor parameters in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with type arguments of type NumberVector

Constructor and Description
EvaluateConcordantPairs(PrimitiveDistanceFunction<? super NumberVector> distance, NoiseHandling noiseHandling) Constructor.

Uses of NumberVector in de.lmu.ifi.dbs.elki.evaluation.scores.adapter

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

Modifier and Type	Field and Description
(package private) NumberVector	VectorZero.vec Vector to use as reference
(package private) NumberVector	VectorOverThreshold.vec Vector to use as reference
protected NumberVector	AbstractVectorIter.vec Data vector.

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

Constructor and Description
AbstractVectorIter(NumberVector vec) Constructor.
DecreasingVectorIter(NumberVector vec) Constructor.
IncreasingVectorIter(NumberVector vec) Constructor.
VectorNonZero(NumberVector vec) Constructor.
VectorOverThreshold(NumberVector vec, double threshold) Constructor.
VectorZero(NumberVector vec) Constructor.

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

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

Modifier and Type	Class and 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.Parameterizer<V extends NumberVector> Parameterizer for inverted list index.

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

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

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

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

Modifier and Type	Method and Description
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 de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with type arguments of type NumberVector

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.index.lsh.hashfunctions

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

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

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

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

Modifier and Type	Class and Description
class	RandomProjectedNeighborsAndDensities<V extends NumberVector> Random Projections used for computing neighbors and density estimates.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	AbstractFilteredPCAIndex<NV extends NumberVector> Abstract base class for a local PCA based index.
static class	AbstractFilteredPCAIndex.Factory<NV extends NumberVector> Factory class.
static class	AbstractFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector,I extends AbstractFilteredPCAIndex<NV>> Parameterization class.
interface	FilteredLocalPCAIndex<NV extends NumberVector> Interface for an index providing local PCA results.
static interface	FilteredLocalPCAIndex.Factory<NV extends NumberVector> Factory interface
class	KNNQueryFilteredPCAIndex<NV extends NumberVector> Provides the local neighborhood to be considered in the PCA as the k nearest neighbors of an object.
static class	KNNQueryFilteredPCAIndex.Factory<V extends NumberVector> Factory class.
static class	KNNQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector> Parameterization class.

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

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

Modifier and Type	Class and Description
class	AbstractPreferenceVectorIndex<NV extends NumberVector> Abstract base class for preference vector based algorithms.
static class	AbstractPreferenceVectorIndex.Factory<V extends NumberVector> Factory class.
class	DiSHPreferenceVectorIndex<V extends NumberVector> Preprocessor for DiSH preference vector assignment to objects of a certain database.
static class	DiSHPreferenceVectorIndex.Factory<V extends NumberVector> Factory class.
static class	DiSHPreferenceVectorIndex.Factory.Parameterizer<V extends NumberVector> Parameterization class.
class	HiSCPreferenceVectorIndex<V extends NumberVector> Preprocessor for HiSC preference vector assignment to objects of a certain database.
static class	HiSCPreferenceVectorIndex.Factory<V extends NumberVector> Factory class.
static class	HiSCPreferenceVectorIndex.Factory.Parameterizer<V extends NumberVector> Parameterization class.
interface	PreferenceVectorIndex<NV extends NumberVector> Interface for an index providing preference vectors.
static interface	PreferenceVectorIndex.Factory<V extends NumberVector> Factory interface

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	FlatRStarTreeFactory<O extends NumberVector> Factory for flat R*-Trees.
static class	FlatRStarTreeFactory.Parameterizer<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 de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query

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

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

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

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

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

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

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

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

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

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

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

Constructor and Description
RdkNNSettings(int k_max, SpatialPrimitiveDistanceFunction<NumberVector> distanceFunction) Constructor.

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

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

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

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

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

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

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

Modifier and Type	Method and Description
protected static VectorApproximation	PartialVAFile.calculatePartialApproximation(DBID id, NumberVector dv, 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 de.lmu.ifi.dbs.elki.index.vafile with parameters of type NumberVector

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

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

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

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

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

Modifier and Type	Method and Description
static double	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 for two FeatureVectors.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
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 de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike

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

Modifier and Type	Method and Description
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 de.lmu.ifi.dbs.elki.utilities.referencepoints

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

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

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

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.parallel3d

Classes in de.lmu.ifi.dbs.elki.visualization.parallel3d with type parameters of type NumberVector

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

Uses of NumberVector in de.lmu.ifi.dbs.elki.visualization.parallel3d.layout

Method parameters in de.lmu.ifi.dbs.elki.visualization.parallel3d.layout with type arguments of type NumberVector

Modifier and Type	Method and Description
static double[]	AbstractLayout3DPC.computeSimilarityMatrix(DependenceMeasure 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 de.lmu.ifi.dbs.elki.visualization.projections

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

Modifier and Type	Method and Description
<NV extends NumberVector> NV	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 de.lmu.ifi.dbs.elki.visualization.projections with parameters of type NumberVector

Modifier and Type	Method and Description
double[]	SimpleParallel.fastProjectDataToRenderSpace(NumberVector data)
double[]	AffineProjection.fastProjectDataToRenderSpace(NumberVector data)
double[]	ProjectionParallel.fastProjectDataToRenderSpace(NumberVector v) Fast project a vector from data to render space
double[]	Projection2D.fastProjectDataToRenderSpace(NumberVector data) Project a data vector from data space to rendering space.
double	Simple1D.fastProjectDataToRenderSpace(NumberVector data)
double	Projection1D.fastProjectDataToRenderSpace(NumberVector data) Project a data vector from data space to rendering space.
double[]	Simple2D.fastProjectDataToRenderSpace(NumberVector data)
double[]	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[]	Projection2D.fastProjectRelativeDataToRenderSpace(NumberVector data) Project a data vector from data space to rendering space.
double	Simple1D.fastProjectRelativeDataToRenderSpace(NumberVector data)
double	Projection1D.fastProjectRelativeDataToRenderSpace(NumberVector data) Project a data vector from data space to rendering space.
double[]	Simple2D.fastProjectRelativeDataToRenderSpace(NumberVector data)
double[]	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 de.lmu.ifi.dbs.elki.visualization.projector

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

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

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

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

Modifier and Type	Method and 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 de.lmu.ifi.dbs.elki.visualization.visualizers.histogram

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

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

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

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

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

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

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

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

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

Modifier and Type	Method and Description
static 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 and Description
class	SameSizeKMeansAlgorithm<V extends NumberVector> K-means variation that produces equally sized clusters.
static class	SameSizeKMeansAlgorithm.Parameterizer<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 and Description
SimpleTypeInformation<? super NumberVector>	TutorialDistanceFunction.getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>	MultiLPNorm.getInputTypeRestriction()

Methods in tutorial.distancefunction with parameters of type NumberVector

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