Uses of Interface
elki.data.NumberVector
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Packages that use NumberVector Package Description elki.algorithm Miscellaneous algorithms.elki.algorithm.statistics Statistical analysis algorithms.elki.application.benchmark Benchmarking pseudo algorithms.elki.application.greedyensemble Greedy ensembles for outlier detection.elki.application.statistics Applications to compute some basic data set statistics.elki.clustering Clustering algorithms.elki.clustering.biclustering Biclustering algorithms.elki.clustering.correlation Correlation clustering algorithms.elki.clustering.correlation.cash Helper classes for theCASH
algorithm.elki.clustering.dbscan DBSCAN and its generalizations.elki.clustering.dbscan.predicates Neighbor and core predicated for Generalized DBSCAN.elki.clustering.em Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).elki.clustering.em.models elki.clustering.hierarchical Hierarchical agglomerative clustering (HAC).elki.clustering.hierarchical.birch BIRCH clustering.elki.clustering.kmeans K-means clustering and variations.elki.clustering.kmeans.initialization Initialization strategies for k-means.elki.clustering.kmeans.parallel Parallelized implementations of k-means.elki.clustering.kmeans.quality Quality measures for k-Means results.elki.clustering.kmeans.spherical Spherical k-means clustering and variations.elki.clustering.kmedoids.initialization elki.clustering.onedimensional Clustering algorithms for one-dimensional data.elki.clustering.optics OPTICS family of clustering algorithms.elki.clustering.subspace Axis-parallel subspace clustering algorithms.elki.clustering.subspace.clique Helper classes for theCLIQUE
algorithm.elki.clustering.svm elki.clustering.uncertain Clustering algorithms for uncertain data.elki.data Basic classes for different data types, database object types and label types.elki.data.model Cluster models classes for various algorithms.elki.data.projection Data projections.elki.data.projection.random Random projection families.elki.data.type Data type information, also used for type restrictions.elki.data.uncertain Uncertain data objects.elki.database.query.distance Prepared queries for distances.elki.database.query.knn Prepared queries for k nearest neighbor (kNN) queries.elki.database.query.range Prepared queries for ε-range queries, that return all objects within the radius ε.elki.database.relation Relations, materialized and virtual (views).elki.datasource.filter Data filtering, in particular for normalization and projection.elki.datasource.filter.cleaning Filters for data cleaning.elki.datasource.filter.normalization.columnwise Normalizations operating on columns / variates; where each column is treated independently.elki.datasource.filter.normalization.instancewise Instancewise normalization, where each instance is normalized independently.elki.datasource.filter.transform Data space transformations.elki.datasource.filter.typeconversions Filters to perform data type conversions.elki.datasource.parser Parsers for different file formats and data types.elki.distance Distance functions for use within ELKI.elki.distance.colorhistogram Distance functions for color histograms.elki.distance.correlation Distance functions using correlations.elki.distance.geo Geographic (earth) distance functions.elki.distance.histogram Distance functions for one-dimensional histograms.elki.distance.minkowski Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.elki.distance.probabilistic Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.elki.distance.set Distance functions for binary and set type data.elki.distance.subspace Distance functions based on subspaces.elki.distance.timeseries Distance functions designed for time series.elki.evaluation.clustering.internal Internal evaluation measures for clusterings.elki.evaluation.scores.adapter Adapter classes for ranking and scoring measures.elki.index.invertedlist Indexes using inverted lists.elki.index.lsh.hashfamilies Hash function families for LSH.elki.index.lsh.hashfunctions Hash functions for LSH.elki.index.preprocessed.fastoptics Preprocessed index used by the FastOPTICS algorithm.elki.index.preprocessed.knn Indexes providing KNN and rKNN data.elki.index.projected Projected indexes for data.elki.index.tree.betula BETULA clustering by aggregating the data into cluster features.elki.index.tree.betula.distance Distance functions for BETULA and BIRCH.elki.index.tree.betula.features Different variants of Betula and BIRCH cluster features.elki.index.tree.metrical.vptree elki.index.tree.spatial Tree-based index structures for spatial indexing.elki.index.tree.spatial.kd K-d-tree and variants.elki.index.tree.spatial.kd.split elki.index.tree.spatial.rstarvariants R*-tree and variants.elki.index.tree.spatial.rstarvariants.deliclu elki.index.tree.spatial.rstarvariants.flat elki.index.tree.spatial.rstarvariants.query Queries on the R-Tree family of indexes: kNN and range queries.elki.index.tree.spatial.rstarvariants.rdknn elki.index.tree.spatial.rstarvariants.rstar elki.index.vafile Vector Approximation File.elki.math Mathematical operations and utilities used throughout the framework.elki.math.linearalgebra The linear algebra package provides classes and computational methods for operations on matrices and vectors.elki.math.linearalgebra.pca Principal Component Analysis (PCA) and eigenvector processing.elki.math.spacefillingcurves Space filling curves.elki.math.statistics.intrinsicdimensionality Methods for estimating the intrinsic dimensionality.elki.outlier Outlier detection algorithms.elki.outlier.anglebased Angle-based outlier detection algorithms.elki.outlier.clustering Clustering based outlier detection.elki.outlier.density Density-based outlier detection algorithms.elki.outlier.distance Distance-based outlier detection algorithms, such as DBOutlier and kNN.elki.outlier.lof LOF family of outlier detection algorithms.elki.outlier.meta Meta outlier detection algorithms: external scores, score rescaling.elki.outlier.spatial Spatial outlier detection algorithms.elki.outlier.subspace Subspace outlier detection methods.elki.outlier.svm Support-Vector-Machines for outlier detection.elki.outlier.trivial Trivial outlier detection algorithms: no outliers, all outliers, label outliers.elki.result Result types, representation and handling.elki.similarity Similarity functions.elki.similarity.kernel Kernel functions.elki.timeseries Algorithms for change point detection in time series.elki.utilities.datastructures.arraylike Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.elki.utilities.referencepoints Package containing strategies to obtain reference points.elki.visualization.parallel3d 3DPC: 3D parallel coordinate plot visualization for ELKI.elki.visualization.parallel3d.layout Layouting algorithms for 3D parallel coordinate plots.elki.visualization.projections Visualization projections.elki.visualization.projector Projectors are responsible for finding appropriate projections for data relations.elki.visualization.svg Base SVG functionality (generation, markers, thumbnails, export, ...).elki.visualization.visualizers.histogram Visualizers based on 1D projected histograms.elki.visualization.visualizers.scatterplot Visualizers based on scatterplots.elki.visualization.visualizers.scatterplot.selection Visualizers for object selection based on 2D projections.tutorial.clustering Classes from the tutorial on implementing a custom k-means variation.tutorial.distancefunction Classes 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Uses of NumberVector in elki.algorithm
Classes in elki.algorithm with type parameters of type NumberVector Modifier and Type Class Description class
DependencyDerivator<V extends NumberVector>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.static class
DependencyDerivator.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.algorithm.statistics
Classes in elki.algorithm.statistics with type parameters of type NumberVector Modifier and Type Class Description class
EvaluateRankingQuality<V extends NumberVector>
Evaluate a distance function with respect to kNN queries.static class
EvaluateRankingQuality.Par<V extends NumberVector>
Parameterization class.Fields in elki.algorithm.statistics with type parameters of type NumberVector Modifier and Type Field Description protected NumberVectorDistance<? super NumberVector>
HopkinsStatisticClusteringTendency. distance
Distance function used.protected NumberVectorDistance<? super NumberVector>
HopkinsStatisticClusteringTendency.Par. distance
The distance function to use.Method parameters in elki.algorithm.statistics with type arguments of type NumberVector Modifier and Type Method Description protected double
HopkinsStatisticClusteringTendency. computeNNForRealData(KNNSearcher<DBIDRef> knnQuery, Relation<NumberVector> relation, int dim)
Search nearest neighbors for real data members.protected double
HopkinsStatisticClusteringTendency. computeNNForUniformData(KNNSearcher<NumberVector> knnQuery, double[] min, double[] extend)
Search nearest neighbors for artificial, uniform data.protected void
HopkinsStatisticClusteringTendency. initializeDataExtends(Relation<NumberVector> relation, int dim, double[] min, double[] extend)
Initialize the uniform sampling area.private ScalesResult
AddSingleScale. run(Relation<? extends NumberVector> rel)
Add scales to a single vector relation.private ScalesResult
AddUniformScale. run(Relation<? extends NumberVector> rel)
Add scales to a single vector relation.java.lang.Double
HopkinsStatisticClusteringTendency. run(Relation<NumberVector> relation)
Compute the Hopkins statistic for a vector relation.Constructor parameters in elki.algorithm.statistics with type arguments of type NumberVector Constructor Description HopkinsStatisticClusteringTendency(NumberVectorDistance<? super NumberVector> distance, int samplesize, RandomFactory random, int rep, int k, double[] minima, double[] maxima)
Constructor. -
Uses of NumberVector in elki.application.benchmark
Classes in elki.application.benchmark with type parameters of type NumberVector Modifier and Type Class Description class
RangeQueryBenchmark<O extends NumberVector>
Benchmarking algorithm that computes a range query for each point.static class
RangeQueryBenchmark.Par<O extends NumberVector>
Parameterization class -
Uses of NumberVector in elki.application.greedyensemble
Classes in elki.application.greedyensemble with type parameters of type NumberVector Modifier and Type Class Description class
ComputeKNNOutlierScores<O extends NumberVector>
Application that runs a series of kNN-based algorithms on a data set, for building an ensemble in a second step.static class
ComputeKNNOutlierScores.Par<O extends NumberVector>
Parameterization class.Fields in elki.application.greedyensemble declared as NumberVector Modifier and Type Field Description (package private) NumberVector
EvaluatePrecomputedOutlierScores. positive
Vector of positive values.Methods in elki.application.greedyensemble that return types with arguments of type NumberVector Modifier and Type Method Description static Relation<NumberVector>
GreedyEnsembleExperiment. applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip)
Prescale each vector (except when inskip
) with the given scaling function.private PrimitiveDistance<NumberVector>
GreedyEnsembleExperiment. getDistance(double[] estimated_weights)
Methods in elki.application.greedyensemble with parameters of type NumberVector Modifier and Type Method Description private boolean
EvaluatePrecomputedOutlierScores. checkForNaNs(NumberVector vec)
Check for NaN values.private void
EvaluatePrecomputedOutlierScores. processRow(java.io.PrintStream fout, NumberVector vec, java.lang.String label)
protected void
GreedyEnsembleExperiment. singleEnsemble(double[] ensemble, NumberVector vec)
Build a single-element "ensemble".Method parameters in elki.application.greedyensemble with type arguments of type NumberVector Modifier and Type Method Description static Relation<NumberVector>
GreedyEnsembleExperiment. applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip)
Prescale each vector (except when inskip
) with the given scaling function. -
Uses of NumberVector in elki.application.statistics
Classes in elki.application.statistics with type parameters of type NumberVector Modifier and Type Class Description class
RangeQuerySelectivity<V extends NumberVector>
Evaluate the range query selectivity.static class
RangeQuerySelectivity.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.clustering
Classes in elki.clustering with type parameters of type NumberVector Modifier and Type Class Description class
NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.static class
NaiveMeanShiftClustering.Par<V extends NumberVector>
Parameterizer.Method parameters in elki.clustering with type arguments of type NumberVector Modifier and Type Method Description Clustering<MeanModel>
BetulaLeafPreClustering. run(Relation<NumberVector> relation)
Run the clustering algorithm. -
Uses of NumberVector in elki.clustering.biclustering
Fields in elki.clustering.biclustering with type parameters of type NumberVector Modifier and Type Field Description protected Relation<? extends NumberVector>
AbstractBiclustering. relation
Relation we use.Method parameters in elki.clustering.biclustering with type arguments of type NumberVector Modifier and Type Method Description Clustering<M>
AbstractBiclustering. run(Relation<? extends NumberVector> relation)
Prepares the algorithm for running on a specific database. -
Uses of NumberVector in elki.clustering.correlation
Fields in elki.clustering.correlation with type parameters of type NumberVector Modifier and Type Field Description private Relation<? extends NumberVector>
HiCO.Instance. relation
Data relation.Method parameters in elki.clustering.correlation with type arguments of type NumberVector Modifier and Type Method Description private void
ORCLUS. assign(Relation<? extends NumberVector> database, java.util.List<ORCLUS.ORCLUSCluster> clusters)
Creates a partitioning of the database by assigning each object to its closest seed.private java.util.List<java.util.List<Cluster<CorrelationModel>>>
ERiC. extractCorrelationClusters(Clustering<Model> dbscanResult, Relation<? extends NumberVector> relation, int dimensionality, ERiCNeighborPredicate.Instance npred)
Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.private double[][]
ORCLUS. findBasis(Relation<? extends NumberVector> database, ORCLUS.ORCLUSCluster cluster, int dim)
Finds the basis of the subspace of dimensionalitydim
for the specified cluster.private LMCLUS.Separation
LMCLUS. findSeparation(Relation<? extends NumberVector> relation, DBIDs currentids, int dimension, java.util.Random r)
This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.private java.util.List<ORCLUS.ORCLUSCluster>
ORCLUS. initialSeeds(Relation<? extends NumberVector> database, int k)
Initializes the list of seeds wit a random sample of size k.private void
ORCLUS. merge(Relation<? extends NumberVector> relation, java.util.List<ORCLUS.ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress)
Reduces the number of seeds to k_newprivate Relation<ParameterizationFunction>
CASH. preprocess(Relation<? extends NumberVector> vrel)
Preprocess the dataset, precomputing the parameterization functions.private ORCLUS.ProjectedEnergy
ORCLUS. projectedEnergy(Relation<? extends NumberVector> relation, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster c_j, int i, int j, int dim)
Computes the projected energy of the specified clusters.Clustering<Model>
CASH. run(Relation<? extends NumberVector> rel)
Run CASH on the relation.Clustering<DimensionModel>
COPAC. run(Database database, Relation<? extends NumberVector> relation)
Run the COPAC algorithm.Clustering<CorrelationModel>
ERiC. run(Database database, Relation<? extends NumberVector> relation)
Performs the ERiC algorithm on the given database.ClusterOrder
HiCO. run(Relation<? extends NumberVector> relation)
Run the HiCO algorithm.Clustering<Model>
LMCLUS. run(Relation<? extends NumberVector> relation)
The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.Clustering<Model>
ORCLUS. run(Relation<? extends NumberVector> relation)
Performs the ORCLUS algorithm on the given database.private ORCLUS.ORCLUSCluster
ORCLUS. union(Relation<? extends NumberVector> relation, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster c2, int dim)
Returns the union of the two specified clusters.Constructor parameters in elki.clustering.correlation with type arguments of type NumberVector Constructor Description Instance(Relation<? extends NumberVector> relation)
Constructor. -
Uses of NumberVector in elki.clustering.correlation.cash
Fields in elki.clustering.correlation.cash declared as NumberVector Modifier and Type Field Description private NumberVector
ParameterizationFunction. vec
The actual vector.Constructors in elki.clustering.correlation.cash with parameters of type NumberVector Constructor Description ParameterizationFunction(NumberVector vec)
Provides a new parameterization function describing all lines in a d-dimensional feature space intersecting in one point p. -
Uses of NumberVector in elki.clustering.dbscan
Classes in elki.clustering.dbscan with type parameters of type NumberVector Modifier and Type Class Description class
GriDBSCAN<V extends NumberVector>
Using Grid for Accelerating Density-Based Clustering.protected static class
GriDBSCAN.Instance<V extends NumberVector>
Instance, for a single run.static class
GriDBSCAN.Par<O extends NumberVector>
Parameterization class.class
LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering.static class
LSDBC.Par<O extends NumberVector>
Parameterization class -
Uses of NumberVector in elki.clustering.dbscan.predicates
Fields in elki.clustering.dbscan.predicates with type parameters of type NumberVector Modifier and Type Field Description private Relation<? extends NumberVector>
ERiCNeighborPredicate.Instance. relation
Vector data relation.Methods in elki.clustering.dbscan.predicates with parameters of type NumberVector Modifier and Type Method Description boolean
ERiCNeighborPredicate.Instance. strongNeighbors(NumberVector v1, NumberVector v2, PCAFilteredResult pca1, PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this distance function.Method parameters in elki.clustering.dbscan.predicates with type arguments of type NumberVector Modifier and Type Method Description protected COPACNeighborPredicate.COPACModel
COPACNeighborPredicate. computeLocalModel(DBIDRef id, DoubleDBIDList knnneighbors, Relation<? extends NumberVector> relation)
COPAC model computationprotected PreDeConNeighborPredicate.PreDeConModel
FourCNeighborPredicate. computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends NumberVector> relation)
protected PreDeConNeighborPredicate.PreDeConModel
PreDeConNeighborPredicate. computeLocalModel(DBIDRef id, DoubleDBIDList neighbors, Relation<? extends NumberVector> relation)
COPACNeighborPredicate.Instance
COPACNeighborPredicate. instantiate(Relation<? extends NumberVector> relation)
Full instantiation method.ERiCNeighborPredicate.Instance
ERiCNeighborPredicate. instantiate(Relation<? extends NumberVector> relation)
Full instantiation interface.Constructor parameters in elki.clustering.dbscan.predicates with type arguments of type NumberVector Constructor Description Instance(DBIDs ids, DataStore<PCAFilteredResult> storage, Relation<? extends NumberVector> relation)
Constructor. -
Uses of NumberVector in elki.clustering.em
Method parameters in elki.clustering.em with type arguments of type NumberVector Modifier and Type Method Description private void
KDTreeEM.KDTree. aggregateStats(Relation<? extends NumberVector> relation, DBIDArrayIter iter, int dim)
Aggregate the statistics for a leaf node.private double[]
KDTreeEM. analyseDimWidth(Relation<? extends NumberVector> relation)
Helper method to retrieve the widths of all data in all dimensions.double
BetulaGMM. assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, java.util.List<? extends BetulaClusterModel> models, WritableDataStore<double[]> probClusterIGivenX)
Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.private void
KDTreeEM.KDTree. computeBoundingBox(Relation<? extends NumberVector> relation, DBIDArrayIter iter)
Compute the bounding box.Clustering<EMModel>
BetulaGMM. run(Relation<NumberVector> relation)
Run the clustering algorithm.Clustering<EMModel>
KDTreeEM. run(Relation<? extends NumberVector> relation)
Calculates the EM Clustering with the given values by calling makeStats and calculation the new models from the given resultsConstructor parameters in elki.clustering.em with type arguments of type NumberVector Constructor Description KDTree(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs sorted, int left, int right, double[] dimWidth, double mbw)
Constructor for a KDTree with statistics needed for KDTreeEM calculation. -
Uses of NumberVector in elki.clustering.em.models
Methods in elki.clustering.em.models with parameters of type NumberVector Modifier and Type Method Description double
DiagonalGaussianModel. estimateLogDensity(NumberVector vec)
double
MultivariateGaussianModel. estimateLogDensity(NumberVector vec)
double
SphericalGaussianModel. estimateLogDensity(NumberVector vec)
double
TextbookMultivariateGaussianModel. estimateLogDensity(NumberVector vec)
double
TextbookSphericalGaussianModel. estimateLogDensity(NumberVector vec)
double
TwoPassMultivariateGaussianModel. estimateLogDensity(NumberVector vec)
void
TwoPassMultivariateGaussianModel. firstPassE(NumberVector vec, double wei)
First pass: update the mean only.double
DiagonalGaussianModel. mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.double
MultivariateGaussianModel. mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.double
SphericalGaussianModel. mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.double
TextbookMultivariateGaussianModel. mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.double
TextbookSphericalGaussianModel. mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.double
TwoPassMultivariateGaussianModel. mahalanobisDistance(NumberVector vec)
Compute the Mahalanobis distance from the centroid for a given vector.void
DiagonalGaussianModel. updateE(NumberVector vec, double wei)
void
MultivariateGaussianModel. updateE(NumberVector vec, double wei)
void
SphericalGaussianModel. updateE(NumberVector vec, double wei)
void
TextbookMultivariateGaussianModel. updateE(NumberVector vec, double wei)
void
TextbookSphericalGaussianModel. updateE(NumberVector vec, double wei)
void
TwoPassMultivariateGaussianModel. updateE(NumberVector vec, double wei)
Second pass: compute the covariance matrix.Method parameters in elki.clustering.em.models with type arguments of type NumberVector Modifier and Type Method Description java.util.List<DiagonalGaussianModel>
DiagonalGaussianModelFactory. buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<MultivariateGaussianModel>
MultivariateGaussianModelFactory. buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<SphericalGaussianModel>
SphericalGaussianModelFactory. buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TextbookMultivariateGaussianModel>
TextbookMultivariateGaussianModelFactory. buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TextbookSphericalGaussianModel>
TextbookSphericalGaussianModelFactory. buildInitialModels(Relation<? extends NumberVector> relation, int k)
java.util.List<TwoPassMultivariateGaussianModel>
TwoPassMultivariateGaussianModelFactory. buildInitialModels(Relation<? extends NumberVector> relation, int k)
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Uses of NumberVector in elki.clustering.hierarchical
Classes in elki.clustering.hierarchical with type parameters of type NumberVector Modifier and Type Class Description class
LinearMemoryNNChain<O extends NumberVector>
NNchain clustering algorithm with linear memory, for particular linkages (that can be aggregated) and numerical vector data only.static class
LinearMemoryNNChain.Instance<O extends NumberVector>
Main worker instance of NNChain.static class
LinearMemoryNNChain.Par<O extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.clustering.hierarchical.birch
Methods in elki.clustering.hierarchical.birch with parameters of type NumberVector Modifier and Type Method Description protected void
ClusteringFeature. addToStatistics(NumberVector nv)
Add a number vector to the current node.protected double
BIRCHLloydKMeans. distance(NumberVector x, double[] y)
Compute a distance (and count the distance computations).private ClusteringFeature
CFTree. findLeaf(CFTree.TreeNode node, NumberVector nv)
Find the leaf of a cluster, to get the final cluster assignment.ClusteringFeature
CFTree. findLeaf(NumberVector nv)
Find the leaf of a cluster, to get the final cluster assignment.private CFTree.TreeNode
CFTree. insert(CFTree.TreeNode node, NumberVector nv)
Recursive insertion.void
CFTree. insert(NumberVector nv)
Insert a data point into the tree.double
BIRCHAbsorptionCriterion. squaredCriterion(ClusteringFeature f1, NumberVector n)
Quality of a CF when adding a data pointdouble
DiameterCriterion. squaredCriterion(ClusteringFeature f1, NumberVector n)
double
EuclideanDistanceCriterion. squaredCriterion(ClusteringFeature f1, NumberVector n)
double
RadiusCriterion. squaredCriterion(ClusteringFeature f1, NumberVector n)
double
AverageInterclusterDistance. squaredDistance(NumberVector v, ClusteringFeature cf)
double
AverageIntraclusterDistance. squaredDistance(NumberVector v, ClusteringFeature cf)
double
BIRCHDistance. squaredDistance(NumberVector v, ClusteringFeature cf)
Distance of a vector to a clustering feature.double
CentroidEuclideanDistance. squaredDistance(NumberVector v, ClusteringFeature cf)
double
CentroidManhattanDistance. squaredDistance(NumberVector v, ClusteringFeature cf)
double
VarianceIncreaseDistance. squaredDistance(NumberVector v, ClusteringFeature cf)
static double
ClusteringFeature. sumOfSquares(NumberVector v)
Compute the sum of squares of a vector.Method parameters in elki.clustering.hierarchical.birch with type arguments of type NumberVector Modifier and Type Method Description CFTree
CFTree.Factory. newTree(DBIDs ids, Relation<? extends NumberVector> relation)
Make a new tree.Clustering<MeanModel>
BIRCHLeafClustering. run(Relation<NumberVector> relation)
Run the clustering algorithm.Clustering<KMeansModel>
BIRCHLloydKMeans. run(Relation<NumberVector> relation)
Run the clustering algorithm. -
Uses of NumberVector in elki.clustering.kmeans
Classes in elki.clustering.kmeans with type parameters of type NumberVector Modifier and Type Class Description class
AbstractKMeans<V extends NumberVector,M extends Model>
Abstract base class for k-means implementations.static class
AbstractKMeans.Par<V extends NumberVector>
Parameterization class.class
AnnulusKMeans<V extends NumberVector>
Annulus k-means algorithm.static class
AnnulusKMeans.Par<V extends NumberVector>
Parameterization class.class
BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
Run K-Means multiple times, and keep the best run.static class
BestOfMultipleKMeans.Par<V extends NumberVector,M extends MeanModel>
Parameterization class.class
BisectingKMeans<V extends NumberVector,M extends MeanModel>
The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.static class
BisectingKMeans.Par<V extends NumberVector,M extends MeanModel>
Parameterization class.class
CompareMeans<V extends NumberVector>
Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.static class
CompareMeans.Par<V extends NumberVector>
Parameterization class.class
ElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.static class
ElkanKMeans.Par<V extends NumberVector>
Parameterization class.class
ExponionKMeans<V extends NumberVector>
Newlings's Exponion k-means algorithm, exploiting the triangle inequality.static class
ExponionKMeans.Par<V extends NumberVector>
Parameterization class.class
FuzzyCMeans<V extends NumberVector>
Fuzzy Clustering developed by Dunn and revisited by Bezdekclass
GMeans<V extends NumberVector,M extends MeanModel>
G-Means extends K-Means and estimates the number of centers with Anderson Darling Test.
Implemented as specialization of XMeans.static class
GMeans.Par<V extends NumberVector,M extends MeanModel>
Parameterization class.class
HamerlyKMeans<V extends NumberVector>
Hamerly's fast k-means by exploiting the triangle inequality.static class
HamerlyKMeans.Par<V extends NumberVector>
Parameterization class.class
HartiganWongKMeans<V extends NumberVector>
Hartigan and Wong k-means clustering.static class
HartiganWongKMeans.Parameterizer<V extends NumberVector>
Parameterization class.class
KDTreeFilteringKMeans<V extends NumberVector>
Filtering or "blacklisting" K-means with k-d-tree acceleration.static class
KDTreeFilteringKMeans.Par<V extends NumberVector>
Parameterization class.class
KDTreePruningKMeans<V extends NumberVector>
Pruning K-means with k-d-tree acceleration.static class
KDTreePruningKMeans.Par<V extends NumberVector>
Parameterization class.interface
KMeans<V extends NumberVector,M extends Model>
Some constants and options shared among kmeans family algorithms.class
KMeansMinusMinus<V extends NumberVector>
k-means--: A Unified Approach to Clustering and Outlier Detection.static class
KMeansMinusMinus.Par<V extends NumberVector>
Parameterization class.class
KMediansLloyd<V extends NumberVector>
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (seePAM
instead).static class
KMediansLloyd.Par<V extends NumberVector>
Parameterization class.class
LloydKMeans<V extends NumberVector>
The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).static class
LloydKMeans.Par<V extends NumberVector>
Parameterization class.class
MacQueenKMeans<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.static class
MacQueenKMeans.Par<V extends NumberVector>
Parameterization class.class
ShallotKMeans<V extends NumberVector>
Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.static class
ShallotKMeans.Par<V extends NumberVector>
Parameterization class.class
SimplifiedElkanKMeans<V extends NumberVector>
Simplified version of Elkan's k-means by exploiting the triangle inequality.static class
SimplifiedElkanKMeans.Par<V extends NumberVector>
Parameterization class.class
SingleAssignmentKMeans<V extends NumberVector>
Pseudo-k-means variations, that assigns each object to the nearest center.static class
SingleAssignmentKMeans.Par<V extends NumberVector>
Parameterization class.class
SortMeans<V extends NumberVector>
Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).static class
SortMeans.Par<V extends NumberVector>
Parameterization class.class
XMeans<V extends NumberVector,M extends MeanModel>
X-means: Extending K-means with Efficient Estimation on the Number of Clusters.static class
XMeans.Par<V extends NumberVector,M extends MeanModel>
Parameterization class.class
YinYangKMeans<V extends NumberVector>
Yin-Yang k-Means Clustering.static class
YinYangKMeans.Par<V extends NumberVector>
Parameterization class.Fields in elki.clustering.kmeans with type parameters of type NumberVector Modifier and Type Field Description protected Relation<? extends NumberVector>
AbstractKMeans.Instance. relation
Data relation.Methods in elki.clustering.kmeans that return types with arguments of type NumberVector Modifier and Type Method Description FuzzyCMeans<NumberVector>
FuzzyCMeans.Par. make()
Methods in elki.clustering.kmeans with parameters of type NumberVector Modifier and Type Method Description private double
HartiganWongKMeans.Instance. cacheR1(DBIDIter it, NumberVector vec, int l1)
Compute and cache the R1 value.private static void
AbstractKMeans. densePlusEquals(double[] sum, NumberVector vec)
Similar to VMath.plusEquals, but accepts a number vector.private static void
AbstractKMeans. densePlusMinusEquals(double[] add, double[] sub, NumberVector vec)
Add to one, remove from another.protected double
AbstractKMeans.Instance. distance(NumberVector x, double[] y)
Compute the squared distance (and count the distance computations).protected double
AbstractKMeans.Instance. distance(NumberVector x, NumberVector y)
Compute the squared distance (and count the distance computations).private double
BetulaLloydKMeans. distance(NumberVector x, double[] y)
Updates statistics and calculates distance between two Objects based on selected criteria.protected static void
AbstractKMeans. incrementalUpdateMean(double[] mean, NumberVector vec, int newsize, double op)
Compute an incremental update for the mean.static void
AbstractKMeans. minusEquals(double[] sum, NumberVector vec)
Similar to VMath.minusEquals, but accepts a number vector.static void
AbstractKMeans. plusEquals(double[] sum, NumberVector vec)
Similar to VMath.plusEquals, but accepts a number vector.static void
AbstractKMeans. plusMinusEquals(double[] add, double[] sub, NumberVector vec)
Add to one, remove from another.protected double
AbstractKMeans.Instance. sqrtdistance(NumberVector x, double[] y)
Compute the distance (and count the distance computations).protected double
AbstractKMeans.Instance. sqrtdistance(NumberVector x, NumberVector y)
Compute the distance (and count the distance computations).private void
HartiganWongKMeans.Instance. transfer(DBIDRef it, NumberVector vec, int l1, int l2)
Transfer a point from one cluster to another.private boolean
MacQueenKMeans.Instance. updateMeanAndAssignment(int minIndex, NumberVector fv, DBIDIter iditer)
Try to update the cluster assignment.Method parameters in elki.clustering.kmeans with type arguments of type NumberVector Modifier and Type Method Description Clustering<KMeansModel>
AbstractKMeans.Instance. buildResult(boolean varstat, Relation<? extends NumberVector> relation)
Build the result, recomputing the cluster variance ifvarstat
is set to true.protected KDTreePruningKMeans.KDNode
KDTreePruningKMeans.Instance. buildTreeBoundedMidpoint(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
Build the k-d-tree using bounded midpoint splitting.protected KDTreePruningKMeans.KDNode
KDTreePruningKMeans.Instance. buildTreeMedian(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
Build the k-d-tree using median splitting.protected KDTreePruningKMeans.KDNode
KDTreePruningKMeans.Instance. buildTreeMidpoint(Relation<? extends NumberVector> relation, int left, int right)
Build the k-d-tree using midpoint splitting.protected KDTreePruningKMeans.KDNode
KDTreePruningKMeans.Instance. buildTreeSSQ(Relation<? extends NumberVector> relation, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
Build the k-d-tree using a variance-based splitting strategy.private static double[][]
AbstractKMeans. denseMeans(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)
Returns the mean vectors of the given clusters in the given database.protected static double[][]
AbstractKMeans. means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)
Returns the mean vectors of the given clusters in the given database.protected double[][]
KMediansLloyd.Instance. medians(java.util.List<? extends DBIDs> clusters, double[][] medians, Relation<? extends NumberVector> relation)
Returns the median vectors of the given clusters in the given database.protected void
AbstractKMeans.Instance. recomputeVariance(Relation<? extends NumberVector> relation)
Recompute the cluster variances.Clustering<KMeansModel>
BetulaLloydKMeans. run(Relation<NumberVector> relation)
Run the clustering algorithm.Constructor parameters in elki.clustering.kmeans with type arguments of type NumberVector Constructor Description Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means, int t)
Constructor.KDNode(Relation<? extends NumberVector> relation, DBIDArrayIter iter, int start, int end)
Constructor. -
Uses of NumberVector in elki.clustering.kmeans.initialization
Classes in elki.clustering.kmeans.initialization with type parameters of type NumberVector Modifier and Type Class Description static class
FirstK.Par<V extends NumberVector>
Parameterization class.class
SampleKMeans<V extends NumberVector>
Initialize k-means by running k-means on a sample of the data set only.static class
SampleKMeans.Par<V extends NumberVector>
Parameterization class.Fields in elki.clustering.kmeans.initialization with type parameters of type NumberVector Modifier and Type Field Description protected Relation<? extends NumberVector>
KMC2.Instance. relation
Data relation.protected Relation<? extends NumberVector>
KMeansPlusPlus.NumberVectorInstance. relation
Data relation.protected Relation<? extends NumberVector>
SphericalKMeansPlusPlus.Instance. relation
Data relation.Methods in elki.clustering.kmeans.initialization with parameters of type NumberVector Modifier and Type Method Description protected double
KMC2.Instance. distance(NumberVector a, DBIDRef b)
Compute the distance of two objects.protected double
KMeansPlusPlus.NumberVectorInstance. distance(NumberVector a, DBIDRef b)
protected double
KMC2.Instance. initialWeights(NumberVector first)
Initialize the weight list.protected double
SphericalAFKMC2.Instance. initialWeights(NumberVector first)
protected double
SphericalKMeansPlusPlus.Instance. initialWeights(NumberVector first)
Initialize the weight list.protected double
SphericalAFKMC2.Instance. similarity(NumberVector a, DBIDRef b)
Compute the distance of two objects.protected double
SphericalKMeansPlusPlus.Instance. similarity(NumberVector a, DBIDRef b)
Compute the distance of two objects.protected double
SphericalKMeansPlusPlus.Instance. updateWeights(NumberVector latest)
Update the weight list.Method parameters in elki.clustering.kmeans.initialization with type arguments of type NumberVector Modifier and Type Method Description double[][]
AFKMC2. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
FarthestPoints. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
FarthestSumPoints. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
FirstK. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
KMC2. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
KMeansInitialization. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
Choose initial meansdouble[][]
KMeansPlusPlus. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
Ostrovsky. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
Predefined. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
RandomlyChosen. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
RandomNormalGenerated. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
RandomUniformGenerated. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
SampleKMeans. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
SphericalAFKMC2. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
SphericalKMeansPlusPlus. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
protected void
KMC2.Instance. chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)
Choose remaining means, weighted by distance.protected void
KMeansPlusPlus.NumberVectorInstance. chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)
Choose remaining means, weighted by distance.protected void
SphericalKMeansPlusPlus.Instance. chooseRemaining(int k, java.util.List<NumberVector> means, double weightsum)
Choose remaining means, weighted by distance.protected double
KMC2.Instance. distance(DBIDRef cand, java.util.List<NumberVector> means)
Minimum distance to the current means.protected double
SphericalAFKMC2.Instance. distance(DBIDRef cand, java.util.List<NumberVector> means)
double[][]
Ostrovsky.NumberVectorInstance. run(Relation<? extends NumberVector> relation, int k)
static double[][]
AbstractKMeansInitialization. unboxVectors(java.util.List<? extends NumberVector> means)
Unbox database means to primitive means.Constructor parameters in elki.clustering.kmeans.initialization with type arguments of type NumberVector Constructor Description Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)
Constructor.Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)
Constructor.Instance(Relation<? extends NumberVector> relation, int m, double alpha, RandomFactory rnd)
Constructor.Instance(Relation<? extends NumberVector> relation, double alpha, RandomFactory rnd)
Constructor.NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)
Constructor.NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)
Constructor. -
Uses of NumberVector in elki.clustering.kmeans.parallel
Classes in elki.clustering.kmeans.parallel with type parameters of type NumberVector Modifier and Type Class Description class
KMeansProcessor<V extends NumberVector>
Parallel k-means implementation.static class
KMeansProcessor.Instance<V extends NumberVector>
Instance to process part of the data set, for a single iteration.class
ParallelLloydKMeans<V extends NumberVector>
Parallel implementation of k-Means clustering.static class
ParallelLloydKMeans.Par<V extends NumberVector>
Parameterization class -
Uses of NumberVector in elki.clustering.kmeans.quality
Classes in elki.clustering.kmeans.quality with type parameters of type NumberVector Modifier and Type Class Description class
AbstractKMeansQualityMeasure<O extends NumberVector>
Base class for evaluating clusterings by information criteria (such as AIC or BIC).interface
KMeansQualityMeasure<O extends NumberVector>
Interface for computing the quality of a K-Means clustering.Methods in elki.clustering.kmeans.quality with type parameters of type NumberVector Modifier and Type Method Description <V extends NumberVector>
doubleAkaikeInformationCriterion. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector>
doubleAkaikeInformationCriterionXMeans. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector>
doubleBayesianInformationCriterion. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector>
doubleBayesianInformationCriterionXMeans. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector>
doubleBayesianInformationCriterionZhao. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector>
doubleWithinClusterMeanDistance. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector>
doubleWithinClusterVariance. quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
Method parameters in elki.clustering.kmeans.quality with type arguments of type NumberVector Modifier and Type Method Description static double
AbstractKMeansQualityMeasure. logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)
Computes log likelihood of an entire clustering.static double
BayesianInformationCriterionXMeans. logLikelihoodXMeans(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)
Computes log likelihood of an entire clustering.static double
BayesianInformationCriterionZhao. logLikelihoodZhao(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)
Computes log likelihood of an entire clustering.static int
AbstractKMeansQualityMeasure. numberOfFreeParameters(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering)
Compute the number of free parameters.static double
AbstractKMeansQualityMeasure. varianceContributionOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistance<?> distance, Relation<? extends NumberVector> relation)
Variance contribution of a single cluster. -
Uses of NumberVector in elki.clustering.kmeans.spherical
Classes in elki.clustering.kmeans.spherical with type parameters of type NumberVector Modifier and Type Class Description class
EuclideanSphericalElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.static class
EuclideanSphericalElkanKMeans.Par<V extends NumberVector>
Parameterization class.class
EuclideanSphericalHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.static class
EuclideanSphericalHamerlyKMeans.Par<V extends NumberVector>
Parameterization class.class
EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.static class
EuclideanSphericalSimplifiedElkanKMeans.Par<V extends NumberVector>
Parameterization class.class
SphericalElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.static class
SphericalElkanKMeans.Par<V extends NumberVector>
Parameterization class.class
SphericalHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.static class
SphericalHamerlyKMeans.Par<V extends NumberVector>
Parameterization class.class
SphericalKMeans<V extends NumberVector>
The standard spherical k-means algorithm.static class
SphericalKMeans.Par<V extends NumberVector>
Parameterization class.class
SphericalSimplifiedElkanKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.static class
SphericalSimplifiedElkanKMeans.Par<V extends NumberVector>
Parameterization class.class
SphericalSimplifiedHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.static class
SphericalSimplifiedHamerlyKMeans.Par<V extends NumberVector>
Parameterization class.class
SphericalSingleAssignmentKMeans<V extends NumberVector>
Pseudo-k-Means variations, that assigns each object to the nearest center.static class
SphericalSingleAssignmentKMeans.Par<V extends NumberVector>
Parameterization class.Methods in elki.clustering.kmeans.spherical with parameters of type NumberVector Modifier and Type Method Description protected double
SphericalKMeans.Instance. distance(NumberVector x, double[] y)
protected double
SphericalKMeans.Instance. distance(NumberVector x, NumberVector y)
protected double
SphericalKMeans.Instance. similarity(NumberVector vec1, double[] vec2)
Compute the similarity of two objects (and count this operation).protected double
SphericalKMeans.Instance. sqrtdistance(NumberVector x, double[] y)
protected double
SphericalKMeans.Instance. sqrtdistance(NumberVector x, NumberVector y)
Method parameters in elki.clustering.kmeans.spherical with type arguments of type NumberVector Modifier and Type Method Description protected static double[][]
SphericalKMeans.Instance. means(java.util.List<? extends DBIDs> clusters, double[][] means, Relation<? extends NumberVector> relation)
Returns the mean vectors of the given clusters in the given database.protected void
SphericalKMeans.Instance. recomputeVariance(Relation<? extends NumberVector> relation)
Constructor parameters in elki.clustering.kmeans.spherical with type arguments of type NumberVector Constructor Description Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor.Instance(Relation<? extends NumberVector> relation, double[][] means)
Constructor. -
Uses of NumberVector in elki.clustering.kmedoids.initialization
Method parameters in elki.clustering.kmedoids.initialization with type arguments of type NumberVector Modifier and Type Method Description double[][]
BUILD. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
LAB. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]
ParkJun. chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
-
Uses of NumberVector in elki.clustering.onedimensional
Method parameters in elki.clustering.onedimensional with type arguments of type NumberVector Modifier and Type Method Description Clustering<ClusterModel>
KNNKernelDensityMinimaClustering. run(Relation<? extends NumberVector> relation)
Run the clustering algorithm on a data relation. -
Uses of NumberVector in elki.clustering.optics
Classes in elki.clustering.optics with type parameters of type NumberVector Modifier and Type Class Description class
DeLiClu<V extends NumberVector>
DeliClu: Density-Based Hierarchical Clusteringstatic class
DeLiClu.Par<V extends NumberVector>
Parameterization class.class
FastOPTICS<V extends NumberVector>
FastOPTICS algorithm (Fast approximation of OPTICS)static class
FastOPTICS.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.clustering.subspace
Classes in elki.clustering.subspace with type parameters of type NumberVector Modifier and Type Class Description class
SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.static class
SUBCLU.Par<V extends NumberVector>
Parameterization class.Fields in elki.clustering.subspace with type parameters of type NumberVector Modifier and Type Field Description private Relation<? extends NumberVector>
DiSH.Instance. relation
Data relation.private Relation<? extends NumberVector>
HiSC.Instance. relation
Data relation.Methods in elki.clustering.subspace with type parameters of type NumberVector Modifier and Type Method Description <V extends NumberVector>
Clustering<SubspaceModel>PROCLUS. run(Relation<V> relation)
Performs the PROCLUS algorithm on the given database.Methods in elki.clustering.subspace with parameters of type NumberVector Modifier and Type Method Description private double
PROCLUS. manhattanSegmentalDistance(NumberVector o1, double[] o2, long[] dimensions)
Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions.private double
PROCLUS. manhattanSegmentalDistance(NumberVector o1, NumberVector o2, long[] dimensions)
Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions.private int
DiSH. subspaceDimensionality(NumberVector v1, NumberVector v2, long[] pv1, long[] pv2, long[] commonPreferenceVector)
Compute the common subspace dimensionality of two vectors.private void
CLIQUE. updateMinMax(NumberVector featureVector, double[] minima, double[] maxima)
Updates the minima and maxima array according to the specified feature vector.protected static double
DiSH. weightedDistance(NumberVector v1, NumberVector v2, long[] weightVector)
Computes the weighted distance between the two specified vectors according to the given preference vector.double
HiSC. weightedDistance(NumberVector v1, NumberVector v2, long[] weightVector)
Computes the weighted distance between the two specified vectors according to the given preference vector.Method parameters in elki.clustering.subspace with type arguments of type NumberVector Modifier and Type Method Description private java.util.ArrayList<PROCLUS.PROCLUSCluster>
PROCLUS. assignPoints(ArrayDBIDs m_current, long[][] dimensions, Relation<? extends NumberVector> database)
Assigns the objects to the clusters.private void
P3C. assignUnassigned(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, ModifiableDBIDs unassigned)
Assign unassigned objects to best candidate based on shortest Mahalanobis distance.private double
PROCLUS. avgDistance(double[] centroid, DBIDs objectIDs, Relation<? extends NumberVector> database, int dimension)
Computes the average distance of the objects to the centroid along the specified dimension.private void
DiSH. buildHierarchy(Relation<? extends NumberVector> database, Clustering<SubspaceModel> clustering, java.util.List<Cluster<SubspaceModel>> clusters, int dimensionality)
Builds the cluster hierarchy.private void
DiSH. checkClusters(Relation<? extends NumberVector> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)
Removes the clusters with size < minpts from the cluster map and adds them to their parents.private Clustering<SubspaceModel>
DiSH. computeClusters(Relation<? extends NumberVector> database, DiSH.DiSHClusterOrder clusterOrder)
Computes the hierarchical clusters according to the cluster order.private void
P3C. computeFuzzyMembership(Relation<? extends NumberVector> relation, java.util.ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, java.util.List<MultivariateGaussianModel> models, int dim)
Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.protected boolean
DOC. dimensionIsRelevant(int dimension, Relation<? extends NumberVector> relation, DBIDs points)
Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).private double
PROCLUS. evaluateClusters(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, long[][] dimensions, Relation<? extends NumberVector> database)
Evaluates the quality of the clusters.private it.unimi.dsi.fastutil.objects.Object2ObjectOpenCustomHashMap<long[],java.util.List<ArrayModifiableDBIDs>>
DiSH. extractClusters(Relation<? extends NumberVector> relation, DiSH.DiSHClusterOrder clusterOrder)
Extracts the clusters from the cluster order.private java.util.List<PROCLUS.PROCLUSCluster>
PROCLUS. finalAssignment(java.util.List<Pair<double[],long[]>> dimensions, Relation<? extends NumberVector> database)
Refinement step to assign the objects to the final clusters.private java.util.List<CLIQUESubspace>
CLIQUE. findDenseSubspaceCandidates(Relation<? extends NumberVector> database, java.util.List<CLIQUESubspace> denseSubspaces)
Determines thek
-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 thek
-dimensional dense subspaces and performs a pruning if this option is chosen.private long[][]
PROCLUS. findDimensions(ArrayDBIDs medoids, Relation<? extends NumberVector> relation, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)
Determines the set of correlated dimensions for each medoid in the specified medoid set.private long[][]
PROCLUS. findDimensions(ArrayDBIDs medoids, Relation<? extends NumberVector> relation, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)
Determines the set of correlated dimensions for each medoid in the specified medoid set.private java.util.List<Pair<double[],long[]>>
PROCLUS. findDimensions(java.util.ArrayList<PROCLUS.PROCLUSCluster> clusters, Relation<? extends NumberVector> database)
Refinement step that determines the set of correlated dimensions for each cluster centroid.protected DBIDs
DOC. findNeighbors(DBIDRef q, long[] nD, ArrayModifiableDBIDs S, Relation<? extends NumberVector> relation)
Find the neighbors of point q in the given subspaceprivate java.util.List<CLIQUESubspace>
CLIQUE. findOneDimensionalDenseSubspaceCandidates(Relation<? extends NumberVector> database)
Determines the one-dimensional dense subspace candidates by making a pass over the database.private java.util.List<CLIQUESubspace>
CLIQUE. findOneDimensionalDenseSubspaces(Relation<? extends NumberVector> database)
Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.private void
P3C. findOutliers(Relation<? extends NumberVector> relation, java.util.List<MultivariateGaussianModel> models, java.util.ArrayList<P3C.ClusterCandidate> clusterCandidates, ModifiableDBIDs noise)
Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.private Pair<long[],ArrayModifiableDBIDs>
DiSH. findParent(Relation<? extends NumberVector> relation, Pair<long[],ArrayModifiableDBIDs> child, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)
Returns the parent of the specified clusterprivate DataStore<DBIDs>
PROCLUS. getLocalities(DBIDs medoids, DistanceQuery<? extends NumberVector> distance, RangeSearcher<DBIDRef> rangeQuery)
Computes the localities of the specified medoids: for each medoid m the objects in the sphere centered at m with radius minDist are determined, where minDist is the minimum distance between medoid m and any other medoid m_i.private ArrayDBIDs
PROCLUS. greedy(DistanceQuery<? extends NumberVector> distance, DBIDs sampleSet, int m, java.util.Random random)
Returns a piercing set of k medoids from the specified sample set.private java.util.Collection<CLIQUEUnit>
CLIQUE. initOneDimensionalUnits(Relation<? extends NumberVector> database)
Initializes and returns the one dimensional units.private boolean
DiSH. isParent(Relation<? extends NumberVector> relation, Cluster<SubspaceModel> parent, It<Cluster<SubspaceModel>> iter, int db_dim)
Returns true, if the specified parent cluster is a parent of one child of the children clusters.protected Cluster<SubspaceModel>
DOC. makeCluster(Relation<? extends NumberVector> relation, DBIDs C, long[] D)
Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.private SetDBIDs[][]
P3C. partitionData(Relation<? extends NumberVector> relation, int bins)
Partition the data set intobins
bins in each dimension independently.Clustering<SubspaceModel>
CLIQUE. run(Relation<? extends NumberVector> relation)
Performs the CLIQUE algorithm on the given database.Clustering<SubspaceModel>
DiSH. run(Relation<? extends NumberVector> relation)
Performs the DiSH algorithm on the given database.Clustering<SubspaceModel>
DOC. run(Relation<? extends NumberVector> relation)
Performs the DOC or FastDOC (as configured) algorithm.ClusterOrder
HiSC. run(Relation<? extends NumberVector> relation)
Run the HiSC algorithmClustering<SubspaceModel>
P3C. run(Relation<? extends NumberVector> relation)
Performs the P3C algorithm on the given Database.protected Cluster<SubspaceModel>
DOC. runDOC(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize)
Performs a single run of DOC, finding a single cluster.protected Cluster<SubspaceModel>
FastDOC. runDOC(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize)
Performs a single run of FastDOC, finding a single cluster.private java.util.List<Cluster<SubspaceModel>>
DiSH. sortClusters(Relation<? extends NumberVector> relation, it.unimi.dsi.fastutil.objects.Object2ObjectMap<long[],java.util.List<ArrayModifiableDBIDs>> clustersMap)
Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.Constructor parameters in elki.clustering.subspace with type arguments of type NumberVector Constructor Description Instance(Relation<? extends NumberVector> relation)
Constructor.Instance(Relation<? extends NumberVector> relation)
Constructor. -
Uses of NumberVector in elki.clustering.subspace.clique
Methods in elki.clustering.subspace.clique with parameters of type NumberVector Modifier and Type Method Description boolean
CLIQUEUnit. addFeatureVector(DBIDRef id, NumberVector vector)
Adds the id of the specified feature vector to this unit, if this unit contains the feature vector.boolean
CLIQUEUnit. contains(NumberVector vector)
Returns true, if the intervals of this unit contain the specified feature vector. -
Uses of NumberVector in elki.clustering.svm
Fields in elki.clustering.svm with type parameters of type NumberVector Modifier and Type Field Description (package private) PrimitiveSimilarity<? super NumberVector>
SupportVectorClustering. kernel
Kernel function.protected PrimitiveSimilarity<? super NumberVector>
SupportVectorClustering.Par. kernel
Kernel in use.Methods in elki.clustering.svm with parameters of type NumberVector Modifier and Type Method Description private boolean
SupportVectorClustering. accept(NumberVector cur, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_square)
evaluate if a point cur is inside the sphere in kernel space.Method parameters in elki.clustering.svm with type arguments of type NumberVector Modifier and Type Method Description private boolean
SupportVectorClustering. accept(NumberVector cur, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_square)
evaluate if a point cur is inside the sphere in kernel space.private double
SupportVectorClustering. calcfixedpart(RegressionModel model, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim)
calculate fixed part of model evaluationprivate boolean
SupportVectorClustering. checkConnectivity(Relation<NumberVector> relation, double[] start, DBIDRef destRef, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_squared)
Checks if the connecting line between start and dest lies inside the kernel space sphere.private boolean
SupportVectorClustering. checkConnectivity(Relation<NumberVector> relation, double[] start, DBIDRef destRef, RegressionModel model, double fixed, ArrayDBIDs ids, SimilarityQuery<NumberVector> sim, double r_squared)
Checks if the connecting line between start and dest lies inside the kernel space sphere.Clustering<Model>
SupportVectorClustering. run(Relation<NumberVector> relation)
perform clusteringConstructor parameters in elki.clustering.svm with type arguments of type NumberVector Constructor Description SupportVectorClustering(PrimitiveSimilarity<? super NumberVector> kernel, double C)
Constructor. -
Uses of NumberVector in elki.clustering.uncertain
Methods in elki.clustering.uncertain with parameters of type NumberVector Modifier and Type Method Description protected double
UKMeans. getExpectedRepDistance(NumberVector rep, DiscreteUncertainObject uo)
Get expected distance between a Vector and an uncertain objectConstructor parameters in elki.clustering.uncertain with type arguments of type NumberVector Constructor Description CKMeans(NumberVectorDistance<? super NumberVector> distance, int k, int maxiter, KMeansInitialization initializer)
Constructor that uses Lloyd's k-means algorithm. -
Uses of NumberVector in elki.data
Classes in elki.data with type parameters of type NumberVector Modifier and Type Interface Description static interface
NumberVector.Factory<V extends NumberVector>
Factory API for this feature vector.Subinterfaces of NumberVector in elki.data Modifier and Type Interface Description interface
SparseNumberVector
Combines the SparseFeatureVector and NumberVector.Classes in elki.data that implement NumberVector Modifier and Type Class Description class
BitVector
Vector using a dense bit set encoding, based onlong[]
storage.class
ByteVector
Vector usingbyte[]
storage.class
DoubleVector
Vector type usingdouble[]
storage for real numbers.class
FloatVector
Vector type usingfloat[]
storage, thus needing approximately half as much memory asDoubleVector
.class
IntegerVector
Vector type usingint[]
storage.class
OneDimensionalDoubleVector
Specialized class implementing a one-dimensional double vector without using an array.class
ShortVector
Vector type usingshort[]
storage.class
SparseByteVector
Sparse vector type, usingbyte[]
for storing the values, andint[]
for storing the indexes, approximately 5 bytes per non-zero value (limited to -128..+127).class
SparseDoubleVector
Sparse vector type, usingdouble[]
for storing the values, andint[]
for storing the indexes, approximately 12 bytes per non-zero value.class
SparseFloatVector
Sparse vector type, usingfloat[]
for storing the values, andint[]
for storing the indexes, approximately 8 bytes per non-zero value.class
SparseIntegerVector
Sparse vector type, usingint[]
for storing the values, andint[]
for storing the indexes, approximately 8 bytes per non-zero integer value.class
SparseShortVector
Sparse vector type, usingshort[]
for storing the values, andint[]
for storing the indexes, approximately 6 bytes per non-zero value.Fields in elki.data with type parameters of type NumberVector Modifier and Type Field Description private Relation<? extends NumberVector>
VectorUtil.SortDBIDsBySingleDimension. data
The relation to sort.static VectorFieldTypeInformation<NumberVector>
NumberVector. FIELD
Input type for algorithms that require number vector fields.static VectorFieldTypeInformation<NumberVector>
NumberVector. FIELD_1D
Type request for two-dimensional number vectorsstatic VectorFieldTypeInformation<NumberVector>
NumberVector. FIELD_2D
Type request for two-dimensional number vectorsstatic VectorTypeInformation<NumberVector>
NumberVector. VARIABLE_LENGTH
Number vectors of variable length.Methods in elki.data with type parameters of type NumberVector Modifier and Type Method Description static <V extends NumberVector>
VVectorUtil. project(V v, long[] selectedAttributes, NumberVector.Factory<V> factory)
Project a number vector to the specified attributes.static <V extends NumberVector>
VVectorUtil. randomVector(NumberVector.Factory<V> factory, int dim)
Produce a new vector based on random numbers in [0:1].static <V extends NumberVector>
VVectorUtil. randomVector(NumberVector.Factory<V> factory, int dim, java.util.Random r)
Produce a new vector based on random numbers in [0:1].Methods in elki.data with parameters of type NumberVector Modifier and Type Method Description static double
VectorUtil. angle(NumberVector v1, NumberVector v2, NumberVector o)
Compute the angle between two vectors with respect to a reference point.static double
VectorUtil. angleDense(NumberVector v1, NumberVector v2)
Compute the absolute cosine of the angle between two dense vectors.static double
VectorUtil. angleSparseDense(SparseNumberVector v1, NumberVector v2)
Compute the angle for a sparse and a dense vector.int
VectorUtil.SortVectorsBySingleDimension. compare(NumberVector o1, NumberVector o2)
static double
VectorUtil. cosAngle(NumberVector v1, NumberVector v2)
Compute the absolute cosine of the angle between two vectors.static double
VectorUtil. dot(NumberVector v1, double[] v2)
Compute the dot product of the angle between two vectors.static double
VectorUtil. dot(NumberVector v1, NumberVector v2)
Compute the dot product of the angle between two vectors.static double
VectorUtil. dotDense(NumberVector v1, double[] v2)
Compute the dot product of two dense vectors.static double
VectorUtil. dotDense(NumberVector v1, NumberVector v2)
Compute the dot product of two dense vectors.static double
VectorUtil. dotSparseDense(SparseNumberVector v1, NumberVector v2)
Compute the dot product for a sparse and a dense vector.default V
NumberVector.Factory. newNumberVector(NumberVector values)
Returns a new NumberVector of N for the given values.Constructor parameters in elki.data with type arguments of type NumberVector Constructor Description SortDBIDsBySingleDimension(Relation<? extends NumberVector> data)
Constructor.SortDBIDsBySingleDimension(Relation<? extends NumberVector> data, int dim)
Constructor. -
Uses of NumberVector in elki.data.model
Methods in elki.data.model with type parameters of type NumberVector Modifier and Type Method Description static <V extends NumberVector>
VModelUtil. getPrototype(Model model, Relation<? extends V> relation, NumberVector.Factory<V> factory)
Get (and convert!)static <V extends NumberVector>
VModelUtil. getPrototypeOrCentroid(Model model, Relation<? extends V> relation, DBIDs ids, NumberVector.Factory<V> factory)
Get the representative vector for a cluster model, or compute the centroid.Methods in elki.data.model that return NumberVector Modifier and Type Method Description static NumberVector
ModelUtil. getPrototype(Model model, Relation<? extends NumberVector> relation)
Get the representative vector for a cluster model.static NumberVector
ModelUtil. getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids)
Get the representative vector for a cluster model, or compute the centroid.Methods in elki.data.model with parameters of type NumberVector Modifier and Type Method Description double[]
CorrelationAnalysisSolution. dataVector(NumberVector p)
Returns the data vectors after projection.double[]
CorrelationAnalysisSolution. errorVector(NumberVector p)
Returns the error vectors after projection.double
CorrelationAnalysisSolution. squaredDistance(NumberVector p)
Returns the distance of NumberVector p from the hyperplane underlying this solution.Method parameters in elki.data.model with type arguments of type NumberVector Modifier and Type Method Description static NumberVector
ModelUtil. getPrototype(Model model, Relation<? extends NumberVector> relation)
Get the representative vector for a cluster model.static NumberVector
ModelUtil. getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids)
Get the representative vector for a cluster model, or compute the centroid.Constructor parameters in elki.data.model with type arguments of type NumberVector Constructor Description CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<? extends NumberVector> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid)
Provides a new CorrelationAnalysisSolution holding the specified matrix.CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<? extends NumberVector> db, double[][] strongEigenvectors, double[][] weakEigenvectors, double[][] similarityMatrix, double[] centroid, java.text.NumberFormat nf)
Provides a new CorrelationAnalysisSolution holding the specified matrix and number format. -
Uses of NumberVector in elki.data.projection
Classes in elki.data.projection with type parameters of type NumberVector Modifier and Type Class Description class
LatLngToECEFProjection<V extends NumberVector>
Project (Latitude, Longitude) vectors to (X, Y, Z), from spherical coordinates to ECEF (earth-centered earth-fixed).class
LngLatToECEFProjection<V extends NumberVector>
Project (Longitude, Latitude) vectors to (X, Y, Z), from spherical coordinates to ECEF (earth-centered earth-fixed).class
NumericalFeatureSelection<V extends NumberVector>
Projection class for number vectors.static class
NumericalFeatureSelection.Par<V extends NumberVector>
Parameterization class.class
RandomProjection<V extends NumberVector>
Randomized projections of the data.Methods in elki.data.projection that return types with arguments of type NumberVector Modifier and Type Method Description LatLngToECEFProjection<NumberVector>
LatLngToECEFProjection.Par. make()
LngLatToECEFProjection<NumberVector>
LngLatToECEFProjection.Par. make()
RandomProjection<NumberVector>
RandomProjection.Par. make()
Methods in elki.data.projection with parameters of type NumberVector Modifier and Type Method Description java.lang.Double
FeatureSelection.ProjectedNumberFeatureVectorAdapter. get(NumberVector array, int off)
double
FeatureSelection.ProjectedNumberFeatureVectorAdapter. getDouble(NumberVector array, int off)
long
FeatureSelection.ProjectedNumberFeatureVectorAdapter. getLong(NumberVector array, int off)
int
FeatureSelection.ProjectedNumberFeatureVectorAdapter. size(NumberVector array)
-
Uses of NumberVector in elki.data.projection.random
Methods in elki.data.projection.random with parameters of type NumberVector Modifier and Type Method Description double[]
AbstractRandomProjectionFamily.MatrixProjection. project(NumberVector in)
double[]
AbstractRandomProjectionFamily.MatrixProjection. project(NumberVector in, double[] ret)
double[]
RandomProjectionFamily.Projection. project(NumberVector in)
Project a single vector.double[]
RandomProjectionFamily.Projection. project(NumberVector in, double[] buffer)
Project a single vector, into the given buffer.double[]
RandomSubsetProjectionFamily.SubsetProjection. project(NumberVector in)
double[]
RandomSubsetProjectionFamily.SubsetProjection. project(NumberVector in, double[] buffer)
double[]
SimplifiedRandomHyperplaneProjectionFamily.SignedProjection. project(NumberVector in)
double[]
SimplifiedRandomHyperplaneProjectionFamily.SignedProjection. project(NumberVector vec, double[] ret)
private double[]
SimplifiedRandomHyperplaneProjectionFamily.SignedProjection. projectDense(NumberVector in, double[] ret)
Slower version, for dense multiplication. -
Uses of NumberVector in elki.data.type
Fields in elki.data.type with type parameters of type NumberVector Modifier and Type Field Description static MultivariateSeriesTypeInformation<NumberVector>
TypeUtil. MULTIVARIATE_SERIES
Type request for multivariate time series.static VectorFieldTypeInformation<NumberVector>
TypeUtil. NUMBER_VECTOR_FIELD
Input type for algorithms that require number vector fields.static VectorFieldTypeInformation<? super NumberVector>
TypeUtil. NUMBER_VECTOR_FIELD_1D
Type request for two-dimensional number vectorsstatic VectorFieldTypeInformation<? super NumberVector>
TypeUtil. NUMBER_VECTOR_FIELD_2D
Type request for two-dimensional number vectorsstatic VectorTypeInformation<NumberVector>
TypeUtil. NUMBER_VECTOR_VARIABLE_LENGTH
Number vectors of variable length. -
Uses of NumberVector in elki.data.uncertain
Methods in elki.data.uncertain with parameters of type NumberVector Modifier and Type Method Description protected static HyperBoundingBox
AbstractUncertainObject. computeBounds(NumberVector[] samples)
Compute the bounding box for some samples. -
Uses of NumberVector in elki.database.query.distance
Classes in elki.database.query.distance with type parameters of type NumberVector Modifier and Type Class Description class
LinearScanEuclideanPrioritySearcher<Q,O extends NumberVector>
Default linear scan search class, for Euclidean distance.static class
LinearScanEuclideanPrioritySearcher.ByDBID<O extends NumberVector>
Search by DBID.static class
LinearScanEuclideanPrioritySearcher.ByObject<O extends NumberVector>
Search by Object.Fields in elki.database.query.distance declared as NumberVector Modifier and Type Field Description private O
LinearScanEuclideanPrioritySearcher. query
Current query object. -
Uses of NumberVector in elki.database.query.knn
Classes in elki.database.query.knn with type parameters of type NumberVector Modifier and Type Class Description class
LinearScanEuclideanKNNByObject<O extends NumberVector>
Instance of this query for a particular database. -
Uses of NumberVector in elki.database.query.range
Classes in elki.database.query.range with type parameters of type NumberVector Modifier and Type Class Description class
LinearScanEuclideanRangeByObject<O extends NumberVector>
Optimized linear scan for Euclidean distance range queries. -
Uses of NumberVector in elki.database.relation
Methods in elki.database.relation with type parameters of type NumberVector Modifier and Type Method Description static <V extends NumberVector>
NumberVector.Factory<V>RelationUtil. getNumberVectorFactory(Relation<V> relation)
Get the number vector factory of a database relation.static <V extends NumberVector,T extends NumberVector>
Relation<V>RelationUtil. relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.static <V extends NumberVector,T extends NumberVector>
Relation<V>RelationUtil. relationUglyVectorCast(Relation<T> database)
An ugly vector type cast unavoidable in some situations due to Generics.Method parameters in elki.database.relation with type arguments of type NumberVector Modifier and Type Method Description static double[][]
RelationUtil. computeMinMax(Relation<? extends NumberVector> relation)
Determines the minimum and maximum values in each dimension of all objects stored in the given database.static double[][]
RelationUtil. relationAsMatrix(Relation<? extends NumberVector> relation, ArrayDBIDs ids)
Copy a relation into a double matrix. -
Uses of NumberVector in elki.datasource.filter
Classes in elki.datasource.filter with type parameters of type NumberVector Modifier and Type Class Description class
AbstractVectorConversionFilter<I,O extends NumberVector>
Abstract class for filters that produce number vectors.class
AbstractVectorStreamConversionFilter<I,O extends NumberVector>
Abstract base class for streaming filters that produce vectors.Methods in elki.datasource.filter with type parameters of type NumberVector Modifier and Type Method Description static <V extends NumberVector>
NumberVector.Factory<V>FilterUtil. guessFactory(SimpleTypeInformation<V> in)
Try to guess the appropriate factory. -
Uses of NumberVector in elki.datasource.filter.cleaning
Classes in elki.datasource.filter.cleaning with type parameters of type NumberVector Modifier and Type Class Description class
VectorDimensionalityFilter<V extends NumberVector>
Filter to remove all vectors that do not have the desired dimensionality.static class
VectorDimensionalityFilter.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.datasource.filter.normalization.columnwise
Classes in elki.datasource.filter.normalization.columnwise with type parameters of type NumberVector Modifier and Type Class Description class
AttributeWiseBetaNormalization<V extends NumberVector>
Project the data using a Beta distribution.static class
AttributeWiseBetaNormalization.Par<V extends NumberVector>
Parameterization class.class
AttributeWiseCDFNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors by estimating the distribution of values along each dimension independently, then rescaling objects to the cumulative density function (CDF) value at the original coordinate.static class
AttributeWiseCDFNormalization.Par<V extends NumberVector>
Parameterization class.class
AttributeWiseMADNormalization<V extends NumberVector>
Median Absolute Deviation is used for scaling the data set as follows:class
AttributeWiseMeanNormalization<V extends NumberVector>
Normalization designed for data with a meaningful zero:
The 0 is retained, and the data is linearly scaled to have a mean of 1, by projection with f(x) = x / mean(X).class
AttributeWiseMinMaxNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to a given minimum and maximum in each dimension.static class
AttributeWiseMinMaxNormalization.Par<V extends NumberVector>
Parameterization class.class
AttributeWiseVarianceNormalization<V extends NumberVector>
Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation in each dimension.static class
AttributeWiseVarianceNormalization.Par<V extends NumberVector>
Parameterization class.Fields in elki.datasource.filter.normalization.columnwise with type parameters of type NumberVector Modifier and Type Field Description (package private) java.util.List<? extends NumberVector>
IntegerRankTieNormalization.Sorter. col
Column to use for sorting.Method parameters in elki.datasource.filter.normalization.columnwise with type arguments of type NumberVector Modifier and Type Method Description java.lang.Double
AttributeWiseCDFNormalization.Adapter. get(java.util.List<? extends NumberVector> array, int off)
double
AttributeWiseCDFNormalization.Adapter. getDouble(java.util.List<? extends NumberVector> array, int off)
long
AttributeWiseCDFNormalization.Adapter. getLong(java.util.List<? extends NumberVector> array, int off)
void
IntegerRankTieNormalization.Sorter. setup(java.util.List<? extends NumberVector> col, int dim)
Configure the sorting class.int
AttributeWiseCDFNormalization.Adapter. size(java.util.List<? extends NumberVector> array)
-
Uses of NumberVector in elki.datasource.filter.normalization.instancewise
Classes in elki.datasource.filter.normalization.instancewise with type parameters of type NumberVector Modifier and Type Class Description class
HellingerHistogramNormalization<V extends NumberVector>
Normalize histograms by scaling them to unit absolute sum, then taking the square root of the absolute value in each attribute, times the normalization constant \(1/\sqrt{2}\).class
InstanceLogRankNormalization<V extends NumberVector>
Normalize vectors such that the smallest value of each instance is 0, the largest is 1, but using \( \log_2(1+x) \).class
InstanceMeanVarianceNormalization<V extends NumberVector>
Normalize vectors such that they have zero mean and unit variance.static class
InstanceMeanVarianceNormalization.Par<V extends NumberVector>
Parameterization class.class
InstanceMinMaxNormalization<V extends NumberVector>
Normalize vectors with respect to a given minimum and maximum in each dimension.static class
InstanceMinMaxNormalization.Par<V extends NumberVector>
Parameterization class.class
InstanceRankNormalization<V extends NumberVector>
Normalize vectors such that the smallest value of each instance is 0, the largest is 1.class
LengthNormalization<V extends NumberVector>
Class to perform a normalization on vectors to norm 1.static class
LengthNormalization.Par<V extends NumberVector>
Parameterization class.class
Log1PlusNormalization<V extends NumberVector>
Normalize the data set by applying \( \frac{\log(1+|x|b)}{\log 1+b} \) to any value.static class
Log1PlusNormalization.Par<V extends NumberVector>
Parameterization class.Fields in elki.datasource.filter.normalization.instancewise with type parameters of type NumberVector Modifier and Type Field Description static HellingerHistogramNormalization<NumberVector>
HellingerHistogramNormalization. STATIC
Static instance.static Log1PlusNormalization<NumberVector>
Log1PlusNormalization. STATIC
Static instance.Methods in elki.datasource.filter.normalization.instancewise that return types with arguments of type NumberVector Modifier and Type Method Description HellingerHistogramNormalization<NumberVector>
HellingerHistogramNormalization.Par. make()
InstanceLogRankNormalization<NumberVector>
InstanceLogRankNormalization.Par. make()
InstanceRankNormalization<NumberVector>
InstanceRankNormalization.Par. make()
-
Uses of NumberVector in elki.datasource.filter.transform
Classes in elki.datasource.filter.transform with type parameters of type NumberVector Modifier and Type Class Description class
AbstractSupervisedProjectionVectorFilter<V extends NumberVector>
Base class for supervised projection methods.static class
AbstractSupervisedProjectionVectorFilter.Par<V extends NumberVector>
Parameterization class.class
ClassicMultidimensionalScalingTransform<I,O extends NumberVector>
Rescale the data set using multidimensional scaling, MDS.static class
ClassicMultidimensionalScalingTransform.Par<I,O extends NumberVector>
Parameterization class.class
FastMultidimensionalScalingTransform<I,O extends NumberVector>
Rescale the data set using multidimensional scaling, MDS.static class
FastMultidimensionalScalingTransform.Par<I,O extends NumberVector>
Parameterization class.class
GlobalPrincipalComponentAnalysisTransform<O extends NumberVector>
Apply Principal Component Analysis (PCA) to the data set.static class
GlobalPrincipalComponentAnalysisTransform.Par<O extends NumberVector>
Parameterization class.class
HistogramJitterFilter<V extends NumberVector>
Add jitter, preserving the histogram properties (same sum, nonnegative).class
LatLngToECEFFilter<V extends NumberVector>
Project a 2D data set (latitude, longitude) to a 3D coordinate system (X, Y, Z), such that Euclidean distance is line-of-sight.static class
LatLngToECEFFilter.Par<V extends NumberVector>
Parameterization class.class
LinearDiscriminantAnalysisFilter<V extends NumberVector>
Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.static class
LinearDiscriminantAnalysisFilter.Par<V extends NumberVector>
Parameterization class.class
LngLatToECEFFilter<V extends NumberVector>
Project a 2D data set (longitude, latitude) to a 3D coordinate system (X, Y, Z), such that Euclidean distance is line-of-sight.static class
LngLatToECEFFilter.Par<V extends NumberVector>
Parameterization class.class
NumberVectorFeatureSelectionFilter<V extends NumberVector>
Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes.class
NumberVectorRandomFeatureSelectionFilter<V extends NumberVector>
Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes.class
PerturbationFilter<V extends NumberVector>
A filter to perturb the values by adding micro-noise.static class
PerturbationFilter.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.datasource.filter.typeconversions
Classes in elki.datasource.filter.typeconversions with type parameters of type NumberVector Modifier and Type Class Description class
SplitNumberVectorFilter<V extends NumberVector>
Split an existing column into two types.static class
SplitNumberVectorFilter.Par<V extends NumberVector>
Parameterization class.Methods in elki.datasource.filter.typeconversions that return types with arguments of type NumberVector Modifier and Type Method Description protected SimpleTypeInformation<? super NumberVector>
UncertainSplitFilter. getInputTypeRestriction()
protected SimpleTypeInformation<? super NumberVector>
WeightedUncertainSplitFilter. getInputTypeRestriction()
Methods in elki.datasource.filter.typeconversions with parameters of type NumberVector Modifier and Type Method Description protected UnweightedDiscreteUncertainObject
UncertainSplitFilter. filterSingleObject(NumberVector vec)
protected WeightedDiscreteUncertainObject
WeightedUncertainSplitFilter. filterSingleObject(NumberVector vec)
Method parameters in elki.datasource.filter.typeconversions with type arguments of type NumberVector Modifier and Type Method Description protected SimpleTypeInformation<UnweightedDiscreteUncertainObject>
UncertainSplitFilter. convertedType(SimpleTypeInformation<NumberVector> in)
protected SimpleTypeInformation<WeightedDiscreteUncertainObject>
WeightedUncertainSplitFilter. convertedType(SimpleTypeInformation<NumberVector> in)
-
Uses of NumberVector in elki.datasource.parser
Classes in elki.datasource.parser with type parameters of type NumberVector Modifier and Type Class Description class
CategorialDataAsNumberVectorParser<V extends NumberVector>
A very simple parser for categorial data, which will then be encoded as numbers.static class
CategorialDataAsNumberVectorParser.Par<V extends NumberVector>
Parameterization class.class
NumberVectorLabelParser<V extends NumberVector>
Parser for a simple CSV type of format, with columns separated by the given pattern (default: whitespace).static class
NumberVectorLabelParser.Par<V extends NumberVector>
Parameterization class.Fields in elki.datasource.parser declared as NumberVector Modifier and Type Field Description protected V
NumberVectorLabelParser. curvec
Current vector. -
Uses of NumberVector in elki.distance
Methods in elki.distance that return types with arguments of type NumberVector Modifier and Type Method Description SimpleTypeInformation<? super NumberVector>
AbstractNumberVectorDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
ArcCosineDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
ArcCosineUnitlengthDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
CanberraDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
ClarkDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
CosineDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
CosineUnitlengthDistance. getInputTypeRestriction()
VectorFieldTypeInformation<? super NumberVector>
MatrixWeightedQuadraticDistance. getInputTypeRestriction()
Methods in elki.distance with parameters of type NumberVector Modifier and Type Method Description static int
AbstractNumberVectorDistance. dimensionality(NumberVector o1, NumberVector o2)
Get the common dimensionality of the two objects.static int
AbstractNumberVectorDistance. dimensionality(NumberVector o1, NumberVector o2, int expect)
Get the common dimensionality of the two objects.double
ArcCosineDistance. distance(NumberVector v1, NumberVector v2)
double
ArcCosineUnitlengthDistance. distance(NumberVector v1, NumberVector v2)
double
BrayCurtisDistance. distance(NumberVector v1, NumberVector v2)
double
CanberraDistance. distance(NumberVector v1, NumberVector v2)
double
ClarkDistance. distance(NumberVector v1, NumberVector v2)
double
CosineDistance. distance(NumberVector v1, NumberVector v2)
double
CosineUnitlengthDistance. distance(NumberVector v1, NumberVector v2)
double
MahalanobisDistance. distance(NumberVector o1, NumberVector o2)
double
MatrixWeightedQuadraticDistance. distance(NumberVector o1, NumberVector o2)
double
NumberVectorDistance. distance(NumberVector o1, NumberVector o2)
Computes the distance between two given vectors according to this distance function.double
SqrtCosineDistance. distance(NumberVector v1, NumberVector v2)
double
SqrtCosineUnitlengthDistance. distance(NumberVector v1, NumberVector v2)
double
WeightedCanberraDistance. distance(NumberVector v1, NumberVector v2)
double
MahalanobisDistance. norm(NumberVector obj)
double
MatrixWeightedQuadraticDistance. norm(NumberVector obj)
-
Uses of NumberVector in elki.distance.colorhistogram
Methods in elki.distance.colorhistogram with parameters of type NumberVector Modifier and Type Method Description double
HistogramIntersectionDistance. distance(NumberVector v1, NumberVector v2)
-
Uses of NumberVector in elki.distance.correlation
Methods in elki.distance.correlation with parameters of type NumberVector Modifier and Type Method Description double
AbsolutePearsonCorrelationDistance. distance(NumberVector v1, NumberVector v2)
Computes the absolute Pearson correlation distance for two given feature vectors.double
AbsoluteUncenteredCorrelationDistance. distance(NumberVector v1, NumberVector v2)
double
PearsonCorrelationDistance. distance(NumberVector v1, NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.double
SquaredPearsonCorrelationDistance. distance(NumberVector v1, NumberVector v2)
double
SquaredUncenteredCorrelationDistance. distance(NumberVector v1, NumberVector v2)
double
UncenteredCorrelationDistance. distance(NumberVector v1, NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.double
WeightedPearsonCorrelationDistance. distance(NumberVector v1, NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.double
WeightedSquaredPearsonCorrelationDistance. distance(NumberVector v1, NumberVector v2)
static double
UncenteredCorrelationDistance. uncenteredCorrelation(NumberVector x, NumberVector y)
Compute the uncentered correlation of two vectors. -
Uses of NumberVector in elki.distance.geo
Methods in elki.distance.geo that return types with arguments of type NumberVector Modifier and Type Method Description SimpleTypeInformation<? super NumberVector>
DimensionSelectingLatLngDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
LatLngDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
LngLatDistance. getInputTypeRestriction()
Methods in elki.distance.geo with parameters of type NumberVector Modifier and Type Method Description double
DimensionSelectingLatLngDistance. distance(NumberVector o1, NumberVector o2)
double
LatLngDistance. distance(NumberVector o1, NumberVector o2)
double
LngLatDistance. distance(NumberVector o1, NumberVector o2)
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Uses of NumberVector in elki.distance.histogram
Methods in elki.distance.histogram with parameters of type NumberVector Modifier and Type Method Description double
HistogramMatchDistance. distance(NumberVector v1, NumberVector v2)
double
KolmogorovSmirnovDistance. distance(NumberVector v1, NumberVector v2)
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Uses of NumberVector in elki.distance.minkowski
Methods in elki.distance.minkowski that return types with arguments of type NumberVector Modifier and Type Method Description SimpleTypeInformation<? super NumberVector>
LPNormDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
SquaredEuclideanDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
WeightedLPNormDistance. getInputTypeRestriction()
Methods in elki.distance.minkowski with parameters of type NumberVector Modifier and Type Method Description double
EuclideanDistance. distance(NumberVector v1, NumberVector v2)
double
LPIntegerNormDistance. distance(NumberVector v1, NumberVector v2)
double
LPNormDistance. distance(NumberVector v1, NumberVector v2)
double
ManhattanDistance. distance(NumberVector v1, NumberVector v2)
double
MaximumDistance. distance(NumberVector v1, NumberVector v2)
double
MinimumDistance. distance(NumberVector v1, NumberVector v2)
double
SquaredEuclideanDistance. distance(NumberVector v1, NumberVector v2)
double
WeightedEuclideanDistance. distance(NumberVector v1, NumberVector v2)
double
WeightedLPNormDistance. distance(NumberVector v1, NumberVector v2)
double
WeightedManhattanDistance. distance(NumberVector v1, NumberVector v2)
double
WeightedMaximumDistance. distance(NumberVector v1, NumberVector v2)
double
WeightedSquaredEuclideanDistance. distance(NumberVector v1, NumberVector v2)
double
EuclideanDistance. norm(NumberVector v)
double
LPIntegerNormDistance. norm(NumberVector v)
double
LPNormDistance. norm(NumberVector v)
double
ManhattanDistance. norm(NumberVector v)
double
MaximumDistance. norm(NumberVector v)
double
MinimumDistance. norm(NumberVector v)
double
SquaredEuclideanDistance. norm(NumberVector v)
double
WeightedEuclideanDistance. norm(NumberVector v)
double
WeightedLPNormDistance. norm(NumberVector v)
double
WeightedManhattanDistance. norm(NumberVector v)
double
WeightedMaximumDistance. norm(NumberVector v)
double
WeightedSquaredEuclideanDistance. norm(NumberVector obj)
private double
EuclideanDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
LPIntegerNormDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
Compute unscaled distance in a range of dimensions.private double
LPNormDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
Compute unscaled distance in a range of dimensions.private double
ManhattanDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
MaximumDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
SquaredEuclideanDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
WeightedEuclideanDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
WeightedLPNormDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
WeightedManhattanDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
WeightedMaximumDistance. preDistance(NumberVector v1, NumberVector v2, int start, int end)
private double
EuclideanDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
LPIntegerNormDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
Compute unscaled distance in a range of dimensions.private double
LPNormDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
Compute unscaled distance in a range of dimensions.private double
ManhattanDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
MaximumDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
SquaredEuclideanDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
WeightedEuclideanDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
WeightedLPNormDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
WeightedManhattanDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
WeightedMaximumDistance. preDistanceVM(NumberVector v, SpatialComparable mbr, int start, int end)
private double
EuclideanDistance. preNorm(NumberVector v, int start, int end)
private double
LPIntegerNormDistance. preNorm(NumberVector v, int start, int end)
Compute unscaled norm in a range of dimensions.private double
LPNormDistance. preNorm(NumberVector v, int start, int end)
Compute unscaled norm in a range of dimensions.private double
ManhattanDistance. preNorm(NumberVector v, int start, int end)
private double
MaximumDistance. preNorm(NumberVector v, int start, int end)
private double
SquaredEuclideanDistance. preNorm(NumberVector v, int start, int end)
private double
WeightedEuclideanDistance. preNorm(NumberVector v, int start, int end)
private double
WeightedLPNormDistance. preNorm(NumberVector v, int start, int end)
private double
WeightedManhattanDistance. preNorm(NumberVector v, int start, int end)
private double
WeightedMaximumDistance. preNorm(NumberVector v, int start, int end)
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Uses of NumberVector in elki.distance.probabilistic
Methods in elki.distance.probabilistic with type parameters of type NumberVector Modifier and Type Method Description <T extends NumberVector>
SpatialPrimitiveDistanceSimilarityQuery<T>HellingerDistance. instantiate(Relation<T> database)
Methods in elki.distance.probabilistic that return types with arguments of type NumberVector Modifier and Type Method Description SimpleTypeInformation<? super NumberVector>
ChiSquaredDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
FisherRaoDistance. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
HellingerDistance. getInputTypeRestriction()
Methods in elki.distance.probabilistic with parameters of type NumberVector Modifier and Type Method Description double
ChiDistance. distance(NumberVector v1, NumberVector v2)
double
ChiSquaredDistance. distance(NumberVector v1, NumberVector v2)
double
FisherRaoDistance. distance(NumberVector fv1, NumberVector fv2)
double
HellingerDistance. distance(NumberVector fv1, NumberVector fv2)
double
JeffreyDivergenceDistance. distance(NumberVector v1, NumberVector v2)
double
JensenShannonDivergenceDistance. distance(NumberVector v1, NumberVector v2)
double
KullbackLeiblerDivergenceAsymmetricDistance. distance(NumberVector v1, NumberVector v2)
double
KullbackLeiblerDivergenceReverseAsymmetricDistance. distance(NumberVector v1, NumberVector v2)
double
SqrtJensenShannonDivergenceDistance. distance(NumberVector v1, NumberVector v2)
double
TriangularDiscriminationDistance. distance(NumberVector v1, NumberVector v2)
double
TriangularDistance. distance(NumberVector v1, NumberVector v2)
double
HellingerDistance. similarity(NumberVector o1, NumberVector o2)
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Uses of NumberVector in elki.distance.set
Methods in elki.distance.set with parameters of type NumberVector Modifier and Type Method Description double
HammingDistance. distance(NumberVector o1, NumberVector o2)
double
JaccardSimilarityDistance. distance(NumberVector o1, NumberVector o2)
private double
HammingDistance. hammingDistanceNumberVector(NumberVector o1, NumberVector o2)
Version for number vectors.static double
JaccardSimilarityDistance. similarityNumberVector(NumberVector o1, NumberVector o2)
Compute Jaccard similarity for two number vectors. -
Uses of NumberVector in elki.distance.subspace
Methods in elki.distance.subspace with type parameters of type NumberVector Modifier and Type Method Description <T extends NumberVector>
SpatialPrimitiveDistanceQuery<T>SubspaceLPNormDistance. instantiate(Relation<T> database)
Methods in elki.distance.subspace that return types with arguments of type NumberVector Modifier and Type Method Description VectorTypeInformation<? super NumberVector>
OnedimensionalDistance. getInputTypeRestriction()
VectorFieldTypeInformation<? super NumberVector>
SubspaceLPNormDistance. getInputTypeRestriction()
Methods in elki.distance.subspace with parameters of type NumberVector Modifier and Type Method Description double
OnedimensionalDistance. distance(NumberVector v1, NumberVector v2)
double
SubspaceEuclideanDistance. distance(NumberVector v1, NumberVector v2)
Constructor.double
SubspaceLPNormDistance. distance(NumberVector v1, NumberVector v2)
double
SubspaceManhattanDistance. distance(NumberVector v1, NumberVector v2)
double
SubspaceMaximumDistance. distance(NumberVector v1, NumberVector v2)
protected double
SubspaceEuclideanDistance. minDistObject(SpatialComparable mbr, NumberVector v)
protected double
SubspaceLPNormDistance. minDistObject(SpatialComparable mbr, NumberVector v)
protected double
SubspaceManhattanDistance. minDistObject(SpatialComparable mbr, NumberVector v)
protected double
SubspaceMaximumDistance. minDistObject(SpatialComparable mbr, NumberVector v)
double
OnedimensionalDistance. norm(NumberVector obj)
double
SubspaceEuclideanDistance. norm(NumberVector obj)
double
SubspaceLPNormDistance. norm(NumberVector obj)
double
SubspaceManhattanDistance. norm(NumberVector obj)
double
SubspaceMaximumDistance. norm(NumberVector obj)
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Uses of NumberVector in elki.distance.timeseries
Methods in elki.distance.timeseries that return types with arguments of type NumberVector Modifier and Type Method Description VectorTypeInformation<? super NumberVector>
AbstractEditDistance. getInputTypeRestriction()
VectorTypeInformation<? super NumberVector>
LCSSDistance. getInputTypeRestriction()
Methods in elki.distance.timeseries with parameters of type NumberVector Modifier and Type Method Description protected double
DerivativeDTWDistance. derivative(int i, NumberVector v)
Given a NumberVector and the position of an element, approximates the gradient of given element.double
DerivativeDTWDistance. distance(NumberVector v1, NumberVector v2)
double
DTWDistance. distance(NumberVector v1, NumberVector v2)
double
EDRDistance. distance(NumberVector v1, NumberVector v2)
double
ERPDistance. distance(NumberVector v1, NumberVector v2)
double
LCSSDistance. distance(NumberVector v1, NumberVector v2)
protected void
DerivativeDTWDistance. firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2)
protected void
DTWDistance. firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2)
Fill the first row.protected void
ERPDistance. firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2)
double
LCSSDistance. getRange(NumberVector v1, int dim1, NumberVector v2, int dim2)
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Uses of NumberVector in elki.evaluation.clustering.internal
Fields in elki.evaluation.clustering.internal with type parameters of type NumberVector Modifier and Type Field Description private PrimitiveDistance<? super NumberVector>
ConcordantPairsGammaTau. distance
Distance function to use.private PrimitiveDistance<NumberVector>
ConcordantPairsGammaTau.Par. distance
Distance function to use.Methods in elki.evaluation.clustering.internal with parameters of type NumberVector Modifier and Type Method Description static int
SimplifiedSilhouette. centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)
Compute centroids.static int
VarianceRatioCriterion. globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)
Update the global centroid.double[]
DaviesBouldinIndex. withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)
Method parameters in elki.evaluation.clustering.internal with type arguments of type NumberVector Modifier and Type Method Description static int
SimplifiedSilhouette. centroids(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)
Compute centroids.protected double[]
ConcordantPairsGammaTau. computeWithinDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, int withinPairs)
double
ClusterRadius. evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.double
ConcordantPairsGammaTau. evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.double
DaviesBouldinIndex. evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.double
PBMIndex. evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.double
SimplifiedSilhouette. evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.double
SquaredErrors. evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.double
VarianceRatioCriterion. evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)
Evaluate a single clustering.static int
VarianceRatioCriterion. globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)
Update the global centroid.double[]
DaviesBouldinIndex. withinGroupDistances(Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids)
Constructor parameters in elki.evaluation.clustering.internal with type arguments of type NumberVector Constructor Description ConcordantPairsGammaTau(PrimitiveDistance<? super NumberVector> distance, NoiseHandling noiseHandling)
Constructor. -
Uses of NumberVector in elki.evaluation.scores.adapter
Fields in elki.evaluation.scores.adapter declared as NumberVector Modifier and Type Field Description protected NumberVector
AbstractVectorIter. positive
Vector of positive examples.protected NumberVector
AbstractVectorIter. vec
Data vector.Constructors in elki.evaluation.scores.adapter with parameters of type NumberVector Constructor Description AbstractVectorIter(NumberVector positive, NumberVector vec)
Constructor.DecreasingVectorIter(NumberVector positive, NumberVector vec)
Constructor.IncreasingVectorIter(NumberVector positive, NumberVector vec)
Constructor. -
Uses of NumberVector in elki.index.invertedlist
Classes in elki.index.invertedlist with type parameters of type NumberVector Modifier and Type Class Description class
InMemoryInvertedIndex<V extends NumberVector>
Simple index using inverted lists, for cosine distance only.static class
InMemoryInvertedIndex.Factory<V extends NumberVector>
Index factorystatic class
InMemoryInvertedIndex.Factory.Par<V extends NumberVector>
Parameterizer for inverted list index.Methods in elki.index.invertedlist with parameters of type NumberVector Modifier and Type Method Description private double
InMemoryInvertedIndex. naiveQueryDense(NumberVector obj, WritableDoubleDataStore scores, HashSetModifiableDBIDs cands)
Query the most similar objects, dense version. -
Uses of NumberVector in elki.index.lsh.hashfamilies
Methods in elki.index.lsh.hashfamilies that return types with arguments of type NumberVector Modifier and Type Method Description java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>
AbstractProjectedHashFunctionFamily. generateHashFunctions(Relation<? extends NumberVector> relation, int l)
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>
CosineHashFunctionFamily. generateHashFunctions(Relation<? extends NumberVector> relation, int l)
Method parameters in elki.index.lsh.hashfamilies with type arguments of type NumberVector Modifier and Type Method Description java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>
AbstractProjectedHashFunctionFamily. generateHashFunctions(Relation<? extends NumberVector> relation, int l)
java.util.ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector>>
CosineHashFunctionFamily. generateHashFunctions(Relation<? extends NumberVector> relation, int l)
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Uses of NumberVector in elki.index.lsh.hashfunctions
Methods in elki.index.lsh.hashfunctions with parameters of type NumberVector Modifier and Type Method Description int
CosineLocalitySensitiveHashFunction. hashObject(NumberVector obj)
int
CosineLocalitySensitiveHashFunction. hashObject(NumberVector obj, double[] buf)
int
MultipleProjectionsLocalitySensitiveHashFunction. hashObject(NumberVector vec)
int
MultipleProjectionsLocalitySensitiveHashFunction. hashObject(NumberVector vec, double[] buf)
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Uses of NumberVector in elki.index.preprocessed.fastoptics
Fields in elki.index.preprocessed.fastoptics with type parameters of type NumberVector Modifier and Type Field Description (package private) Relation<? extends NumberVector>
RandomProjectedNeighborsAndDensities. points
entire point setMethod parameters in elki.index.preprocessed.fastoptics with type arguments of type NumberVector Modifier and Type Method Description void
RandomProjectedNeighborsAndDensities. computeSetsBounds(Relation<? extends NumberVector> points, int minSplitSize, DBIDs ptList)
Create random projections, project points and put points into sets of size about minSplitSize/2 -
Uses of NumberVector in elki.index.preprocessed.knn
Classes in elki.index.preprocessed.knn with type parameters of type NumberVector Modifier and Type Class Description class
MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.static class
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
The parameterizable factory.static class
MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.Par<O extends NumberVector,N extends Node<E>,E extends MTreeEntry>
Parameterization class.class
NaiveProjectedKNNPreprocessor<O extends NumberVector>
Compute the approximate k nearest neighbors using 1 dimensional projections.static class
NaiveProjectedKNNPreprocessor.Factory<V extends NumberVector>
Index factory classclass
SpacefillingKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.static class
SpacefillingKNNPreprocessor.Factory<V extends NumberVector>
Index factory classclass
SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.static class
SpacefillingMaterializeKNNPreprocessor.Factory<V extends NumberVector>
Index factory classstatic class
SpacefillingMaterializeKNNPreprocessor.Factory.Par<V extends NumberVector>
Parameterization class.class
SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector>
A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.Fields in elki.index.preprocessed.knn with type parameters of type NumberVector Modifier and Type Field Description (package private) java.util.List<java.util.List<SpatialPair<DBID,NumberVector>>>
SpacefillingKNNPreprocessor. curves
Curve storageMethods in elki.index.preprocessed.knn that return types with arguments of type NumberVector Modifier and Type Method Description SpatialApproximationMaterializeKNNPreprocessor<NumberVector>
SpatialApproximationMaterializeKNNPreprocessor.Factory. instantiate(Relation<NumberVector> relation)
Method parameters in elki.index.preprocessed.knn with type arguments of type NumberVector Modifier and Type Method Description SpatialApproximationMaterializeKNNPreprocessor<NumberVector>
SpatialApproximationMaterializeKNNPreprocessor.Factory. instantiate(Relation<NumberVector> relation)
Constructor parameters in elki.index.preprocessed.knn with type arguments of type NumberVector Constructor Description Factory(int k, Distance<? super NumberVector> distance)
Constructor. -
Uses of NumberVector in elki.index.projected
Classes in elki.index.projected with type parameters of type NumberVector Modifier and Type Class Description class
LatLngAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lat/Lng pairs) by using a projection to 3D coordinates (WGS-86 to ECEF).static class
LatLngAsECEFIndex.Factory<O extends NumberVector>
Index factory.static class
LatLngAsECEFIndex.Factory.Par<O extends NumberVector>
Parameterization class.class
LngLatAsECEFIndex<O extends NumberVector>
Index a 2d data set (consisting of Lng/Lat pairs) by using a projection to 3D coordinates (WGS-86 to ECEF).static class
LngLatAsECEFIndex.Factory<O extends NumberVector>
Index factory.static class
LngLatAsECEFIndex.Factory.Par<O extends NumberVector>
Parameterization class.class
PINN<O extends NumberVector>
Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the nearest neighbors in high dimensional spaces by using a random projection based index.static class
PINN.Par<O extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.index.tree.betula
Methods in elki.index.tree.betula with parameters of type NumberVector Modifier and Type Method Description L
CFTree. findLeaf(NumberVector nv)
Find the leaf of a cluster, to get the final cluster assignment.private L
CFTree. findLeaf(CFNode<L> node, NumberVector nv)
Find the leaf of a cluster, to get the final cluster assignment.void
CFTree. insert(NumberVector nv, DBIDRef dbid)
Insert a data point into the tree.private CFNode<L>
CFTree. insert(CFNode<L> node, NumberVector nv, DBIDRef dbid)
Recursive insertion.private double
CFTree. sqabsorption(NumberVector nv, ClusterFeature cf)
Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.private double
CFTree. sqdistance(NumberVector nv, ClusterFeature cf)
Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.Method parameters in elki.index.tree.betula with type arguments of type NumberVector Modifier and Type Method Description CFTree<L>
CFTree.Factory. newTree(DBIDs ids, Relation<? extends NumberVector> relation, boolean storeIds)
Make a new tree. -
Uses of NumberVector in elki.index.tree.betula.distance
Methods in elki.index.tree.betula.distance with parameters of type NumberVector Modifier and Type Method Description double
AverageInterclusterDistance. squaredDistance(NumberVector nv, ClusterFeature cf)
double
AverageIntraclusterDistance. squaredDistance(NumberVector nv, ClusterFeature cf1)
double
BIRCHAverageInterclusterDistance. squaredDistance(NumberVector v, ClusterFeature ocf)
double
BIRCHAverageIntraclusterDistance. squaredDistance(NumberVector v, ClusterFeature ocf)
double
BIRCHRadiusDistance. squaredDistance(NumberVector n, ClusterFeature ocf)
double
BIRCHVarianceIncreaseDistance. squaredDistance(NumberVector v, ClusterFeature ocf)
double
CentroidEuclideanDistance. squaredDistance(NumberVector v, ClusterFeature cf)
double
CentroidManhattanDistance. squaredDistance(NumberVector v, ClusterFeature cf)
double
CFDistance. squaredDistance(NumberVector v, ClusterFeature cf)
Distance of a vector to a clustering feature.double
RadiusDistance. squaredDistance(NumberVector nv, ClusterFeature cf1)
double
VarianceIncreaseDistance. squaredDistance(NumberVector nv, ClusterFeature cf)
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Uses of NumberVector in elki.index.tree.betula.features
Subinterfaces of NumberVector in elki.index.tree.betula.features Modifier and Type Interface Description interface
ClusterFeature
Interface for basic ClusteringFeature functionsClasses in elki.index.tree.betula.features that implement NumberVector Modifier and Type Class Description class
BIRCHCF
Clustering Feature of BIRCH, only for comparisonclass
VIIFeature
Clustering Feature of stable BIRCH with a single variance per cluster featureclass
VVIFeature
Clustering Feature of stable BIRCH with variance per dimensionclass
VVVFeature
Clustering Feature of stable BIRCH with covariance instead of varianceMethods in elki.index.tree.betula.features with parameters of type NumberVector Modifier and Type Method Description void
BIRCHCF. addToStatistics(NumberVector nv)
void
ClusterFeature. addToStatistics(NumberVector nv)
Add NumberVector to CFvoid
VIIFeature. addToStatistics(NumberVector nv)
void
VVIFeature. addToStatistics(NumberVector nv)
void
VVVFeature. addToStatistics(NumberVector nv)
static double
BIRCHCF. sumOfSquares(NumberVector v)
Compute the sum of squares of a vector. -
Uses of NumberVector in elki.index.tree.metrical.vptree
Classes in elki.index.tree.metrical.vptree with type parameters of type NumberVector Modifier and Type Class Description static class
GNAT.Factory<O extends NumberVector>
Index Factorystatic class
GNAT.Factory.Par<O extends NumberVector>
Parameterization class.static class
VPTree.Factory<O extends NumberVector>
Index factory for the VP-Treestatic class
VPTree.Factory.Par<O extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.index.tree.spatial
Classes in elki.index.tree.spatial that implement NumberVector Modifier and Type Class Description class
SpatialPointLeafEntry
Represents an entry in a leaf node of a spatial index.Constructors in elki.index.tree.spatial with parameters of type NumberVector Constructor Description SpatialPointLeafEntry(DBID id, NumberVector vector)
Constructor from number vector. -
Uses of NumberVector in elki.index.tree.spatial.kd
Classes in elki.index.tree.spatial.kd with type parameters of type NumberVector Modifier and Type Class Description class
MemoryKDTree<O extends NumberVector>
Implementation of a static in-memory K-D-tree.static class
MemoryKDTree.Factory<O extends NumberVector>
Factory classstatic class
MemoryKDTree.Factory.Par<O extends NumberVector>
Parameterization class.class
MinimalisticMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.static class
MinimalisticMemoryKDTree.Factory<O extends NumberVector>
Factory classstatic class
MinimalisticMemoryKDTree.Factory.Par<O extends NumberVector>
Parameterization class.class
SmallMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.static class
SmallMemoryKDTree.Factory<O extends NumberVector>
Factory classstatic class
SmallMemoryKDTree.Factory.Par<O extends NumberVector>
Parameterization class.Fields in elki.index.tree.spatial.kd declared as NumberVector Modifier and Type Field Description private O
MemoryKDTree.KDTreePrioritySearcher. query
Current query object.private O
MinimalisticMemoryKDTree.KDTreePrioritySearcher. query
Current query object.private O
SmallMemoryKDTree.KDTreePrioritySearcher. query
Current query object.Methods in elki.index.tree.spatial.kd with parameters of type NumberVector Modifier and Type Method Description double
PartialEuclideanDistance. distance(NumberVector a, NumberVector b)
double
PartialLPNormDistance. distance(NumberVector a, NumberVector b)
double
PartialManhattanDistance. distance(NumberVector a, NumberVector b)
double
PartialSquaredEuclideanDistance. distance(NumberVector a, NumberVector b)
Method parameters in elki.index.tree.spatial.kd with type arguments of type NumberVector Modifier and Type Method Description java.lang.Object
MemoryKDTree. buildTree(Relation<? extends NumberVector> relation, int left, int right, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, VectorUtil.SortDBIDsBySingleDimension comp)
Build the k-d tree.Constructor parameters in elki.index.tree.spatial.kd with type arguments of type NumberVector Constructor Description CountSortAccesses(Counter objaccess, Relation<? extends NumberVector> data)
Constructor. -
Uses of NumberVector in elki.index.tree.spatial.kd.split
Method parameters in elki.index.tree.spatial.kd.split with type arguments of type NumberVector Modifier and Type Method Description SplitStrategy.Info
BoundedMidpointSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
LeastOneDimSSQSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
LeastSSQSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
MeanVarianceSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
MedianSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
MedianVarianceSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
MidpointSplit. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
SplitStrategy.Info
SplitStrategy. findSplit(Relation<? extends NumberVector> relation, int dims, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int left, int right, VectorUtil.SortDBIDsBySingleDimension comp)
Build the k-d-tree using midpoint splitting.(package private) static double[]
SplitStrategy.Util. minmaxRange(int dims, Relation<? extends NumberVector> relation, DBIDArrayIter iter, int left, int right)
Find the minimum and maximum in each dimension of a range of values.(package private) static int
SplitStrategy.Util. pivot(Relation<? extends NumberVector> relation, ArrayModifiableDBIDs sorted, DBIDArrayMIter iter, int dim, int left, int right, double mid)
Pivot an interval.(package private) static double[]
SplitStrategy.Util. sumvar(Relation<? extends NumberVector> relation, int dims, DBIDArrayMIter iter, int left, int right)
Compute the sum and sum-of-squares (for variance). -
Uses of NumberVector in elki.index.tree.spatial.rstarvariants
Classes in elki.index.tree.spatial.rstarvariants with type parameters of type NumberVector Modifier and Type Class Description class
AbstractRStarTreeFactory<O extends NumberVector,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,S extends RTreeSettings>
Abstract factory for R*-Tree based trees.static class
AbstractRStarTreeFactory.Par<O extends NumberVector,S extends RTreeSettings>
Parameterization class. -
Uses of NumberVector in elki.index.tree.spatial.rstarvariants.deliclu
Classes in elki.index.tree.spatial.rstarvariants.deliclu with type parameters of type NumberVector Modifier and Type Class Description class
DeLiCluTreeFactory<O extends NumberVector>
Factory for DeLiClu R*-Trees.static class
DeLiCluTreeFactory.Par<O extends NumberVector>
Parameterization class.class
DeLiCluTreeIndex<O extends NumberVector>
The common use of the DeLiClu tree: indexing number vectors.Classes in elki.index.tree.spatial.rstarvariants.deliclu that implement NumberVector Modifier and Type Class Description class
DeLiCluLeafEntry
Defines the requirements for a leaf entry in an DeLiClu-Tree node.Constructors in elki.index.tree.spatial.rstarvariants.deliclu with parameters of type NumberVector Constructor Description DeLiCluLeafEntry(DBID id, NumberVector vector)
Constructs a new LeafEntry object with the given parameters. -
Uses of NumberVector in elki.index.tree.spatial.rstarvariants.flat
Classes in elki.index.tree.spatial.rstarvariants.flat with type parameters of type NumberVector Modifier and Type Class Description class
FlatRStarTreeFactory<O extends NumberVector>
Factory for flat R*-Trees.static class
FlatRStarTreeFactory.Par<O extends NumberVector>
Parameterization class.class
FlatRStarTreeIndex<O extends NumberVector>
The common use of the flat rstar tree: indexing number vectors. -
Uses of NumberVector in elki.index.tree.spatial.rstarvariants.query
Classes in elki.index.tree.spatial.rstarvariants.query with type parameters of type NumberVector Modifier and Type Class Description class
EuclideanRStarTreeKNNQuery<O extends NumberVector>
Instance of a KNN query for a particular spatial index.class
EuclideanRStarTreeRangeQuery<O extends NumberVector>
Instance of a range query for a particular spatial index. -
Uses of NumberVector in elki.index.tree.spatial.rstarvariants.rdknn
Classes in elki.index.tree.spatial.rstarvariants.rdknn with type parameters of type NumberVector Modifier and Type Class Description class
RdKNNTree<O extends NumberVector>
RDkNNTree is a spatial index structure based on the concepts of the R*-Tree supporting efficient processing of reverse k nearest neighbor queries.class
RdKNNTreeFactory<O extends NumberVector>
Factory for RdKNN R*-Trees.static class
RdKNNTreeFactory.Par<O extends NumberVector>
Parameterization class.Classes in elki.index.tree.spatial.rstarvariants.rdknn that implement NumberVector Modifier and Type Class Description class
RdKNNLeafEntry
Represents an entry in a leaf node of an RdKNN-Tree.Fields in elki.index.tree.spatial.rstarvariants.rdknn with type parameters of type NumberVector Modifier and Type Field Description (package private) SpatialPrimitiveDistance<NumberVector>
RdkNNSettings. distance
The distance function.Constructors in elki.index.tree.spatial.rstarvariants.rdknn with parameters of type NumberVector Constructor Description RdKNNLeafEntry(DBID id, NumberVector vector, double knnDistance)
Constructs a new RDkNNLeafEntry object with the given parameters.Constructor parameters in elki.index.tree.spatial.rstarvariants.rdknn with type arguments of type NumberVector Constructor Description RdkNNSettings(int k_max, SpatialPrimitiveDistance<NumberVector> distance)
Constructor. -
Uses of NumberVector in elki.index.tree.spatial.rstarvariants.rstar
Classes in elki.index.tree.spatial.rstarvariants.rstar with type parameters of type NumberVector Modifier and Type Class Description class
RStarTreeFactory<O extends NumberVector>
Factory for regular R*-Trees.static class
RStarTreeFactory.Par<O extends NumberVector>
Parameterization class.class
RStarTreeIndex<O extends NumberVector>
The common use of the rstar tree: indexing number vectors. -
Uses of NumberVector in elki.index.vafile
Classes in elki.index.vafile with type parameters of type NumberVector Modifier and Type Class Description class
PartialVAFile<V extends NumberVector>
PartialVAFile.static class
PartialVAFile.Factory<V extends NumberVector>
Index factory class.class
VAFile<V extends NumberVector>
Vector-approximation file (VAFile)static class
VAFile.Factory<V extends NumberVector>
Index factory class.Methods in elki.index.vafile with parameters of type NumberVector Modifier and Type Method Description protected static VectorApproximation
PartialVAFile. calculatePartialApproximation(NumberVector dv, java.util.List<DoubleObjPair<DAFile>> daFiles)
Calculate partial vector approximation.protected static void
PartialVAFile. calculateSelectivityCoeffs(java.util.List<DoubleObjPair<DAFile>> daFiles, NumberVector query, double epsilon)
Calculate selectivity coefficients.private void
VALPNormDistance. initializeLookupTable(double[][] splitPositions, NumberVector query, double p)
Initialize the lookup table.Constructors in elki.index.vafile with parameters of type NumberVector Constructor Description VALPNormDistance(double p, double[][] splitPositions, NumberVector query, VectorApproximation queryApprox)
Constructor.Constructor parameters in elki.index.vafile with type arguments of type NumberVector Constructor Description DAFile(Relation<? extends NumberVector> relation, int dimension, int partitions)
Constructor. -
Uses of NumberVector in elki.math
Methods in elki.math with parameters of type NumberVector Modifier and Type Method Description static double
PearsonCorrelation. coefficient(NumberVector x, NumberVector y)
Compute the Pearson product-moment correlation coefficient for two NumberVectors.static double
PearsonCorrelation. weightedCoefficient(NumberVector x, NumberVector y, double[] weights)
Compute the Pearson product-moment correlation coefficient for two NumberVectors.static double
PearsonCorrelation. weightedCoefficient(NumberVector x, NumberVector y, NumberVector weights)
Compute the Pearson product-moment correlation coefficient,Method parameters in elki.math with type arguments of type NumberVector Modifier and Type Method Description static MeanVariance[]
MeanVariance. of(Relation<? extends NumberVector> relation)
Compute the variances of a relation. -
Uses of NumberVector in elki.math.linearalgebra
Classes in elki.math.linearalgebra that implement NumberVector Modifier and Type Class Description class
Centroid
Class to compute the centroid of some data.class
ProjectedCentroid
Centroid only using a subset of dimensions.Methods in elki.math.linearalgebra with type parameters of type NumberVector Modifier and Type Method Description <F extends NumberVector>
FCovarianceMatrix. getMeanVector(Relation<? extends F> relation)
Get the mean as vector.Methods in elki.math.linearalgebra with parameters of type NumberVector Modifier and Type Method Description void
Centroid. put(NumberVector val)
Add a single value with weight 1.0.void
Centroid. put(NumberVector val, double weight)
Add data with a given weight.void
CovarianceMatrix. put(NumberVector val)
Add a single value with weight 1.0.void
CovarianceMatrix. put(NumberVector val, double weight)
Add data with a given weight.void
ProjectedCentroid. put(NumberVector val)
Add a single value with weight 1.0.void
ProjectedCentroid. put(NumberVector val, double weight)
Add data with a given weight.Method parameters in elki.math.linearalgebra with type arguments of type NumberVector Modifier and Type Method Description static Centroid
Centroid. make(Relation<? extends NumberVector> relation, DBIDs ids)
Static constructor from an existing relation.static CovarianceMatrix
CovarianceMatrix. make(Relation<? extends NumberVector> relation)
Static Constructor from a full relation.static CovarianceMatrix
CovarianceMatrix. make(Relation<? extends NumberVector> relation, DBIDs ids)
Static Constructor from a full relation.static ProjectedCentroid
ProjectedCentroid. make(long[] dims, Relation<? extends NumberVector> relation)
Static Constructor from a relation.static ProjectedCentroid
ProjectedCentroid. make(long[] dims, Relation<? extends NumberVector> relation, DBIDs ids)
Static Constructor from a relation. -
Uses of NumberVector in elki.math.linearalgebra.pca
Fields in elki.math.linearalgebra.pca with type parameters of type NumberVector Modifier and Type Field Description private PrimitiveDistance<? super NumberVector>
WeightedCovarianceMatrixBuilder. weightDistance
Holds the distance function used for weight calculation.Method parameters in elki.math.linearalgebra.pca with type arguments of type NumberVector Modifier and Type Method Description PCAResult
AutotuningPCA. processIds(DBIDs ids, Relation<? extends NumberVector> database)
double[][]
CovarianceMatrixBuilder. processIds(DBIDs ids, Relation<? extends NumberVector> database)
Compute covariance matrix for a collection of database IDs.PCAResult
PCARunner. processIds(DBIDs ids, Relation<? extends NumberVector> database)
Run PCA on a collection of database IDs.double[][]
RANSACCovarianceMatrixBuilder. processIds(DBIDs ids, Relation<? extends NumberVector> relation)
double[][]
StandardCovarianceMatrixBuilder. processIds(DBIDs ids, Relation<? extends NumberVector> database)
Compute Covariance Matrix for a collection of database IDs.double[][]
WeightedCovarianceMatrixBuilder. processIds(DBIDs ids, Relation<? extends NumberVector> relation)
Weighted Covariance Matrix for a set of IDs.PCAResult
AutotuningPCA. processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)
PCAResult
PCARunner. processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database)
Run PCA on a QueryResult Collection.default double[][]
CovarianceMatrixBuilder. processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database)
Compute covariance matrix for a QueryResult Collection.default double[][]
CovarianceMatrixBuilder. processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)
Compute covariance matrix for a QueryResult collection.double[][]
WeightedCovarianceMatrixBuilder. processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k)
Compute Covariance Matrix for a QueryResult Collection.default double[][]
CovarianceMatrixBuilder. processRelation(Relation<? extends NumberVector> relation)
Compute covariance matrix for a complete relation. -
Uses of NumberVector in elki.math.spacefillingcurves
Methods in elki.math.spacefillingcurves with parameters of type NumberVector Modifier and Type Method Description byte[]
ZCurveTransformer. asByteArray(NumberVector vector)
Transform a single vector.Constructor parameters in elki.math.spacefillingcurves with type arguments of type NumberVector Constructor Description ZCurveTransformer(Relation<? extends NumberVector> relation, DBIDs ids)
Constructor. -
Uses of NumberVector in elki.math.statistics.intrinsicdimensionality
Method parameters in elki.math.statistics.intrinsicdimensionality with type arguments of type NumberVector Modifier and Type Method Description protected double
LPCAEstimator. estimate(DBIDs ids, Relation<? extends NumberVector> relation)
Returns an ID estimate based on the specified filter for the given point DBID set and relation.double
LPCAEstimator. estimate(KNNSearcher<DBIDRef> knnq, DistanceQuery<? extends NumberVector> distq, DBIDRef cur, int k)
double
LPCAEstimator. estimate(RangeSearcher<DBIDRef> rnq, DistanceQuery<? extends NumberVector> distq, DBIDRef cur, double range)
-
Uses of NumberVector in elki.outlier
Classes in elki.outlier with type parameters of type NumberVector Modifier and Type Class Description class
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspacesstatic class
COP.Par<V extends NumberVector>
Parameterization class.class
SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability.static class
SimpleCOP.Par<V extends NumberVector>
Parameterization class.Method parameters in elki.outlier with type arguments of type NumberVector Modifier and Type Method Description private static void
COP. computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids)
Recompute the centroid of a set.private double
GaussianUniformMixture. loglikelihoodNormal(DBIDs objids, SetDBIDs anomalous, CovarianceMatrix builder, Relation<? extends NumberVector> relation)
Computes the loglikelihood of all normal objects.OutlierResult
GaussianModel. run(Relation<? extends NumberVector> relation)
Run the algorithmOutlierResult
GaussianUniformMixture. run(Relation<? extends NumberVector> relation)
Run the algorithm -
Uses of NumberVector in elki.outlier.anglebased
Classes in elki.outlier.anglebased with type parameters of type NumberVector Modifier and Type Class Description class
ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.static class
ABOD.Par<V extends NumberVector>
Parameterization class.class
FastABOD<V extends NumberVector>
Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.static class
FastABOD.Par<V extends NumberVector>
Parameterization class.class
LBABOD<V extends NumberVector>
LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.static class
LBABOD.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.outlier.clustering
Classes in elki.outlier.clustering with type parameters of type NumberVector Modifier and Type Class Description class
CBLOF<O extends NumberVector>
Cluster-based local outlier factor (CBLOF).static class
CBLOF.Par<O extends NumberVector>
Parameterization class.class
EMOutlier<V extends NumberVector>
Outlier detection algorithm using EM Clustering.static class
EMOutlier.Par<V extends NumberVector>
Parameterization class.class
KMeansOutlierDetection<O extends NumberVector>
Outlier detection by using k-means clustering.static class
KMeansOutlierDetection.Par<O extends NumberVector>
Parameterizer.Methods in elki.outlier.clustering with parameters of type NumberVector Modifier and Type Method Description private double
CBLOF. computeLargeClusterCBLOF(O obj, NumberVectorDistance<? super O> distance, NumberVector clusterMean, Cluster<MeanModel> cluster)
Method parameters in elki.outlier.clustering with type arguments of type NumberVector Modifier and Type Method Description private double
CBLOF. computeSmallClusterCBLOF(O obj, NumberVectorDistance<? super O> distance, java.util.List<NumberVector> largeClusterMeans, Cluster<MeanModel> cluster)
OutlierResult
DBSCANOutlierDetection. run(Database db, Relation<? extends NumberVector> relation)
Runs the algorithm in the timed evaluation part.OutlierResult
GLOSH. run(Database db, Relation<? extends NumberVector> relation)
-
Uses of NumberVector in elki.outlier.density
Fields in elki.outlier.density with type parameters of type NumberVector Modifier and Type Field Description (package private) Relation<? extends NumberVector>
IsolationForest.ForestBuilder. relation
Data relation to useMethods in elki.outlier.density with parameters of type NumberVector Modifier and Type Method Description protected double
IsolationForest. isolationScore(IsolationForest.Node n, NumberVector v)
Search a vector in the tree, return depth (path length)Method parameters in elki.outlier.density with type arguments of type NumberVector Modifier and Type Method Description private java.util.List<HySortOD.Hypercube>
HySortOD. getSortedHypercubes(Relation<? extends NumberVector> relation)
Create and sort hypercubes considering their coordinates.OutlierResult
HySortOD. run(Database db, Relation<? extends NumberVector> relation)
OutlierResult
IsolationForest. run(Relation<? extends NumberVector> relation)
Run the isolation forest algorithm.Constructors in elki.outlier.density with parameters of type NumberVector Constructor Description Hypercube(NumberVector values, double length)
Hypercube constructor.Constructor parameters in elki.outlier.density with type arguments of type NumberVector Constructor Description ForestBuilder(Relation<? extends NumberVector> relation, int subsampleSize, java.util.Random random)
Constructor for the tree builder. -
Uses of NumberVector in elki.outlier.distance
Classes in elki.outlier.distance with type parameters of type NumberVector Modifier and Type Class Description class
HilOut<O extends NumberVector>
Fast Outlier Detection in High Dimensional Spacesstatic class
HilOut.Par<O extends NumberVector>
Parameterization classFields in elki.outlier.distance with type parameters of type NumberVector Modifier and Type Field Description protected NumberVectorDistance<? super NumberVector>
ReferenceBasedOutlierDetection. distance
Distance function used.protected NumberVectorDistance<? super NumberVector>
ReferenceBasedOutlierDetection.Par. distance
The distance function to use.Methods in elki.outlier.distance with parameters of type NumberVector Modifier and Type Method Description protected DoubleDBIDList
ReferenceBasedOutlierDetection. computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)
Computes for each object the distance to one reference point.private double
HilOut.HilbertFeatures. getDimForObject(NumberVector obj, int dim)
Get the (projected) position of the object in dimension dim.Method parameters in elki.outlier.distance with type arguments of type NumberVector Modifier and Type Method Description protected DoubleDBIDList
ReferenceBasedOutlierDetection. computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)
Computes for each object the distance to one reference point.protected DoubleDBIDList
ReferenceBasedOutlierDetection. computeDistanceVector(NumberVector refPoint, Relation<? extends NumberVector> database, PrimitiveDistanceQuery<? super NumberVector> distFunc)
Computes for each object the distance to one reference point.OutlierResult
ReferenceBasedOutlierDetection. run(Relation<? extends NumberVector> relation)
Run the algorithm on the given relation.Constructor parameters in elki.outlier.distance with type arguments of type NumberVector Constructor Description ReferenceBasedOutlierDetection(int k, NumberVectorDistance<? super NumberVector> distance, ReferencePointsHeuristic refp)
Constructor with parameters. -
Uses of NumberVector in elki.outlier.lof
Classes in elki.outlier.lof with type parameters of type NumberVector Modifier and Type Class Description class
ALOCI<V extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".static class
ALOCI.Par<O extends NumberVector>
Parameterization class.class
LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.static class
LDF.Par<O extends NumberVector>
Parameterization class.class
SimpleKernelDensityLOF<O extends NumberVector>
A simple variant of the LOF algorithm, which uses a simple kernel density estimation instead of the local reachability density.static class
SimpleKernelDensityLOF.Par<O extends NumberVector>
Parameterization class.Classes in elki.outlier.lof that implement NumberVector Modifier and Type Class Description (package private) static class
ALOCI.Node
Node of the ALOCI QuadtreeFields in elki.outlier.lof with type parameters of type NumberVector Modifier and Type Field Description private Relation<? extends NumberVector>
ALOCI.ALOCIQuadTree. relation
Relation indexed.Methods in elki.outlier.lof with parameters of type NumberVector Modifier and Type Method Description ALOCI.Node
ALOCI.ALOCIQuadTree. findClosestNode(NumberVector vec, int tlevel)
Find the closest node (of depthtlevel
or above, if there is no node at this depth) for the given vector.private double
ALOCI.ALOCIQuadTree. getShiftedDim(NumberVector obj, int dim, int level)
Shift and wrap a single dimension.Constructor parameters in elki.outlier.lof with type arguments of type NumberVector Constructor Description ALOCIQuadTree(double[] min, double[] max, double[] shift, int nmin, Relation<? extends NumberVector> relation)
Constructor. -
Uses of NumberVector in elki.outlier.meta
Method parameters in elki.outlier.meta with type arguments of type NumberVector Modifier and Type Method Description private java.util.ArrayList<ArrayDBIDs>
HiCS. buildOneDimIndexes(Relation<? extends NumberVector> relation)
Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a listprivate void
HiCS. calculateContrast(Relation<? extends NumberVector> relation, HiCS.HiCSSubspace subspace, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random)
Calculates the actual contrast of a given subspace.private java.util.Set<HiCS.HiCSSubspace>
HiCS. calculateSubspaces(Relation<? extends NumberVector> relation, java.util.ArrayList<ArrayDBIDs> subspaceIndex, java.util.Random random)
Identifies high contrast subspaces in a given full-dimensional database.OutlierResult
FeatureBagging. run(Relation<NumberVector> relation)
Run the algorithm on a data set.OutlierResult
HiCS. run(Relation<? extends NumberVector> relation)
Perform HiCS on a given database. -
Uses of NumberVector in elki.outlier.spatial
Classes in elki.outlier.spatial with type parameters of type NumberVector Modifier and Type Class Description class
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.static class
CTLuGLSBackwardSearchAlgorithm.Par<V extends NumberVector>
Parameterization classclass
CTLuMeanMultipleAttributes<N,O extends NumberVector>
Mean Approach is used to discover spatial outliers with multiple attributes.static class
CTLuMeanMultipleAttributes.Par<N,O extends NumberVector>
Parameterization class.class
CTLuMedianMultipleAttributes<N,O extends NumberVector>
Median Approach is used to discover spatial outliers with multiple attributes.static class
CTLuMedianMultipleAttributes.Par<N,O extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.outlier.subspace
Classes in elki.outlier.subspace with type parameters of type NumberVector Modifier and Type Class Description class
SOD<V extends NumberVector>
Subspace Outlier Degree: Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data.static class
SOD.Par<V extends NumberVector>
Parameterization class.Fields in elki.outlier.subspace with type parameters of type NumberVector Modifier and Type Field Description (package private) Relation<? extends NumberVector>
OUTRES.KernelDensityEstimator. relation
Relation to retrieve data fromMethod parameters in elki.outlier.subspace with type arguments of type NumberVector Modifier and Type Method Description protected java.util.ArrayList<java.util.ArrayList<DBIDs>>
AbstractAggarwalYuOutlier. buildRanges(Relation<? extends NumberVector> relation)
Grid discretization of the data:
Each attribute of data is divided into phi equi-depth ranges.
Each range contains a fraction f=1/phi of the records.private static double[]
SOD. computePerDimensionVariances(Relation<? extends NumberVector> relation, double[] center, DBIDs neighborhood)
Compute the per-dimension variances for the given neighborhood and center.private DoubleDBIDList
OUTRES. initialRange(DBIDRef obj, DBIDs cands, PrimitiveDistance<? super NumberVector> df, double eps, OUTRES.KernelDensityEstimator kernel, ModifiableDoubleDBIDList n)
Initial range query.OutlierResult
AggarwalYuEvolutionary. run(Relation<? extends NumberVector> relation)
Performs the evolutionary algorithm on the given database.OutlierResult
AggarwalYuNaive. run(Relation<? extends NumberVector> relation)
Run the algorithm on the given relation.OutlierResult
OUTRES. run(Relation<? extends NumberVector> relation)
Main loop for OUTRESprivate DoubleDBIDList
OUTRES. subsetNeighborhoodQuery(DoubleDBIDList neighc, DBIDRef dbid, PrimitiveDistance<? super NumberVector> df, double adjustedEps, OUTRES.KernelDensityEstimator kernel, ModifiableDoubleDBIDList n)
Refine neighbors within a subset.Constructor parameters in elki.outlier.subspace with type arguments of type NumberVector Constructor Description EvolutionarySearch(Relation<? extends NumberVector> relation, java.util.ArrayList<java.util.ArrayList<DBIDs>> ranges, java.util.Random random)
Constructor.KernelDensityEstimator(Relation<? extends NumberVector> relation, double eps)
Constructor. -
Uses of NumberVector in elki.outlier.svm
Classes in elki.outlier.svm with type parameters of type NumberVector Modifier and Type Class Description class
LibSVMOneClassOutlierDetection<V extends NumberVector>
Outlier-detection using one-class support vector machines.static class
LibSVMOneClassOutlierDetection.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in elki.outlier.trivial
Method parameters in elki.outlier.trivial with type arguments of type NumberVector Modifier and Type Method Description OutlierResult
TrivialAverageCoordinateOutlier. run(Relation<? extends NumberVector> relation)
Run the actual algorithm. -
Uses of NumberVector in elki.result
Method parameters in elki.result with type arguments of type NumberVector Modifier and Type Method Description private DoubleObjPair<Polygon>
KMLOutputHandler. buildHullsRecursively(Cluster<Model> clu, Hierarchy<Cluster<Model>> hier, java.util.Map<java.lang.Object,DoubleObjPair<Polygon>> hulls, Relation<? extends NumberVector> coords)
Recursively step through the clusters to build the hulls. -
Uses of NumberVector in elki.similarity
Methods in elki.similarity with type parameters of type NumberVector Modifier and Type Method Description <T extends NumberVector>
SpatialPrimitiveDistanceSimilarityQuery<T>Kulczynski1Similarity. instantiate(Relation<T> database)
Methods in elki.similarity that return types with arguments of type NumberVector Modifier and Type Method Description SimpleTypeInformation<? super NumberVector>
AbstractVectorSimilarity. getInputTypeRestriction()
Methods in elki.similarity with parameters of type NumberVector Modifier and Type Method Description double
Kulczynski1Similarity. distance(NumberVector v1, NumberVector v2)
double
Kulczynski1Similarity. similarity(NumberVector v1, NumberVector v2)
double
Kulczynski2Similarity. similarity(NumberVector v1, NumberVector v2)
-
Uses of NumberVector in elki.similarity.kernel
Methods in elki.similarity.kernel with type parameters of type NumberVector Modifier and Type Method Description <T extends NumberVector>
DistanceSimilarityQuery<T>PolynomialKernel. instantiate(Relation<T> database)
Methods in elki.similarity.kernel with parameters of type NumberVector Modifier and Type Method Description double
LinearKernel. distance(NumberVector fv1, NumberVector fv2)
double
PolynomialKernel. distance(NumberVector fv1, NumberVector fv2)
double
LaplaceKernel. similarity(NumberVector o1, NumberVector o2)
double
LinearKernel. similarity(NumberVector o1, NumberVector o2)
double
PolynomialKernel. similarity(NumberVector o1, NumberVector o2)
double
RadialBasisFunctionKernel. similarity(NumberVector o1, NumberVector o2)
double
RationalQuadraticKernel. similarity(NumberVector o1, NumberVector o2)
double
SigmoidKernel. similarity(NumberVector o1, NumberVector o2)
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Uses of NumberVector in elki.timeseries
Methods in elki.timeseries with parameters of type NumberVector Modifier and Type Method Description private double
SigniTrendChangeDetection.Instance. processRow(DBIDRef iter, NumberVector row, ChangePoints changepoints)
Process one row, assuming a constant time interval.Method parameters in elki.timeseries with type arguments of type NumberVector Modifier and Type Method Description ChangePoints
SigniTrendChangeDetection.Instance. run(Relation<NumberVector> relation)
Process a relation.ChangePoints
SigniTrendChangeDetection. run(Relation<NumberVector> relation)
Executes Signi-Trend for given relation -
Uses of NumberVector in elki.utilities.datastructures.arraylike
Methods in elki.utilities.datastructures.arraylike with parameters of type NumberVector Modifier and Type Method Description java.lang.Number
NumberVectorAdapter. get(NumberVector array, int off)
Deprecated.byte
NumberVectorAdapter. getByte(NumberVector array, int off)
double
NumberVectorAdapter. getDouble(NumberVector array, int off)
float
NumberVectorAdapter. getFloat(NumberVector array, int off)
int
NumberVectorAdapter. getInteger(NumberVector array, int off)
long
NumberVectorAdapter. getLong(NumberVector array, int off)
short
NumberVectorAdapter. getShort(NumberVector array, int off)
int
NumberVectorAdapter. size(NumberVector array)
static double[]
ArrayLikeUtil. toPrimitiveDoubleArray(NumberVector obj)
Convert a number vector todouble[]
.static float[]
ArrayLikeUtil. toPrimitiveFloatArray(NumberVector obj)
Convert a number vector tofloat[]
.static int[]
ArrayLikeUtil. toPrimitiveIntegerArray(NumberVector obj)
Convert a number vector toint[]
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Uses of NumberVector in elki.utilities.referencepoints
Methods in elki.utilities.referencepoints that return types with arguments of type NumberVector Modifier and Type Method Description java.util.Collection<? extends NumberVector>
AxisBasedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
FullDatabaseReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
GridBasedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
RandomGeneratedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
RandomSampleReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
ReferencePointsHeuristic. getReferencePoints(Relation<? extends NumberVector> db)
Get the reference points for the given database.java.util.Collection<? extends NumberVector>
StarBasedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
Method parameters in elki.utilities.referencepoints with type arguments of type NumberVector Modifier and Type Method Description java.util.Collection<? extends NumberVector>
AxisBasedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
FullDatabaseReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
GridBasedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
RandomGeneratedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
RandomSampleReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
java.util.Collection<? extends NumberVector>
ReferencePointsHeuristic. getReferencePoints(Relation<? extends NumberVector> db)
Get the reference points for the given database.java.util.Collection<? extends NumberVector>
StarBasedReferencePoints. getReferencePoints(Relation<? extends NumberVector> db)
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Uses of NumberVector in elki.visualization.parallel3d
Classes in elki.visualization.parallel3d with type parameters of type NumberVector Modifier and Type Class Description class
OpenGL3DParallelCoordinates<O extends NumberVector>
Simple JOGL2 based parallel coordinates visualization.static class
OpenGL3DParallelCoordinates.Instance<O extends NumberVector>
Visualizer instance.static class
OpenGL3DParallelCoordinates.Par<O extends NumberVector>
Parameterization class.class
Parallel3DRenderer<O extends NumberVector>
Renderer for 3D parallel plots. -
Uses of NumberVector in elki.visualization.parallel3d.layout
Method parameters in elki.visualization.parallel3d.layout with type arguments of type NumberVector Modifier and Type Method Description static double[]
AbstractLayout3DPC. computeSimilarityMatrix(Dependence sim, Relation<? extends NumberVector> rel)
Compute a column-wise dependency matrix for the given relation.Layout
AbstractLayout3DPC. layout(Relation<? extends NumberVector> rel)
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Uses of NumberVector in elki.visualization.projections
Methods in elki.visualization.projections with type parameters of type NumberVector Modifier and Type Method Description <NV extends NumberVector>
NVAbstractFullProjection. projectRelativeRenderToDataSpace(double[] v, NumberVector.Factory<NV> prototype)
Project a relative vector from rendering space to data space.<NV extends NumberVector>
NVFullProjection. projectRelativeRenderToDataSpace(double[] v, NumberVector.Factory<NV> prototype)
Project a relative vector from rendering space to data space.<NV extends NumberVector>
NVAbstractFullProjection. projectRelativeScaledToDataSpace(double[] v, NumberVector.Factory<NV> prototype)
Project a relative vector from scaled space to data space.<NV extends NumberVector>
NVFullProjection. projectRelativeScaledToDataSpace(double[] v, NumberVector.Factory<NV> prototype)
Project a relative vector from scaled space to data space.<NV extends NumberVector>
NVAbstractFullProjection. projectRenderToDataSpace(double[] v, NumberVector.Factory<NV> prototype)
Project a vector from rendering space to data space.<NV extends NumberVector>
NVFullProjection. projectRenderToDataSpace(double[] v, NumberVector.Factory<NV> prototype)
Project a vector from rendering space to data space.<NV extends NumberVector>
NVAbstractFullProjection. projectScaledToDataSpace(double[] v, NumberVector.Factory<NV> factory)
Project a vector from scaled space to data space.<NV extends NumberVector>
NVFullProjection. projectScaledToDataSpace(double[] v, NumberVector.Factory<NV> factory)
Project a vector from scaled space to data space.Methods in elki.visualization.projections with parameters of type NumberVector Modifier and Type Method Description double[]
AffineProjection. fastProjectDataToRenderSpace(NumberVector data)
double
Projection1D. fastProjectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.double[]
Projection2D. fastProjectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.double[]
ProjectionParallel. fastProjectDataToRenderSpace(NumberVector v)
Fast project a vector from data to render spacedouble
Simple1D. fastProjectDataToRenderSpace(NumberVector data)
double[]
Simple2D. fastProjectDataToRenderSpace(NumberVector data)
double[]
SimpleParallel. fastProjectDataToRenderSpace(NumberVector data)
double[]
AffineProjection. fastProjectDataToScaledSpace(NumberVector data)
double[]
Projection2D. fastProjectDataToScaledSpace(NumberVector data)
Project a data vector from data space to scaled space.double[]
Simple2D. fastProjectDataToScaledSpace(NumberVector data)
double[]
AffineProjection. fastProjectRelativeDataToRenderSpace(NumberVector data)
double
Projection1D. fastProjectRelativeDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.double[]
Projection2D. fastProjectRelativeDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.double
Simple1D. fastProjectRelativeDataToRenderSpace(NumberVector data)
double[]
Simple2D. fastProjectRelativeDataToRenderSpace(NumberVector data)
double[]
AbstractFullProjection. projectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.double[]
FullProjection. projectDataToRenderSpace(NumberVector data)
Project a data vector from data space to rendering space.double[]
AbstractFullProjection. projectDataToScaledSpace(NumberVector data)
Project a data vector from data space to scaled space.double[]
FullProjection. projectDataToScaledSpace(NumberVector data)
Project a data vector from data space to scaled space.double[]
AbstractFullProjection. projectRelativeDataToRenderSpace(NumberVector data)
Project a relative data vector from data space to rendering space.double[]
FullProjection. projectRelativeDataToRenderSpace(NumberVector data)
Project a relative data vector from data space to rendering space.double[]
AbstractFullProjection. projectRelativeDataToScaledSpace(NumberVector data)
Project a relative data vector from data space to scaled space.double[]
FullProjection. projectRelativeDataToScaledSpace(NumberVector data)
Project a relative data vector from data space to scaled space. -
Uses of NumberVector in elki.visualization.projector
Classes in elki.visualization.projector with type parameters of type NumberVector Modifier and Type Class Description class
HistogramProjector<V extends NumberVector>
ScatterPlotProjector is responsible for producing a set of scatterplot visualizations. -
Uses of NumberVector in elki.visualization.svg
Methods in elki.visualization.svg with parameters of type NumberVector Modifier and Type Method Description static org.w3c.dom.Element
SVGHyperSphere. drawCross(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius)
Wireframe "cross" hyperspherestatic org.w3c.dom.Element
SVGHyperSphere. drawEuclidean(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius)
Wireframe "euclidean" hyperspherestatic 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" hyperspherestatic org.w3c.dom.Element
SVGHyperSphere. drawManhattan(SVGPlot svgp, Projection2D proj, NumberVector mid, double radius)
Wireframe "manhattan" hypersphereprivate static java.util.ArrayList<double[]>
SVGHyperCube. getVisibleEdges(Projection2D proj, NumberVector s_min, NumberVector s_max)
Get the visible (non-0) edges of a hypercube -
Uses of NumberVector in elki.visualization.visualizers.histogram
Classes in elki.visualization.visualizers.histogram with type parameters of type NumberVector Modifier and Type Class Description class
ColoredHistogramVisualizer.Instance<NV extends NumberVector>
Instance -
Uses of NumberVector in elki.visualization.visualizers.scatterplot
Fields in elki.visualization.visualizers.scatterplot with type parameters of type NumberVector Modifier and Type Field Description protected Relation<? extends NumberVector>
AbstractScatterplotVisualization. rel
The representation we visualizeprotected ReferencePointsResult<? extends NumberVector>
ReferencePointsVisualization.Instance. result
Serves reference points. -
Uses of NumberVector in elki.visualization.visualizers.scatterplot.selection
Fields in elki.visualization.visualizers.scatterplot.selection with type parameters of type NumberVector Modifier and Type Field Description private AbstractMaterializeKNNPreprocessor<? extends NumberVector>
DistanceFunctionVisualization.Instance. result
The selection result we work onMethods in elki.visualization.visualizers.scatterplot.selection with parameters of type NumberVector Modifier and Type Method Description static org.w3c.dom.Element
DistanceFunctionVisualization. drawCosine(SVGPlot svgp, Projection2D proj, NumberVector mid, double angle)
Visualizes Cosine and ArcCosine distance functions -
Uses of NumberVector in tutorial.clustering
Classes in tutorial.clustering with type parameters of type NumberVector Modifier and Type Class Description class
SameSizeKMeans<V extends NumberVector>
K-means variation that produces equally sized clusters.static class
SameSizeKMeans.Par<V extends NumberVector>
Parameterization class. -
Uses of NumberVector in tutorial.distancefunction
Methods in tutorial.distancefunction that return types with arguments of type NumberVector Modifier and Type Method Description SimpleTypeInformation<? super NumberVector>
MultiLPNorm. getInputTypeRestriction()
SimpleTypeInformation<? super NumberVector>
TutorialDistance. getInputTypeRestriction()
Methods in tutorial.distancefunction with parameters of type NumberVector Modifier and Type Method Description double
MultiLPNorm. distance(NumberVector o1, NumberVector o2)
double
TutorialDistance. distance(NumberVector o1, NumberVector o2)
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