Uses of Interface
de.lmu.ifi.dbs.elki.database.ids.DBIDs

Packages that use DBIDs

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique	Helper classes for the CLIQUE algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood	Spatial outlier neighborhood classes
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.data.type	Data type information, also used for type restrictions.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.datastore	General data store layer API (along the lines of Map<DBID, T> - use everywhere!)
de.lmu.ifi.dbs.elki.database.datastore.memory	Memory data store implementation for ELKI.
de.lmu.ifi.dbs.elki.database.ids	Database object identification and ID group handling API.
de.lmu.ifi.dbs.elki.database.ids.generic	Database object identification and ID group handling - generic implementations.
de.lmu.ifi.dbs.elki.database.ids.integer	Integer-based DBID implementation -- do not use directly - always use DBIDUtil.
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views).
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
de.lmu.ifi.dbs.elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
de.lmu.ifi.dbs.elki.evaluation.scores	Evaluation of rankings and scorings.
de.lmu.ifi.dbs.elki.evaluation.scores.adapter	Adapter classes for ranking and scoring measures.
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.lsh	Locality Sensitive Hashing
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.preference	Indexes storing preference vectors.
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees	Metrical index structures based on the concepts of the M-Tree supporting processing of reverse k nearest neighbor queries by using the k-nn distances of the entries.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp	MkAppTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.flat	FlatRStarTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).
de.lmu.ifi.dbs.elki.math.linearalgebra	Linear Algebra package provides classes and computational methods for operations on matrices.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves.
de.lmu.ifi.dbs.elki.parallel	Parallel processing core for ELKI.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.outlier	Outlier result classes
de.lmu.ifi.dbs.elki.visualization.style	Style management for ELKI visualizations.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection	Visualizers for object selection based on 2D projections.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

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

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

Modifier and Type	Method and Description
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Relation<V> db, DBIDs ids) Runs the pca on the given set of IDs.
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Relation<V> db, DBIDs ids, Vector centroid) Runs the pca on the given set of IDs and for the given centroid.

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

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

Modifier and Type	Method and Description
static ArrayModifiableDBIDs[]	ClusteringAlgorithmUtil.partitionsFromIntegerLabels(DBIDs ids, IntegerDataStore assignment, int k) Collect clusters from their [0;k-1] integer labels.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type DBIDs

Modifier and Type	Method and Description
private MaterializedRelation<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, DBIDs ids, Relation<ParameterizationFunction> relation) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, DBIDs ids) Builds a database for the derivator consisting of the ids in the specified interval.
private LMCLUS.Separation	LMCLUS.findSeparation(Relation<NumberVector> relation, DBIDs currentids, int dimension, Random r) This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.
private void	CASH.initHeap(ObjectHeap<IntegerPriorityObject<CASHInterval>> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) Initializes the heap with the root intervals.
private LinearEquationSystem	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash with parameters of type DBIDs

Modifier and Type	Method and Description
ModifiableDBIDs	CASHIntervalSplit.determineIDs(DBIDs superSetIDs, HyperBoundingBox interval, double d_min, double d_max) Determines the ids belonging to the given interval, i.e. the parameterization functions falling within the interval.
void	CASHInterval.removeIDs(DBIDs ids2) Removes the specified ids from this interval.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan that implement DBIDs

Modifier and Type	Class and Description
static class	COPACNeighborPredicate.COPACModel Model used by COPAC for core point property.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan declared as DBIDs

Modifier and Type	Field and Description
protected DBIDs	EpsilonNeighborPredicate.Instance.ids DBIDs to process
protected DBIDs	AbstractRangeQueryNeighborPredicate.Instance.ids DBIDs to process

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan that return DBIDs

Modifier and Type	Method and Description
DBIDs	NeighborPredicate.Instance.getIDs() Get the IDs the predicate is defined for.
DBIDs	EpsilonNeighborPredicate.Instance.getIDs()
DBIDs	AbstractRangeQueryNeighborPredicate.Instance.getIDs()
DBIDs	ERiCNeighborPredicate.Instance.getNeighbors(DBIDRef reference)

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

Modifier and Type	Method and Description
protected int	LSDBC.expandCluster(int clusterid, WritableIntegerDataStore clusterids, KNNQuery<O> knnq, DBIDs neighbors, double maxkdist, FiniteProgress progress) Set-based expand cluster implementation.
private void	LSDBC.fillDensities(KNNQuery<O> knnq, DBIDs ids, WritableDoubleDataStore dens) Collect all densities into an array for sorting.
boolean	MinPtsCorePredicate.Instance.isCorePoint(DBIDRef point, DBIDs neighbors)
private boolean	LSDBC.isLocalMaximum(double kdist, DBIDs neighbors, WritableDoubleDataStore kdists) Test if a point is a local density maximum.
DBIDIter	ERiCNeighborPredicate.Instance.iterDBIDs(DBIDs neighbors)

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with parameters of type DBIDs

Constructor and Description
Instance(DBIDs ids, DataStore<COPACNeighborPredicate.COPACModel> storage) Constructor.
Instance(DBIDs ids, DataStore<M> storage) Constructor.
Instance(DBIDs ids, DataStore<PCAFilteredResult> storage, Relation<? extends NumberVector> relation) Constructor.
Instance(DBIDs ids, DataStore<PreDeConNeighborPredicate.PreDeConModel> storage) Constructor.
Instance(DBIDs ids, DataStore<PreDeConNeighborPredicate.PreDeConModel> storage) Constructor.
Instance(double epsilon, RangeQuery<?> rq, DBIDs ids) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical declared as DBIDs

Modifier and Type	Field and Description
(package private) DBIDs	PointerHierarchyRepresentationResult.ids The DBIDs in this result.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical that return DBIDs

Modifier and Type	Method and Description
DBIDs	PointerHierarchyRepresentationResult.getDBIDs() Get the clustered DBIDs.

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

Modifier and Type	Method and Description
protected WritableDoubleDataStore	AbstractHDBSCAN.computeCoreDists(DBIDs ids, KNNQuery<O> knnQ, int minPts) Compute the core distances for all objects.
private void	SLINKHDBSCANLinearMemory.step2(DBIDRef id, DBIDs processedIDs, DistanceQuery<? super O> distQuery, DoubleDataStore coredists, WritableDoubleDataStore m) Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.
private void	SLINKHDBSCANLinearMemory.step3(DBIDRef id, WritableDBIDDataStore pi, WritableDoubleDataStore lambda, DBIDs processedIDs, WritableDoubleDataStore m) Third step: Determine the values for P and L
private void	SLINKHDBSCANLinearMemory.step4(DBIDRef id, WritableDBIDDataStore pi, WritableDoubleDataStore lambda, DBIDs processedIDs) Fourth step: Actualize the clusters if necessary

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with parameters of type DBIDs

Constructor and Description
PointerDensityHierarchyRepresentationResult(DBIDs ids, DBIDDataStore parent, DoubleDataStore parentDistance, DoubleDataStore coreDistance) Constructor.
PointerHierarchyRepresentationResult(DBIDs ids, DBIDDataStore parent, DoubleDataStore parentDistance) Constructor.

Uses of DBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction with parameters of type DBIDs

Modifier and Type	Method and Description
void	SimplifiedHierarchyExtraction.TempCluster.addDBIDs(DBIDs ids) Add new objects to the cluster.
private Clustering<DendrogramModel>	ExtractFlatClusteringFromHierarchy.extractClusters(DBIDs ids, DBIDDataStore pi, DoubleDataStore lambda) Extract all clusters from the pi-lambda-representation.
private Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.extractClusters(DBIDs ids, DBIDDataStore pi, DoubleDataStore lambda, DoubleDataStore coredist) Extract all clusters from the pi-lambda-representation.
private Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.extractClusters(DBIDs ids, DBIDDataStore pi, DoubleDataStore lambda, DoubleDataStore coredist) Extract all clusters from the pi-lambda-representation.
private Cluster<DendrogramModel>	ExtractFlatClusteringFromHierarchy.makeCluster(DBIDRef lead, double depth, DBIDs members) Make the cluster for the given object

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

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

Modifier and Type	Method and Description
protected double	CLARA.assignRemainingToNearestCluster(ArrayDBIDs means, DBIDs ids, DBIDs rids, WritableIntegerDataStore assignment, DistanceQuery<V> distQ) Returns a list of clusters.
protected double	KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means, DBIDs ids, WritableDoubleDataStore nearest, WritableDoubleDataStore second, WritableIntegerDataStore assignment, DistanceQuery<V> distQ) Returns a list of clusters.
protected boolean	KMeansBatchedLloyd.assignToNearestCluster(Relation<V> relation, DBIDs ids, List<? extends NumberVector> oldmeans, double[][] meanshift, int[] changesize, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum) Returns a list of clusters.
protected void	KMedoidsPAM.runPAMOptimization(DistanceQuery<V> distQ, DBIDs ids, ArrayModifiableDBIDs medoids, WritableIntegerDataStore assignment) Run the PAM optimization phase.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization that return DBIDs

Modifier and Type	Method and Description
DBIDs	FarthestSumPointsInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	RandomlyChosenInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction)
DBIDs	FirstKInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction)
DBIDs	KMeansPlusPlusInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	PAMInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	FarthestPointsInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	KMedoidsInitialization.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super V> distanceFunction) Choose initial means

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

Modifier and Type	Method and Description
DBIDs	FarthestSumPointsInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	RandomlyChosenInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction)
DBIDs	FirstKInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction)
DBIDs	KMeansPlusPlusInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	PAMInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	FarthestPointsInitialMeans.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distQ)
DBIDs	KMedoidsInitialization.chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super V> distanceFunction) Choose initial means
protected <T> double	KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights, DBIDs ids, T latest, DistanceQuery<? super T> distQ) Initialize the weight list.
protected <T> double	KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights, DBIDs ids, T latest, DistanceQuery<? super T> distQ) Update the weight list.

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

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.optics declared as DBIDs

Modifier and Type	Field and Description
(package private) DBIDs	OPTICSList.Instance.ids IDs to process.
(package private) DBIDs	GeneralizedOPTICS.Instance.ids IDs to process.
private DBIDs	OPTICSHeap.Instance.ids IDs to process.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with type parameters of type DBIDs

Modifier and Type	Field and Description
(package private) DataStore<? extends DBIDs>	FastOPTICS.neighs neighbors of a point

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with parameters of type DBIDs

Modifier and Type	Method and Description
ArrayModifiableDBIDs	ClusterOrder.order(DBIDs ids) Use the cluster order to sort the given collection ids.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with parameters of type DBIDs

Constructor and Description
ClusterOrder(DBIDs ids, String name, String shortname) Constructor

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

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

Modifier and Type	Field and Description
(package private) DBIDs	P3C.Signature.ids Object ids.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return DBIDs

Modifier and Type	Method and Description
private DBIDs	PROCLUS.computeBadMedoids(ArrayDBIDs m_current, ArrayList<PROCLUS.PROCLUSCluster> clusters, int threshold) Computes the bad medoids, where the medoid of a cluster with less than the specified threshold of objects is bad.

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

Modifier and Type	Method and Description
private double	PROCLUS.avgDistance(Vector centroid, DBIDs objectIDs, Relation<V> database, int dimension) Computes the average distance of the objects to the centroid along the specified dimension.
private ArrayDBIDs	PROCLUS.computeM_current(DBIDs m, DBIDs m_best, DBIDs m_bad, Random random) Computes the set of medoids in current iteration.
private boolean	DOC.dimensionIsRelevant(int dimension, Relation<V> 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 DataStore<DoubleDBIDList>	PROCLUS.getLocalities(DBIDs medoids, Relation<V> database, DistanceQuery<V> distFunc, RangeQuery<V> 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<V> distFunc, DBIDs sampleSet, int m, Random random) Returns a piercing set of k medoids from the specified sample set.
private ArrayList<P3C.ClusterCandidate>	P3C.hardClustering(WritableDataStore<double[]> probClusterIGivenX, List<P3C.Signature> clusterCores, DBIDs dbids) Creates a hard clustering from the specified soft membership matrix.
private ArrayDBIDs	PROCLUS.initialSet(DBIDs sampleSet, int k, Random random) Returns a set of k elements from the specified sample set.
private Cluster<SubspaceModel>	DOC.makeCluster(Relation<V> relation, DBIDs C, long[] D) Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
private List<Cluster<Model>>	SUBCLU.runDBSCAN(Relation<V> relation, DBIDs ids, Subspace subspace) Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
protected HashSetModifiableDBIDs	P3C.unionDBIDs(DBIDs[] parts, int start, int end) Compute the union of multiple DBID sets.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type DBIDs

Constructor and Description
Signature(int[] spec, DBIDs ids) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique that return DBIDs

Modifier and Type	Method and Description
DBIDs	CLIQUEUnit.getIds() Returns the ids of the feature vectors this unit contains.

Uses of DBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

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

Modifier and Type	Method and Description
private HashMap<String,DBIDs>	ByLabelClustering.multipleAssignment(Relation<?> data) Assigns the objects of the database to multiple clusters according to their labels.
private HashMap<String,DBIDs>	ByLabelClustering.singleAssignment(Relation<?> data) Assigns the objects of the database to single clusters according to their labels.

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

Modifier and Type	Method and Description
private void	ByLabelHierarchicalClustering.assign(HashMap<String,DBIDs> labelMap, String label, DBIDRef id) Assigns the specified id to the labelMap according to its label
private void	ByLabelClustering.assign(HashMap<String,DBIDs> labelMap, String label, DBIDRef id) Assigns the specified id to the labelMap according to its label

Uses of DBIDs in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain that return DBIDs

Modifier and Type	Method and Description
DBIDs	FDBSCANNeighborPredicate.Instance.getIDs()
DBIDs	FDBSCANNeighborPredicate.Instance.getNeighbors(DBIDRef reference)

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

Modifier and Type	Method and Description
DBIDIter	FDBSCANNeighborPredicate.Instance.iterDBIDs(DBIDs neighbors)
protected C	CenterOfMassMetaClustering.runClusteringAlgorithm(ResultHierarchy hierarchy, Result parent, DBIDs ids, DataStore<DoubleVector> store, int dim, String title) Run a clustering algorithm on a single instance.
protected Clustering<?>	RepresentativeUncertainClustering.runClusteringAlgorithm(ResultHierarchy hierarchy, Result parent, DBIDs ids, DataStore<DoubleVector> store, int dim, String title) Run a clustering algorithm on a single instance.

Uses of DBIDs in de.lmu.ifi.dbs.elki.algorithm.itemsetmining

Methods in de.lmu.ifi.dbs.elki.algorithm.itemsetmining that return DBIDs

Modifier and Type	Method and Description
private DBIDs[]	Eclat.buildIndex(Relation<BitVector> relation, int dim, int minsupp)
private DBIDs	Eclat.mergeJoin(DBIDs first, DBIDs second)

Methods in de.lmu.ifi.dbs.elki.algorithm.itemsetmining with parameters of type DBIDs

Modifier and Type	Method and Description
protected List<SparseItemset>	APRIORI.buildFrequentTwoItemsets(List<OneItemset> oneitems, Relation<BitVector> relation, int dim, int needed, DBIDs ids, ArrayModifiableDBIDs survivors) Build the 2-itemsets.
private void	Eclat.extractItemsets(DBIDs[] idx, int start, int minsupp, List<Itemset> solution)
private void	Eclat.extractItemsets(DBIDs iset, DBIDs[] idx, int[] buf, int depth, int start, int minsupp, List<Itemset> solution)
private void	Eclat.extractItemsets(DBIDs iset, DBIDs[] idx, int[] buf, int depth, int start, int minsupp, List<Itemset> solution)
protected List<? extends Itemset>	APRIORI.frequentItemsets(List<? extends Itemset> candidates, Relation<BitVector> relation, int needed, DBIDs ids, ArrayModifiableDBIDs survivors, int length) Returns the frequent BitSets out of the given BitSets with respect to the given database.
protected List<SparseItemset>	APRIORI.frequentItemsetsSparse(List<SparseItemset> candidates, Relation<BitVector> relation, int needed, DBIDs ids, ArrayModifiableDBIDs survivors, int length) Returns the frequent BitSets out of the given BitSets with respect to the given database.
private DBIDs	Eclat.mergeJoin(DBIDs first, DBIDs second)

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

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

Modifier and Type	Method and Description
private void	DWOF.clusterData(DBIDs ids, RangeQuery<O> rnnQuery, WritableDoubleDataStore radii, WritableDataStore<ModifiableDBIDs> labels) This method applies a density based clustering algorithm.
private void	DWOF.initializeRadii(DBIDs ids, KNNQuery<O> knnq, DistanceQuery<O> distFunc, WritableDoubleDataStore radii) This method prepares a container for the radii of the objects and initializes radii according to the equation: initialRadii of a certain object = (absoluteMinDist of all objects) * (avgDist of the object) / (minAvgDist of all objects)
private double	GaussianUniformMixture.loglikelihoodAnomalous(DBIDs anomalousObjs) Loglikelihood anomalous objects.
private double	GaussianUniformMixture.loglikelihoodNormal(DBIDs objids, Relation<V> database) Computes the loglikelihood of all normal objects.
private int	DWOF.updateSizes(DBIDs ids, WritableDataStore<ModifiableDBIDs> labels, WritableIntegerDataStore newSizes) This method updates each object's cluster size after the clustering step.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.lof that return DBIDs

Modifier and Type	Method and Description
protected DBIDs	INFLO.getKNN(DBIDIter q, KNNQuery<O> knnQuery, WritableDataStore<ModifiableDBIDs> knns, WritableDoubleDataStore density) Get the (forward only) kNN of an object, including the query point

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

Modifier and Type	Method and Description
protected void	COF.computeAverageChainingDistances(KNNQuery<O> knnq, DistanceQuery<O> dq, DBIDs ids, WritableDoubleDataStore acds) Computes the average chaining distance, the average length of a path through the given set of points to each target.
private void	COF.computeCOFScores(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore acds, WritableDoubleDataStore cofs, DoubleMinMax cofminmax) Compute Connectivity outlier factors.
protected void	FlexibleLOF.computeLOFs(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore lrds, WritableDoubleDataStore lofs, DoubleMinMax lofminmax) Computes the Local outlier factor (LOF) of the specified objects.
private void	LOF.computeLOFScores(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore lrds, WritableDoubleDataStore lofs, DoubleMinMax lofminmax) Compute local outlier factors.
protected void	FlexibleLOF.computeLRDs(KNNQuery<O> knnq, DBIDs ids, WritableDoubleDataStore lrds) Computes the local reachability density (LRD) of the specified objects.
private void	LOF.computeLRDs(KNNQuery<O> knnq, DBIDs ids, WritableDoubleDataStore lrds) Compute local reachability distances.
protected void	KDEOS.computeOutlierScores(KNNQuery<O> knnq, DBIDs ids, WritableDataStore<double[]> densities, WritableDoubleDataStore kdeos, DoubleMinMax minmax) Compute the final KDEOS scores.
private void	SimplifiedLOF.computeSimplifiedLOFs(DBIDs ids, KNNQuery<O> knnq, WritableDoubleDataStore slrds, WritableDoubleDataStore lofs, DoubleMinMax lofminmax) Compute the simplified LOF factors.
private void	SimplifiedLOF.computeSimplifiedLRDs(DBIDs ids, KNNQuery<O> knnq, WritableDoubleDataStore lrds) Compute the simplified reachability densities.
protected FlexibleLOF.LOFResult<O>	FlexibleLOF.doRunInTime(DBIDs ids, KNNQuery<O> kNNRefer, KNNQuery<O> kNNReach, StepProgress stepprog) Performs the Generalized LOF_SCORE algorithm on the given database and returns a FlexibleLOF.LOFResult encapsulating information that may be needed by an OnlineLOF algorithm.
protected void	KDEOS.estimateDensities(Relation<O> rel, KNNQuery<O> knnq, DBIDs ids, WritableDataStore<double[]> densities) Perform the kernel density estimation step.
private void	OnlineLOF.LOFKNNListener.kNNsInserted(DBIDs insertions, DBIDs updates1, DBIDs updates2, FlexibleLOF.LOFResult<O> lofResult) Invoked after kNNs have been inserted and updated, updates the result.
private void	OnlineLOF.LOFKNNListener.kNNsRemoved(DBIDs deletions, DBIDs updates1, DBIDs updates2, FlexibleLOF.LOFResult<O> lofResult) Invoked after kNNs have been removed and updated, updates the result.
private ArrayModifiableDBIDs	OnlineLOF.LOFKNNListener.mergeIDs(List<? extends DoubleDBIDList> queryResults, DBIDs... ids) Merges the ids of the query result with the specified ids.
protected void	LOCI.precomputeInterestingRadii(DBIDs ids, RangeQuery<O> rangeQuery, WritableDataStore<LOCI.DoubleIntArrayList> interestingDistances) Preprocessing step: determine the radii of interest for each point.
private void	OnlineLOF.LOFKNNListener.recomputeLOFs(DBIDs ids, FlexibleLOF.LOFResult<O> lofResult) Recomputes the lofs of the specified ids.

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

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood with type parameters of type DBIDs

Modifier and Type	Field and Description
protected DataStore<DBIDs>	AbstractPrecomputedNeighborhood.store The data

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood that return DBIDs

Modifier and Type	Method and Description
DBIDs	NeighborSetPredicate.getNeighborDBIDs(DBIDRef reference) Get the neighbors of a reference object for DBSCAN.
DBIDs	AbstractPrecomputedNeighborhood.getNeighborDBIDs(DBIDRef reference)

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood that return types with arguments of type DBIDs

Modifier and Type	Method and Description
private DataStore<DBIDs>	ExtendedNeighborhood.Factory.extendNeighborhood(Database database, Relation<? extends O> relation) Method to load the external neighbors.
private DataStore<DBIDs>	ExternalNeighborhood.Factory.loadNeighbors(Database database, Relation<?> relation) Method to load the external neighbors.

Constructor parameters in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood with type arguments of type DBIDs

Constructor and Description
AbstractPrecomputedNeighborhood(DataStore<DBIDs> store) Constructor.
ExtendedNeighborhood(DataStore<DBIDs> store) Constructor.
ExternalNeighborhood(DataStore<DBIDs> store) Constructor.
PrecomputedKNearestNeighborNeighborhood(DataStore<DBIDs> store) Constructor.

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

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

Modifier and Type	Field and Description
(package private) ArrayList<ArrayList<DBIDs>>	AggarwalYuEvolutionary.EvolutionarySearch.ranges Database ranges.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace that return DBIDs

Modifier and Type	Method and Description
protected DBIDs	AbstractAggarwalYuOutlier.computeSubspace(ArrayList<IntIntPair> subspace, ArrayList<ArrayList<DBIDs>> ranges) Method to get the ids in the given subspace.
protected DBIDs	AbstractAggarwalYuOutlier.computeSubspaceForGene(short[] gene, ArrayList<ArrayList<DBIDs>> ranges) Get the DBIDs in the current subspace.
private DBIDs	SOD.getNearestNeighbors(Relation<V> relation, SimilarityQuery<V> simQ, DBIDRef queryObject) Get the k nearest neighbors in terms of the shared nearest neighbor distance.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace that return types with arguments of type DBIDs

Modifier and Type	Method and Description
protected ArrayList<ArrayList<DBIDs>>	AbstractAggarwalYuOutlier.buildRanges(Relation<V> relation) Grid discretization of the data: Each attribute of data is divided into phi equi-depth ranges.

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

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

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

Modifier and Type	Method and Description
protected DBIDs	AbstractAggarwalYuOutlier.computeSubspace(ArrayList<IntIntPair> subspace, ArrayList<ArrayList<DBIDs>> ranges) Method to get the ids in the given subspace.
protected DBIDs	AbstractAggarwalYuOutlier.computeSubspaceForGene(short[] gene, ArrayList<ArrayList<DBIDs>> ranges) Get the DBIDs in the current subspace.

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

Constructor and Description
EvolutionarySearch(Relation<V> relation, ArrayList<ArrayList<DBIDs>> ranges, int m, Random random) Constructor.

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

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

Modifier and Type	Method and Description
static Relation<NumberVector>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.
(package private) void	ComputeKNNOutlierScores.writeResult(PrintStream out, DBIDs ids, OutlierResult result, ScalingFunction scaling, String label) Write a single output line.

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

Fields in de.lmu.ifi.dbs.elki.data declared as DBIDs

Modifier and Type	Field and Description
private DBIDs	Cluster.ids Cluster data.

Methods in de.lmu.ifi.dbs.elki.data that return DBIDs

Modifier and Type	Method and Description
DBIDs	Cluster.getIDs() Access group object

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

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

Constructors in de.lmu.ifi.dbs.elki.data with parameters of type DBIDs

Constructor and Description
Cluster(DBIDs ids) Constructor without hierarchy information and name and model
Cluster(DBIDs ids, boolean noise) Constructor without hierarchy information and name and model
Cluster(DBIDs ids, boolean noise, M model) Constructor without hierarchy information and name
Cluster(DBIDs ids, M model) Constructor without hierarchy information and name
Cluster(String name, DBIDs ids) Constructor without hierarchy information and model
Cluster(String name, DBIDs ids, boolean noise) Constructor without hierarchy information and model
Cluster(String name, DBIDs ids, boolean noise, M model) Full constructor
Cluster(String name, DBIDs ids, M model) Constructor without hierarchy information.

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

Fields in de.lmu.ifi.dbs.elki.data.model declared as DBIDs

Modifier and Type	Field and Description
(package private) DBIDs	CoreObjectsModel.core Objects that are part of the cluster core.
private DBIDs	BiclusterWithInversionsModel.invertedRows The ids of inverted rows.

Methods in de.lmu.ifi.dbs.elki.data.model that return DBIDs

Modifier and Type	Method and Description
DBIDs	CoreObjectsModel.getCoreObjects() Get the core object IDs.
DBIDs	BiclusterWithInversionsModel.getInvertedRows() Provides a copy of the inverted column IDs.

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

Modifier and Type	Method and Description
static NumberVector	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends NumberVector> relation, DBIDs ids) Get the representative vector for a cluster model, or compute the centroid.
static <V extends NumberVector> V	ModelUtil.getPrototypeOrCentroid(Model model, Relation<? extends V> relation, DBIDs ids, NumberVector.Factory<V> factory) Get the representative vector for a cluster model, or compute the centroid.
void	BiclusterWithInversionsModel.setInvertedRows(DBIDs invertedRows) Sets the ids of the inverted rows.

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type DBIDs

Constructor and Description
BiclusterWithInversionsModel(int[] colIDs, DBIDs invertedRows)
CoreObjectsModel(DBIDs core) Constructor.

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

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

Modifier and Type	Field and Description
static SimpleTypeInformation<DBIDs>	TypeUtil.DBIDS Database ID lists.

Uses of DBIDs in de.lmu.ifi.dbs.elki.database

Methods in de.lmu.ifi.dbs.elki.database that return DBIDs

Modifier and Type	Method and Description
DBIDs	HashmapDatabase.insert(ObjectBundle objpackages)
DBIDs	UpdatableDatabase.insert(ObjectBundle objpackages) Inserts the given object(s) and their associations into the database.

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

Modifier and Type	Method and Description
MultipleObjectsBundle	HashmapDatabase.delete(DBIDs ids) Removes the objects from the database (by calling HashmapDatabase.doDelete(DBIDRef) for each object) and indexes and fires a deletion event.
ObjectBundle	UpdatableDatabase.delete(DBIDs ids) Removes and returns the specified objects with the given ids from the database.
private void	DatabaseEventManager.fireObjectsChanged(DBIDs objects, DatabaseEventManager.Type type) Handles a DataStoreEvent with the specified type.
void	DatabaseEventManager.fireObjectsInserted(DBIDs insertions) Event when new objects are inserted.
protected void	DatabaseEventManager.fireObjectsRemoved(DBIDs deletions) Event when objects were removed / deleted.
void	DatabaseEventManager.fireObjectsUpdated(DBIDs updates) Event when objects have changed / were updated.
void	ProxyDatabase.setDBIDs(DBIDs ids) Set the DBIDs to use.

Constructors in de.lmu.ifi.dbs.elki.database with parameters of type DBIDs

Constructor and Description
ProxyDatabase(DBIDs ids) Constructor.
ProxyDatabase(DBIDs ids, Database database) Constructor, proxying all relations of an existing database.
ProxyDatabase(DBIDs ids, Iterable<Relation<?>> relations) Constructor.
ProxyDatabase(DBIDs ids, Relation<?>... relations) Constructor.

Uses of DBIDs in de.lmu.ifi.dbs.elki.database.datastore

Fields in de.lmu.ifi.dbs.elki.database.datastore declared as DBIDs

Modifier and Type	Field and Description
private DBIDs	DataStoreEvent.inserts Insertions.
private DBIDs	DataStoreEvent.removals Removals.
private DBIDs	DataStoreEvent.updates Updates.

Methods in de.lmu.ifi.dbs.elki.database.datastore that return DBIDs

Modifier and Type	Method and Description
DBIDs	DataStoreEvent.getInserts() Get the inserted objects.
DBIDs	DataStoreEvent.getRemovals() Get the removed objects.
DBIDs	DataStoreEvent.getUpdates() Get the updates objects.

Methods in de.lmu.ifi.dbs.elki.database.datastore with parameters of type DBIDs

Modifier and Type	Method and Description
static DataStoreEvent	DataStoreEvent.insertionEvent(DBIDs inserts) Insertion event.
static WritableDBIDDataStore	DataStoreUtil.makeDBIDStorage(DBIDs ids, int hints) Make a new storage, to associate the given ids with an object of class dataclass.
WritableDBIDDataStore	DataStoreFactory.makeDBIDStorage(DBIDs ids, int hints) Make a new storage, to associate the given ids with an object of class dataclass.
static WritableDoubleDataStore	DataStoreUtil.makeDoubleStorage(DBIDs ids, int hints) Make a new storage, to associate the given ids with an object of class dataclass.
WritableDoubleDataStore	DataStoreFactory.makeDoubleStorage(DBIDs ids, int hints) Make a new storage, to associate the given ids with an object of class dataclass.
static WritableDoubleDataStore	DataStoreUtil.makeDoubleStorage(DBIDs ids, int hints, double def) Make a new storage, to associate the given ids with an object of class dataclass.
WritableDoubleDataStore	DataStoreFactory.makeDoubleStorage(DBIDs ids, int hints, double def) Make a new storage, to associate the given ids with an object of class dataclass.
static WritableIntegerDataStore	DataStoreUtil.makeIntegerStorage(DBIDs ids, int hints) Make a new storage, to associate the given ids with an object of class dataclass.
WritableIntegerDataStore	DataStoreFactory.makeIntegerStorage(DBIDs ids, int hints) Make a new storage, to associate the given ids with an object of class dataclass.
static WritableIntegerDataStore	DataStoreUtil.makeIntegerStorage(DBIDs ids, int hints, int def) Make a new storage, to associate the given ids with an object of class dataclass.
WritableIntegerDataStore	DataStoreFactory.makeIntegerStorage(DBIDs ids, int hints, int def) Make a new storage, to associate the given ids with an object of class dataclass.
static WritableRecordStore	DataStoreUtil.makeRecordStorage(DBIDs ids, int hints, Class<?>... dataclasses) Make a new record storage, to associate the given ids with an object of class dataclass.
WritableRecordStore	DataStoreFactory.makeRecordStorage(DBIDs ids, int hints, Class<?>... dataclasses) Make a new record storage, to associate the given ids with an object of class dataclass.
static <T> WritableDataStore<T>	DataStoreUtil.makeStorage(DBIDs ids, int hints, Class<? super T> dataclass) Make a new storage, to associate the given ids with an object of class dataclass.
<T> WritableDataStore<T>	DataStoreFactory.makeStorage(DBIDs ids, int hints, Class<? super T> dataclass) Make a new storage, to associate the given ids with an object of class dataclass.
static DataStoreEvent	DataStoreEvent.removalEvent(DBIDs removals) Removal event.
static DataStoreEvent	DataStoreEvent.updateEvent(DBIDs updates) Update event.

Constructors in de.lmu.ifi.dbs.elki.database.datastore with parameters of type DBIDs

Constructor and Description
DataStoreEvent(DBIDs inserts, DBIDs removals, DBIDs updates) Constructor.

Uses of DBIDs in de.lmu.ifi.dbs.elki.database.datastore.memory

Methods in de.lmu.ifi.dbs.elki.database.datastore.memory with parameters of type DBIDs

Modifier and Type	Method and Description
WritableDBIDDataStore	MemoryDataStoreFactory.makeDBIDStorage(DBIDs ids, int hints)
WritableDoubleDataStore	MemoryDataStoreFactory.makeDoubleStorage(DBIDs ids, int hints)
WritableDoubleDataStore	MemoryDataStoreFactory.makeDoubleStorage(DBIDs ids, int hints, double def)
WritableIntegerDataStore	MemoryDataStoreFactory.makeIntegerStorage(DBIDs ids, int hints)
WritableIntegerDataStore	MemoryDataStoreFactory.makeIntegerStorage(DBIDs ids, int hints, int def)
WritableRecordStore	MemoryDataStoreFactory.makeRecordStorage(DBIDs ids, int hints, Class<?>... dataclasses)
<T> WritableDataStore<T>	MemoryDataStoreFactory.makeStorage(DBIDs ids, int hints, Class<? super T> dataclass)

Uses of DBIDs in de.lmu.ifi.dbs.elki.database.ids

Subinterfaces of DBIDs in de.lmu.ifi.dbs.elki.database.ids

Modifier and Type	Interface and Description
interface	ArrayDBIDs Interface for array based DBIDs.
interface	ArrayModifiableDBIDs Array-oriented implementation of a modifiable DBID collection.
interface	ArrayStaticDBIDs Unmodifiable, indexed DBIDs.
interface	DBID Database ID object.
interface	DBIDPair Immutable pair of two DBIDs.
interface	DBIDRange Static DBID range.
interface	DBIDVar (Persistent) variable storing a DBID reference.
interface	DoubleDBIDList Collection of double values associated with objects.
interface	HashSetDBIDs Hash-organized DBIDs
interface	HashSetModifiableDBIDs Set-oriented implementation of a modifiable DBID collection.
interface	KNNList Interface for kNN results.
interface	ModifiableDBIDs Interface for a generic modifiable DBID collection.
interface	ModifiableDoubleDBIDList Modifiable API for Distance-DBID results
interface	SetDBIDs Interface for DBIDs that support fast "set" operations, in particular "contains" lookups.
interface	StaticDBIDs Unmodifiable DBIDs.

Classes in de.lmu.ifi.dbs.elki.database.ids that implement DBIDs

Modifier and Type	Class and Description
class	EmptyDBIDs Empty DBID collection.

Methods in de.lmu.ifi.dbs.elki.database.ids that return DBIDs

Modifier and Type	Method and Description
static DBIDs	DBIDUtil.randomSample(DBIDs ids, double rate, Random random) Produce a random sample of the given DBIDs.
static DBIDs	DBIDUtil.randomSample(DBIDs ids, double rate, RandomFactory random) Produce a random sample of the given DBIDs.

Methods in de.lmu.ifi.dbs.elki.database.ids with parameters of type DBIDs

Modifier and Type	Method and Description
boolean	ModifiableDBIDs.addDBIDs(DBIDs ids) Add DBIDs to collection.
static DBIDRange	DBIDUtil.assertRange(DBIDs ids) Assert that the presented ids constitute a continuous DBIDRange.
static ModifiableDBIDs	DBIDUtil.difference(DBIDs ids1, DBIDs ids2) Returns the difference of the two specified collection of IDs.
static ArrayDBIDs	DBIDUtil.ensureArray(DBIDs ids) Ensure that the given DBIDs are array-indexable.
static ModifiableDBIDs	DBIDUtil.ensureModifiable(DBIDs ids) Ensure modifiable.
static SetDBIDs	DBIDUtil.ensureSet(DBIDs ids) Ensure that the given DBIDs support fast "contains" operations.
private static int	DBIDUtil.internalIntersectionSize(DBIDs first, DBIDs second) Compute the set intersection size of two sets.
static ModifiableDBIDs	DBIDUtil.intersection(DBIDs first, DBIDs second) Compute the set intersection of two sets.
static int	DBIDUtil.intersectionSize(DBIDs first, DBIDs second) Compute the set intersection size of two sets.
static StaticDBIDs	DBIDUtil.makeUnmodifiable(DBIDs existing) Wrap an existing DBIDs collection to be unmodifiable.
ArrayModifiableDBIDs	DBIDFactory.newArray(DBIDs existing) Make a new (modifiable) array of DBIDs.
static ArrayModifiableDBIDs	DBIDUtil.newArray(DBIDs existing) Make a new (modifiable) array of DBIDs.
HashSetModifiableDBIDs	DBIDFactory.newHashSet(DBIDs existing) Make a new (modifiable) hash set of DBIDs.
static HashSetModifiableDBIDs	DBIDUtil.newHashSet(DBIDs existing) Make a new (modifiable) hash set of DBIDs.
static DBIDs	DBIDUtil.randomSample(DBIDs ids, double rate, Random random) Produce a random sample of the given DBIDs.
static DBIDs	DBIDUtil.randomSample(DBIDs ids, double rate, RandomFactory random) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, int seed) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, Long seed) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, Random random) Produce a random sample of the given DBIDs.
static ModifiableDBIDs	DBIDUtil.randomSample(DBIDs source, int k, RandomFactory rnd) Produce a random sample of the given DBIDs.
static DBIDVar	DBIDUtil.randomSample(DBIDs ids, Random random) Draw a single random sample.
static DBIDVar	DBIDUtil.randomSample(DBIDs ids, RandomFactory random) Draw a single random sample.
static ArrayDBIDs[]	DBIDUtil.randomSplit(DBIDs oids, int p, Random random) Randomly split IDs into p partitions of almost-equal size.
static ArrayDBIDs[]	DBIDUtil.randomSplit(DBIDs ids, int p, RandomFactory rnd) Randomly split IDs into p partitions of almost-equal size.
boolean	ModifiableDBIDs.removeDBIDs(DBIDs ids) Remove DBIDs from collection.
boolean	HashSetModifiableDBIDs.retainAll(DBIDs set) Retain all elements that also are in the second set.
static void	DBIDUtil.symmetricIntersection(DBIDs first, DBIDs second, HashSetModifiableDBIDs firstonly, HashSetModifiableDBIDs intersection, HashSetModifiableDBIDs secondonly) Compute the set symmetric intersection of two sets.
static String	DBIDUtil.toString(DBIDs ids) Format a DBID as string.
static ModifiableDBIDs	DBIDUtil.union(DBIDs ids1, DBIDs ids2) Returns the union of the two specified collection of IDs.

Uses of DBIDs in de.lmu.ifi.dbs.elki.database.ids.generic

Classes in de.lmu.ifi.dbs.elki.database.ids.generic that implement DBIDs

Modifier and Type	Class and Description
class	KNNSubList Sublist of an existing result to contain only the first k elements.
class	MaskedDBIDs View on an ArrayDBIDs masked using a BitMask for efficient mask changing.
class	UnmodifiableArrayDBIDs Unmodifiable wrapper for DBIDs.
class	UnmodifiableDBIDs Unmodifiable wrapper for DBIDs.

Fields in de.lmu.ifi.dbs.elki.database.ids.generic declared as DBIDs

Modifier and Type	Field and Description
private DBIDs	UnmodifiableDBIDs.inner The DBIDs we wrap.

Constructors in de.lmu.ifi.dbs.elki.database.ids.generic with parameters of type DBIDs

Constructor and Description
UnmodifiableDBIDs(DBIDs inner) Constructor.

Uses of DBIDs in de.lmu.ifi.dbs.elki.database.ids.integer

Subinterfaces of DBIDs in de.lmu.ifi.dbs.elki.database.ids.integer

Modifier and Type	Interface and Description
interface	IntegerArrayDBIDs Trivial combination interface.
(package private) interface	IntegerArrayStaticDBIDs Combination of ArrayStaticDBIDs and IntegerDBIDs.
interface	IntegerDBIDKNNList Combination interface for KNNList and IntegerDBIDs.
interface	IntegerDBIDs Integer DBID collection.

Classes in de.lmu.ifi.dbs.elki.database.ids.integer that implement DBIDs

Modifier and Type	Class and Description
(package private) class	ArrayModifiableIntegerDBIDs Class using a primitive int[] array as storage.
private class	ArrayModifiableIntegerDBIDs.Slice Slice of an array.
(package private) class	ArrayStaticIntegerDBIDs Static (no modifications allowed) set of Database Object IDs.
private class	ArrayStaticIntegerDBIDs.Slice Slice of an array.
class	DoubleIntegerDBIDKNNList kNN list, but without automatic sorting.
(package private) class	DoubleIntegerDBIDList Class to store double distance, integer DBID results.
(package private) class	DoubleIntegerDBIDListKNNHeap Track the k nearest neighbors, with insertion sort to ensure the correct order.
(package private) class	DoubleIntegerDBIDPairKNNListHeap KNN Heap implemented using a list of DoubleInt pair objects.
(package private) class	DoubleIntegerDBIDPairList Class to store double distance, integer DBID results.
(package private) class	IntegerDBID Database ID object.
class	IntegerDBIDKNNSubList Sublist of an existing result to contain only the first k elements.
(package private) class	IntegerDBIDPair DBID pair using two ints for storage.
(package private) class	IntegerDBIDRange Representing a DBID range allocation.
(package private) class	IntegerDBIDVar Variable for storing a single DBID reference.
(package private) class	TroveHashSetModifiableDBIDs Implementation using GNU Trove Int Hash Sets.
class	UnmodifiableIntegerArrayDBIDs Unmodifiable wrapper for DBIDs.
class	UnmodifiableIntegerDBIDs Unmodifiable wrapper for DBIDs.

Methods in de.lmu.ifi.dbs.elki.database.ids.integer with parameters of type DBIDs

Modifier and Type	Method and Description
boolean	TroveHashSetModifiableDBIDs.addDBIDs(DBIDs ids)
boolean	ArrayModifiableIntegerDBIDs.addDBIDs(DBIDs ids)
ArrayModifiableDBIDs	AbstractIntegerDBIDFactory.newArray(DBIDs existing)
HashSetModifiableDBIDs	AbstractIntegerDBIDFactory.newHashSet(DBIDs existing)
boolean	TroveHashSetModifiableDBIDs.removeDBIDs(DBIDs ids)
boolean	ArrayModifiableIntegerDBIDs.removeDBIDs(DBIDs ids)
boolean	TroveHashSetModifiableDBIDs.retainAll(DBIDs set)

Constructors in de.lmu.ifi.dbs.elki.database.ids.integer with parameters of type DBIDs

Constructor and Description
ArrayModifiableIntegerDBIDs(DBIDs existing) Constructor.
TroveHashSetModifiableDBIDs(DBIDs existing) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.database.relation declared as DBIDs

Modifier and Type	Field and Description
private DBIDs	DBIDView.ids The ids object
private DBIDs	ProxyView.idview The DBIDs we contain

Methods in de.lmu.ifi.dbs.elki.database.relation that return DBIDs

Modifier and Type	Method and Description
DBIDs	ProxyView.getDBIDs()
DBIDs	ConvertToStringView.getDBIDs()
DBIDs	Relation.getDBIDs() Get the IDs the query is defined for.
DBIDs	DBIDView.getDBIDs()
DBIDs	ProjectedView.getDBIDs()

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

Modifier and Type	Method and Description
void	ProxyView.setDBIDs(DBIDs ids) Set the DBIDs to use.
void	DBIDView.setDBIDs(DBIDs ids) Set the DBIDs of the view.
static double[]	RelationUtil.variances(Relation<? extends NumberVector> database, NumberVector centroid, DBIDs ids) Determines the variances in each dimension of the specified objects stored in the given database.

Constructors in de.lmu.ifi.dbs.elki.database.relation with parameters of type DBIDs

Constructor and Description
DBIDView(DBIDs ids) Constructor.
MaterializedDoubleRelation(DBIDs ids) Constructor.
MaterializedDoubleRelation(DBIDs ids, String name) Constructor.
MaterializedDoubleRelation(DBIDs ids, String name, DoubleDataStore content) Constructor.
MaterializedDoubleRelation(String name, String shortname, DoubleDataStore content, DBIDs ids) Constructor.
MaterializedRelation(SimpleTypeInformation<O> type, DBIDs ids) Constructor.
MaterializedRelation(SimpleTypeInformation<O> type, DBIDs ids, String name) Constructor.
MaterializedRelation(SimpleTypeInformation<O> type, DBIDs ids, String name, DataStore<O> content) Constructor.
MaterializedRelation(String name, String shortname, SimpleTypeInformation<O> type, DataStore<O> content, DBIDs ids) Constructor.
ProxyView(DBIDs idview, Relation<O> inner) Constructor.

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

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

Modifier and Type	Method and Description
protected static double	SharedNearestNeighborJaccardDistanceFunction.Instance.jaccardCoefficient(DBIDs neighbors1, DBIDs neighbors2) Compute the Jaccard coefficient

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

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

Modifier and Type	Method and Description
protected static int	FractionalSharedNearestNeighborSimilarityFunction.Instance.countSharedNeighbors(DBIDs neighbors1, DBIDs neighbors2) Compute the intersection size.
protected static int	SharedNearestNeighborSimilarityFunction.countSharedNeighbors(DBIDs neighbors1, DBIDs neighbors2) Compute the intersection size

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

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

Modifier and Type	Method and Description
Matrix	KernelMatrix.getSubColumn(DBIDRef i1, DBIDs ids) Deprecated.
Matrix	KernelMatrix.getSubMatrix(DBIDs ids) Returns a sub kernel matrix for all objects in ids

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type DBIDs

Constructor and Description
KernelMatrix(PrimitiveSimilarityFunction<? super O> kernelFunction, Relation<? extends O> relation, DBIDs ids) Provides a new kernel matrix.
KernelMatrix(SimilarityQuery<? super O> kernelFunction, Relation<? extends O> relation, DBIDs ids) Provides a new kernel matrix.
SortedArrayMap(DBIDs ids)

Uses of DBIDs in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments

Fields in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments declared as DBIDs

Modifier and Type	Field and Description
protected DBIDs	Segment.objIDs IDs in segment, for object segments.

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments that return DBIDs

Modifier and Type	Method and Description
DBIDs	Segment.getDBIDs() Get the DBIDs of objects contained in this segment.

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments with parameters of type DBIDs

Modifier and Type	Method and Description
private void	Segments.makeOrUpdateSegment(int[] path, DBIDs ids, int pairsize)

Uses of DBIDs in de.lmu.ifi.dbs.elki.evaluation.outlier

Methods in de.lmu.ifi.dbs.elki.evaluation.outlier with parameters of type DBIDs

Modifier and Type	Method and Description
private XYCurve	OutlierPrecisionAtKCurve.computePrecisionResult(int size, SetDBIDs positiveids, DBIDs order)
private OutlierROCCurve.ROCResult	OutlierROCCurve.computeROCResult(int size, SetDBIDs positiveids, DBIDs order)
protected JudgeOutlierScores.ScoreResult	JudgeOutlierScores.computeScore(DBIDs ids, DBIDs outlierIds, OutlierResult or) Evaluate a single outlier score result.
private EvaluationResult	OutlierRankingEvaluation.evaluateOrderingResult(int size, SetDBIDs positiveids, DBIDs order)

Uses of DBIDs in de.lmu.ifi.dbs.elki.evaluation.scores

Methods in de.lmu.ifi.dbs.elki.evaluation.scores with parameters of type DBIDs

Modifier and Type	Method and Description
double	ScoreEvaluation.evaluate(DBIDs ids, DoubleDBIDList nei) Evaluate given a list of positives and a scoring.
double	AbstractScoreEvaluation.evaluate(DBIDs ids, DoubleDBIDList nei)
double	ScoreEvaluation.evaluate(DBIDs ids, OutlierResult outlier) Evaluate given a set of positives and a scoring.
double	AbstractScoreEvaluation.evaluate(DBIDs ids, OutlierResult outlier)

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

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

Modifier and Type	Field and Description
private DBIDs	DBIDsTest.set DBID set.

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

Constructor and Description
DBIDsTest(DBIDs set) Constructor.

Uses of DBIDs in de.lmu.ifi.dbs.elki.index

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

Modifier and Type	Method and Description
void	DynamicIndex.deleteAll(DBIDs ids) Deletes the specified objects from this index.
void	DynamicIndex.insertAll(DBIDs ids) Inserts the specified objects into this index.

Uses of DBIDs in de.lmu.ifi.dbs.elki.index.lsh

Fields in de.lmu.ifi.dbs.elki.index.lsh with type parameters of type DBIDs

Modifier and Type	Field and Description
(package private) ArrayList<gnu.trove.map.TIntObjectMap<DBIDs>>	InMemoryLSHIndex.Instance.hashtables The actual table

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

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

Modifier and Type	Method and Description
DataStore<? extends DBIDs>	RandomProjectedNeighborssAndDensities.getNeighs() Compute list of neighbors for each point from sets resulting from projection

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics with parameters of type DBIDs

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

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

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.knn declared as DBIDs

Modifier and Type	Field and Description
private DBIDs	KNNChangeEvent.objects The ids of the kNNs that were inserted or deleted due to the insertion or removals of objects.
private DBIDs	KNNChangeEvent.updates The ids of the kNNs that were updated due to the insertion or removals of objects.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn that return DBIDs

Modifier and Type	Method and Description
DBIDs	KNNChangeEvent.getObjects() Returns the ids of the removed or inserted kNNs (according to the type of this event).
DBIDs	KNNChangeEvent.getUpdates() Returns the ids of kNNs which have been changed due to the removals or insertions.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type DBIDs

Modifier and Type	Method and Description
protected ArrayDBIDs	MaterializeKNNAndRKNNPreprocessor.affectedkNN(List<? extends KNNList> extract, DBIDs remove) Extracts and removes the DBIDs in the given collections.
protected ArrayDBIDs	MaterializeKNNAndRKNNPreprocessor.affectedRkNN(List<? extends Collection<DoubleDBIDPair>> extract, DBIDs remove) Extracts and removes the DBIDs in the given collections.
void	MaterializeKNNPreprocessor.deleteAll(DBIDs ids)
protected void	MaterializeKNNPreprocessor.fireKNNsInserted(DBIDs insertions, DBIDs updates) Informs all registered KNNListener that new kNNs have been inserted and as a result some kNNs have been changed.
protected void	MaterializeKNNPreprocessor.fireKNNsRemoved(DBIDs removals, DBIDs updates) Informs all registered KNNListener that existing kNNs have been removed and as a result some kNNs have been changed.
void	MaterializeKNNPreprocessor.insertAll(DBIDs ids)
protected void	MaterializeKNNPreprocessor.objectsInserted(DBIDs ids) Called after new objects have been inserted, updates the materialized neighborhood.
protected void	MaterializeKNNAndRKNNPreprocessor.objectsInserted(DBIDs ids)
protected void	MaterializeKNNPreprocessor.objectsRemoved(DBIDs ids) Called after objects have been removed, updates the materialized neighborhood.
protected void	MaterializeKNNAndRKNNPreprocessor.objectsRemoved(DBIDs ids)
private ArrayDBIDs	MaterializeKNNPreprocessor.updateKNNsAfterDeletion(DBIDs ids) Updates the kNNs of the RkNNs of the specified ids.
private ArrayDBIDs	MaterializeKNNPreprocessor.updateKNNsAfterInsertion(DBIDs ids) Updates the kNNs of the RkNNs of the specified ids.
private ArrayDBIDs	MaterializeKNNAndRKNNPreprocessor.updateKNNsAndRkNNs(DBIDs ids) Updates the kNNs and RkNNs after insertion of the specified ids.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type DBIDs

Constructor and Description
KNNChangeEvent(Object source, KNNChangeEvent.Type type, DBIDs objects, DBIDs updates) Used to create an event when kNNs of some objects have been changed.

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

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.preference with parameters of type DBIDs

Modifier and Type	Method and Description
private long[]	HiSCPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation, DBIDRef id, DBIDs neighborIDs, StringBuilder msg) Determines the preference vector according to the specified neighbor ids.
private int	DiSHPreferenceVectorIndex.maxIntersection(Map<Integer,ModifiableDBIDs> candidates, DBIDs set, ModifiableDBIDs result) Returns the index of the set having the maximum intersection set with the specified set contained in the specified map.

Uses of DBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with parameters of type DBIDs

Modifier and Type	Method and Description
void	CoverTree.bulkLoad(DBIDs ids) Bulk-load the index.
void	SimplifiedCoverTree.bulkLoad(DBIDs ids) Bulk-load the index.

Uses of DBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees with parameters of type DBIDs

Modifier and Type	Method and Description
protected Map<DBID,KNNList>	AbstractMkTree.batchNN(N node, DBIDs ids, int kmax) Deprecated. Change to use by-object NN lookups instead.

Uses of DBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp with parameters of type DBIDs

Modifier and Type	Method and Description
private double[]	MkAppTree.getMeanKNNList(DBIDs ids, Map<DBID,KNNList> knnLists)

Uses of DBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with parameters of type DBIDs

Modifier and Type	Method and Description
void	MkMaxTreeIndex.deleteAll(DBIDs ids) Throws an UnsupportedOperationException since deletion of objects is not yet supported by an M-Tree.
void	MkMaxTreeIndex.insertAll(DBIDs ids)

Uses of DBIDs in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with parameters of type DBIDs

Modifier and Type	Method and Description
void	MTreeIndex.deleteAll(DBIDs ids) Throws an UnsupportedOperationException since deletion of objects is not yet supported by an M-Tree.
void	MTreeIndex.insertAll(DBIDs ids)

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

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

Modifier and Type	Method and Description
void	DeLiCluTreeIndex.deleteAll(DBIDs ids)
void	DeLiCluTreeIndex.insertAll(DBIDs ids) Inserts the specified objects into this index.

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

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

Modifier and Type	Method and Description
void	FlatRStarTreeIndex.deleteAll(DBIDs ids)
void	FlatRStarTreeIndex.insertAll(DBIDs ids) Inserts the specified objects into this index.

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

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

Modifier and Type	Method and Description
protected List<RStarTreeKNNQuery.DoubleDistanceEntry>	RStarTreeKNNQuery.getSortedEntries(AbstractRStarTreeNode<?,?> node, DBIDs ids) Sorts the entries of the specified node according to their minimum distance to the specified objects.

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

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

Modifier and Type	Method and Description
List<ModifiableDoubleDBIDList>	RdKNNTree.bulkReverseKNNQueryForID(DBIDs ids, int k, SpatialPrimitiveDistanceFunction<? super O> distanceFunction, KNNQuery<O> knnQuery)
void	RdKNNTree.deleteAll(DBIDs ids)
private void	RdKNNTree.doBulkReverseKNN(RdKNNNode node, DBIDs ids, Map<DBID,ModifiableDoubleDBIDList> result) Performs a bulk reverse knn query in the specified subtree.
void	RdKNNTree.insertAll(DBIDs ids) Inserts the specified objects into this index.

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

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

Modifier and Type	Method and Description
void	RStarTreeIndex.deleteAll(DBIDs ids)
void	RStarTreeIndex.insertAll(DBIDs ids) Inserts the specified objects into this index.

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

Methods in de.lmu.ifi.dbs.elki.math.dimensionsimilarity that return types with arguments of type DBIDs

Modifier and Type	Method and Description
private ArrayList<ArrayList<DBIDs>>	MCEDimensionSimilarity.buildPartitions(Relation<? extends NumberVector> relation, DBIDs ids, int depth, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list.

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

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

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

Modifier and Type	Method and Description
private void	MCEDimensionSimilarity.divide(DBIDArrayIter it, double[] data, ArrayList<DBIDs> idx, int start, int end, int depth, Mean mean) Recursive call to further subdivide the array.
private void	MCEDimensionSimilarity.intersectionMatrix(int[][] res, ArrayList<? extends DBIDs> partsx, ArrayList<? extends DBIDs> partsy, int gridsize) Intersect the two 1d grid decompositions, to obtain a 2d matrix.
private void	MCEDimensionSimilarity.intersectionMatrix(int[][] res, ArrayList<? extends DBIDs> partsx, ArrayList<? extends DBIDs> partsy, int gridsize) Intersect the two 1d grid decompositions, to obtain a 2d matrix.

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

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

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

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

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

Modifier and Type	Method and Description
Matrix	StandardCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database) Compute Covariance Matrix for a collection of database IDs.
PCAFilteredResult	PCAFilteredAutotuningRunner.processIds(DBIDs ids, Relation<? extends NumberVector> database)
abstract Matrix	AbstractCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> database)
Matrix	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.
Matrix	WeightedCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation) Weighted Covariance Matrix for a set of IDs.
PCAFilteredResult	PCAFilteredRunner.processIds(DBIDs ids, Relation<? extends NumberVector> database) Run PCA on a collection of database IDs.
Matrix	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)

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

Constructors in de.lmu.ifi.dbs.elki.math.spacefillingcurves with parameters of type DBIDs

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

Uses of DBIDs in de.lmu.ifi.dbs.elki.parallel

Fields in de.lmu.ifi.dbs.elki.parallel declared as DBIDs

Modifier and Type	Field and Description
private DBIDs	SingleThreadedExecutor.SingleThreadedRunner.ids Array IDs to process

Methods in de.lmu.ifi.dbs.elki.parallel with parameters of type DBIDs

Modifier and Type	Method and Description
static void	ParallelExecutor.run(DBIDs ids, Processor... procs) Run a task on all available CPUs.
static void	SingleThreadedExecutor.run(DBIDs ids, Processor... procs) Run a task on a single thread.

Constructors in de.lmu.ifi.dbs.elki.parallel with parameters of type DBIDs

Constructor and Description
SingleThreadedRunner(DBIDs ids, Processor[] procs) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.result declared as DBIDs

Modifier and Type	Field and Description
protected DBIDs	OrderingFromDataStore.ids Database IDs
(package private) DBIDs	SamplingResult.sample The actual selection
private DBIDs	DBIDSelection.selectedIds Selected IDs

Methods in de.lmu.ifi.dbs.elki.result that return DBIDs

Modifier and Type	Method and Description
DBIDs	OrderingResult.getDBIDs() Get the full set of DBIDs this ordering is defined for.
DBIDs	OrderingFromDataStore.getDBIDs()
DBIDs	SamplingResult.getSample()
DBIDs	DBIDSelection.getSelectedIds() Getter for the selected IDs

Methods in de.lmu.ifi.dbs.elki.result with parameters of type DBIDs

Modifier and Type	Method and Description
ArrayModifiableDBIDs	OrderingResult.order(DBIDs ids) Sort the given ids according to this ordering and return an iterator.
ArrayModifiableDBIDs	OrderingFromDataStore.order(DBIDs ids)
void	SamplingResult.setSample(DBIDs sample) Note: trigger a resultchanged event!

Constructors in de.lmu.ifi.dbs.elki.result with parameters of type DBIDs

Constructor and Description
DBIDSelection(DBIDs selectedIds) Constructor with new object IDs.
OrderingFromDataStore(String name, String shortname, DBIDs ids, DataStore<? extends T> map) Minimal Constructor
OrderingFromDataStore(String name, String shortname, DBIDs ids, DataStore<? extends T> map, boolean descending) Constructor without comparator
OrderingFromDataStore(String name, String shortname, DBIDs ids, DataStore<? extends T> map, Comparator<T> comparator, boolean descending) Constructor with comparator
RangeSelection(DBIDs selectedIds) Constructor.
RangeSelection(DBIDs selection, ModifiableHyperBoundingBox ranges) Constructor.

Uses of DBIDs in de.lmu.ifi.dbs.elki.result.outlier

Methods in de.lmu.ifi.dbs.elki.result.outlier that return DBIDs

Modifier and Type	Method and Description
DBIDs	OrderingFromRelation.getDBIDs()

Methods in de.lmu.ifi.dbs.elki.result.outlier with parameters of type DBIDs

Modifier and Type	Method and Description
ArrayModifiableDBIDs	OrderingFromRelation.order(DBIDs ids)

Uses of DBIDs in de.lmu.ifi.dbs.elki.visualization.style

Fields in de.lmu.ifi.dbs.elki.visualization.style with type parameters of type DBIDs

Modifier and Type	Field and Description
(package private) ArrayList<DBIDs>	ClusterStylingPolicy.ids Object IDs

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

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

Modifier and Type	Method and Description
private void	MoveObjectsToolVisualization.Instance.updateDB(DBIDs dbids, Vector movingVector) Updates the objects with the given DBIDs It will be moved depending on the given Vector

Uses of DBIDs in tutorial.clustering

Methods in tutorial.clustering with parameters of type DBIDs

Modifier and Type	Method and Description
protected ArrayModifiableDBIDs	SameSizeKMeansAlgorithm.initialAssignment(List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, DBIDs ids)