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.kmeans |
K-means clustering and variations.
|
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.outlier |
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.distanceresultlist |
Classes for building and storing the results of distance-based queries
The classes in this package essentially form three groups:
KNNHeap for building kNN results. |
de.lmu.ifi.dbs.elki.distance.similarityfunction |
Similarity 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.roc |
Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
|
de.lmu.ifi.dbs.elki.index |
Index structure implementations
|
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.mtreevariants |
M-Tree and variants.
|
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 | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab | |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree | |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
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.rstar | |
de.lmu.ifi.dbs.elki.index.vafile |
Vector Approximation File
|
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.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.result.optics |
Result classes for OPTICS.
|
de.lmu.ifi.dbs.elki.result.outlier |
Outlier result classes
|
de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
|
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.
|
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.
|
Modifier and Type | Method and Description |
---|---|
private Clustering<DendrogramModel<D>> |
SLINK.extractClusters(DBIDs ids,
DBIDDataStore pi,
DataStore<D> lambda,
int minclusters)
Extract all clusters from the pi-lambda-representation.
|
private Clustering<DendrogramModel<D>> |
SLINK.extractClustersDouble(DBIDs ids,
DBIDDataStore pi,
DoubleDistanceDataStore lambda,
int minclusters)
Extract all clusters from the pi-lambda-representation.
|
private Cluster<DendrogramModel<D>> |
SLINK.makeCluster(DBIDRef lead,
D depth,
DBIDs members,
ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier)
Make the cluster for the given object
|
private void |
SLINK.step2(DBIDRef id,
DBIDs processedIDs,
DistanceQuery<O,D> distFunc,
WritableDataStore<D> m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINK.step2double(DBIDRef id,
DBIDs processedIDs,
Relation<? extends O> relation,
PrimitiveDoubleDistanceFunction<? super O> distFunc,
WritableDoubleDistanceDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINK.step3(DBIDRef id,
WritableDBIDDataStore pi,
WritableDataStore<D> lambda,
DBIDs processedIDs,
WritableDataStore<D> m)
Third step: Determine the values for P and L
|
private void |
SLINK.step3double(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDistanceDataStore lambda,
DBIDs processedIDs,
WritableDoubleDistanceDataStore m)
Third step: Determine the values for P and L
|
private void |
SLINK.step4(DBIDRef id,
WritableDBIDDataStore pi,
WritableDataStore<D> lambda,
DBIDs processedIDs)
Fourth step: Actualize the clusters if necessary
|
private void |
SLINK.step4double(DBIDRef id,
WritableDBIDDataStore pi,
WritableDoubleDistanceDataStore lambda,
DBIDs processedIDs)
Fourth step: Actualize the clusters if necessary
|
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(Heap<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.
|
Modifier and Type | Method and Description |
---|---|
private Clustering<Model> |
COPAC.runPartitionAlgorithm(Relation<V> relation,
Map<Integer,DBIDs> partitionMap,
DistanceQuery<V,D> query)
Runs the partition algorithm and creates the result.
|
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.
|
Modifier and Type | Field and Description |
---|---|
(package private) DBIDs |
EpsilonNeighborPredicate.Instance.ids
DBIDs to process
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
EpsilonNeighborPredicate.Instance.getIDs() |
DBIDs |
NeighborPredicate.Instance.getIDs()
Get the IDs the predicate is defined for.
|
Modifier and Type | Method and Description |
---|---|
boolean |
MinPtsCorePredicate.Instance.isCorePoint(DBIDRef point,
DBIDs neighbors) |
Constructor and Description |
---|
EpsilonNeighborPredicate.Instance(D epsilon,
RangeQuery<?,D> rq,
DBIDs ids)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
KMeansPlusPlusInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distQ2) |
DBIDs |
PAMInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distQ2) |
DBIDs |
KMedoidsInitialization.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distanceFunction)
Choose initial means
|
DBIDs |
RandomlyChosenInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distanceFunction) |
DBIDs |
FirstKInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distanceFunction) |
Modifier and Type | Method and Description |
---|---|
protected boolean |
KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means,
DBIDs ids,
WritableDoubleDataStore second,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V,D> distQ)
Returns a list of clusters.
|
Modifier and Type | Method and Description |
---|---|
private double |
PROCLUS.avgDistance(V centroid,
DBIDs objectIDs,
Relation<V> database,
int dimension)
Computes the average distance of the objects to the centroid along the
specified dimension.
|
private ModifiableDBIDs |
PROCLUS.computeM_current(DBIDs m,
DBIDs m_best,
DBIDs m_bad,
Random random)
Computes the set of medoids in current iteration.
|
private Map<DBID,gnu.trove.set.TIntSet> |
PROCLUS.findDimensions(DBIDs medoids,
Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
RangeQuery<V,DoubleDistance> rangeQuery)
Determines the set of correlated dimensions for each medoid in the
specified medoid set.
|
private Map<DBID,DistanceDBIDResult<DoubleDistance>> |
PROCLUS.getLocalities(DBIDs medoids,
Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
RangeQuery<V,DoubleDistance> 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 ModifiableDBIDs |
PROCLUS.greedy(DistanceQuery<V,DoubleDistance> distFunc,
DBIDs sampleSet,
int m,
Random random)
Returns a piercing set of k medoids from the specified sample set.
|
private ModifiableDBIDs |
PROCLUS.initialSet(DBIDs sampleSet,
int k,
Random random)
Returns a set of k elements from the specified sample set.
|
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.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
CLIQUEUnit.getIds()
Returns the ids of the feature vectors this unit contains.
|
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.
|
Modifier and Type | Method and Description |
---|---|
private void |
ByLabelClustering.assign(HashMap<String,DBIDs> labelMap,
String label,
DBIDRef id)
Assigns the specified id to the labelMap according to its label
|
private void |
ByLabelHierarchicalClustering.assign(HashMap<String,DBIDs> labelMap,
String label,
DBIDRef id)
Assigns the specified id to the labelMap according to its label
|
Modifier and Type | Field and Description |
---|---|
(package private) ArrayList<ArrayList<DBIDs>> |
AggarwalYuEvolutionary.EvolutionarySearch.ranges
Database ranges.
|
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(int[] gene,
ArrayList<ArrayList<DBIDs>> ranges)
Get the DBIDs in the current subspace.
|
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. |
Modifier and Type | Method and Description |
---|---|
private double[] |
ABOD.calcFastNormalization(DBIDRef x,
WritableDoubleDataStore dists,
DBIDs ids) |
protected Pair<WritableDoubleDataStore,DoubleMinMax> |
LOF.computeLOFs(DBIDs ids,
DoubleDataStore lrds,
KNNQuery<O,D> knnRefer)
Computes the Local outlier factor (LOF) of the specified objects.
|
protected WritableDoubleDataStore |
LOF.computeLRDs(DBIDs ids,
KNNQuery<O,D> knnReach)
Computes the local reachability density (LRD) of the specified objects.
|
protected LOF.LOFResult<O,D> |
LOF.doRunInTime(DBIDs ids,
KNNQuery<O,D> kNNRefer,
KNNQuery<O,D> kNNReach,
StepProgress stepprog)
Performs the Generalized LOF_SCORE algorithm on the given database and
returns a
LOF.LOFResult encapsulating information that may be
needed by an OnlineLOF algorithm. |
private void |
ABOD.generateExplanation(StringBuilder buf,
Relation<V> data,
DBIDRef key,
DBIDs expList) |
private double |
ABOD.getAbofFilter(KernelMatrix kernelMatrix,
DBIDRef aKey,
WritableDoubleDataStore dists,
double fulCounter,
double counter,
DBIDs neighbors) |
private void |
OnlineLOF.LOFKNNListener.kNNsInserted(DBIDs insertions,
DBIDs updates1,
DBIDs updates2,
LOF.LOFResult<O,D> lofResult)
Invoked after kNNs have been inserted and updated, updates the result.
|
private void |
OnlineLOF.LOFKNNListener.kNNsRemoved(DBIDs deletions,
DBIDs updates1,
DBIDs updates2,
LOF.LOFResult<O,D> lofResult)
Invoked after kNNs have been removed and updated, updates the result.
|
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 ArrayModifiableDBIDs |
OnlineLOF.LOFKNNListener.mergeIDs(List<? extends DistanceDBIDResult<D>> queryResults,
DBIDs... ids)
Merges the ids of the query result with the specified ids.
|
private void |
OnlineLOF.LOFKNNListener.recomputeLOFs(DBIDs ids,
LOF.LOFResult<O,D> lofResult)
Recomputes the lofs of the specified ids.
|
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(int[] gene,
ArrayList<ArrayList<DBIDs>> ranges)
Get the DBIDs in the current subspace.
|
Constructor and Description |
---|
AggarwalYuEvolutionary.EvolutionarySearch(Relation<V> relation,
ArrayList<ArrayList<DBIDs>> ranges,
int m,
Random random)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DataStore<DBIDs> |
AbstractPrecomputedNeighborhood.store
The data
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
AbstractPrecomputedNeighborhood.getNeighborDBIDs(DBIDRef reference) |
DBIDs |
NeighborSetPredicate.getNeighborDBIDs(DBIDRef reference)
Get the neighbors of a reference object for DBSCAN.
|
Modifier and Type | Method and Description |
---|---|
private DataStore<DBIDs> |
ExtendedNeighborhood.Factory.extendNeighborhood(Relation<? extends O> database)
Method to load the external neighbors.
|
private DataStore<DBIDs> |
ExternalNeighborhood.Factory.loadNeighbors(Relation<?> database)
Method to load the external neighbors.
|
Constructor and Description |
---|
AbstractPrecomputedNeighborhood(DataStore<DBIDs> store)
Constructor.
|
ExtendedNeighborhood(DataStore<DBIDs> store)
Constructor.
|
ExternalNeighborhood(DataStore<DBIDs> store)
Constructor.
|
PrecomputedKNearestNeighborNeighborhood(DataStore<DBIDs> store)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
(package private) DBIDs |
SOD.SODProxyScoreResult.dbids
The IDs we are defined for.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
SOD.SODProxyScoreResult.getDBIDs() |
private DBIDs |
SOD.getNearestNeighbors(Relation<V> relation,
SimilarityQuery<V,D> simQ,
DBIDRef queryObject)
Provides the k nearest neighbors in terms of the shared nearest neighbor
distance.
|
Constructor and Description |
---|
SOD.SODModel(Relation<V> relation,
DBIDs neighborhood,
double alpha,
V queryObject)
Initialize SOD Model
|
SOD.SODProxyScoreResult(Relation<SOD.SODModel<?>> models,
DBIDs dbids)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
(package private) void |
ComputeKNNOutlierScores.writeResult(PrintStream out,
DBIDs ids,
OutlierResult result,
ScalingFunction scaling,
String label)
Write a single output line.
|
Modifier and Type | Field and Description |
---|---|
private DBIDs |
Cluster.ids
Cluster data.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
Cluster.getIDs()
Access group object
|
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
|
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)
Constructor without hierarchy information.
|
Cluster(String name,
DBIDs ids,
boolean noise,
M model,
Hierarchy<Cluster<M>> hierarchy)
Full constructor
|
Cluster(String name,
DBIDs ids,
boolean noise,
M model,
List<Cluster<M>> children,
List<Cluster<M>> parents)
Constructor with hierarchy information.
|
Cluster(String name,
DBIDs ids,
M model)
Constructor without hierarchy information.
|
Cluster(String name,
DBIDs ids,
M model,
Hierarchy<Cluster<M>> hierarchy)
Constructor with hierarchy but noise flag defaulting to false.
|
Cluster(String name,
DBIDs ids,
M model,
List<Cluster<M>> children,
List<Cluster<M>> parents)
Constructor with hierarchy information, but no noise flag.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
Bicluster.getDatabaseObjectGroup()
Creates a DBIDs for the row IDs included in this Bicluster.
|
DBIDs |
BiclusterWithInverted.getInvertedRows()
Provides a copy of the inverted column IDs.
|
Modifier and Type | Method and Description |
---|---|
void |
BiclusterWithInverted.setInvertedRows(DBIDs invertedRows)
Sets the ids of the inverted rows.
|
Modifier and Type | Field and Description |
---|---|
static SimpleTypeInformation<DBIDs> |
TypeUtil.DBIDS
Database ID lists.
|
Modifier and Type | Field and Description |
---|---|
protected DBIDs |
ProxyDatabase.ids
Our DBIDs
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
UpdatableDatabase.insert(ObjectBundle objpackages)
Inserts the given object(s) and their associations into the database.
|
DBIDs |
HashmapDatabase.insert(ObjectBundle objpackages) |
Modifier and Type | Method and Description |
---|---|
ObjectBundle |
UpdatableDatabase.delete(DBIDs ids)
Removes and returns the specified objects with the given ids from the
database.
|
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. |
private void |
DatabaseEventManager.fireObjectsChanged(DBIDs objects,
DataStoreEvent.Type type)
Handles a DataStoreEvent with the specified type.
|
void |
DatabaseEventManager.fireObjectsInserted(DBIDs insertions)
Convenience method, calls
fireObjectsChanged(insertions,
DataStoreEvent.Type.INSERT) . |
protected void |
DatabaseEventManager.fireObjectsRemoved(DBIDs deletions)
Convenience method, calls
fireObjectsChanged(deletions,
DataStoreEvent.Type.DELETE) . |
void |
DatabaseEventManager.fireObjectsUpdated(DBIDs updates)
Convenience method, calls
fireObjectsChanged(updates,
DataStoreEvent.Type.UPDATE) . |
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.
|
Modifier and Type | Field and Description |
---|---|
private Map<DataStoreEvent.Type,DBIDs> |
DataStoreEvent.objects
The objects that were changed in the
DataStore mapped by the type
of change. |
Modifier and Type | Method and Description |
---|---|
Map<DataStoreEvent.Type,DBIDs> |
DataStoreEvent.getObjects()
Returns the objects that have been changed and the type of change.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Constructor and Description |
---|
DataStoreEvent(Object source,
Map<DataStoreEvent.Type,DBIDs> objects)
Used to create an event when objects have been updated in, inserted into,
and / or removed from the specified
DataStore . |
Modifier and Type | Method and Description |
---|---|
WritableDBIDDataStore |
MemoryDataStoreFactory.makeDBIDStorage(DBIDs ids,
int hints) |
WritableDoubleDistanceDataStore |
MemoryDataStoreFactory.makeDoubleDistanceStorage(DBIDs ids,
int hints)
Make a data storage for double distances.
|
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) |
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 |
DBIDRange
Static DBID range.
|
interface |
DBIDVar
(Persistent) variable storing a DBID reference.
|
interface |
HashSetDBIDs
Hash-organized DBIDs
|
interface |
HashSetModifiableDBIDs
Set-oriented implementation of a modifiable DBID collection.
|
interface |
ModifiableDBIDs
Interface for a generic modifiable DBID collection.
|
interface |
SetDBIDs
Interface for DBIDs that support fast "set" operations, in particular
"contains" lookups.
|
interface |
StaticDBIDs
Unmodifiable DBIDs.
|
Modifier and Type | Class and Description |
---|---|
class |
EmptyDBIDs
Empty DBID collection.
|
Modifier and Type | Method and Description |
---|---|
boolean |
ModifiableDBIDs.addDBIDs(DBIDs ids)
Add DBIDs to collection.
|
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.
|
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.
|
static ArrayModifiableDBIDs |
DBIDUtil.newArray(DBIDs existing)
Make a new (modifiable) array of DBIDs.
|
ArrayModifiableDBIDs |
DBIDFactory.newArray(DBIDs existing)
Make a new (modifiable) array of DBIDs.
|
static HashSetModifiableDBIDs |
DBIDUtil.newHashSet(DBIDs existing)
Make a new (modifiable) hash set of DBIDs.
|
HashSetModifiableDBIDs |
DBIDFactory.newHashSet(DBIDs existing)
Make a new (modifiable) hash set of 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.
|
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.
|
Modifier and Type | Class and Description |
---|---|
class |
MaskedDBIDs
View on an ArrayDBIDs masked using a BitMask for efficient mask changing.
|
class |
MergedDBIDs
Merge the IDs of multiple layers into one.
|
class |
UnmodifiableArrayDBIDs
Unmodifiable wrapper for DBIDs.
|
class |
UnmodifiableDBIDs
Unmodifiable wrapper for DBIDs.
|
Modifier and Type | Field and Description |
---|---|
(package private) DBIDs[] |
MergedDBIDs.childs
Childs to merge
|
private DBIDs |
UnmodifiableDBIDs.inner
The DBIDs we wrap.
|
Constructor and Description |
---|
MergedDBIDs(DBIDs... childs)
Constructor.
|
UnmodifiableDBIDs(DBIDs inner)
Constructor.
|
Modifier and Type | Interface and Description |
---|---|
interface |
IntegerArrayStaticDBIDs
Combination of
ArrayStaticDBIDs and IntegerDBIDs . |
interface |
IntegerDBIDs
Integer DBID collection.
|
Modifier and Type | Class and Description |
---|---|
class |
IntArrayStaticDBIDs
Static (no modifications allowed) set of Database Object IDs.
|
(package private) class |
IntegerDBID
Database ID object.
|
(package private) class |
IntegerDBIDRange
Representing a DBID range allocation.
|
(package private) class |
IntegerDBIDVar
Variable for storing a single DBID reference.
|
class |
TroveArrayDBIDs
Abstract base class for GNU Trove array based lists.
|
(package private) class |
TroveArrayModifiableDBIDs
Class using a GNU Trove int array list as storage.
|
(package private) class |
TroveArrayStaticDBIDs
Class accessing a trove int array.
|
(package private) class |
TroveHashSetModifiableDBIDs
Implementation using GNU Trove Int Hash Sets.
|
class |
UnmodifiableIntegerArrayDBIDs
Unmodifiable wrapper for DBIDs.
|
class |
UnmodifiableIntegerDBIDs
Unmodifiable wrapper for DBIDs.
|
Modifier and Type | Method and Description |
---|---|
boolean |
TroveArrayModifiableDBIDs.addDBIDs(DBIDs ids) |
boolean |
TroveHashSetModifiableDBIDs.addDBIDs(DBIDs ids) |
ArrayModifiableDBIDs |
SimpleDBIDFactory.newArray(DBIDs existing) |
ArrayModifiableDBIDs |
TrivialDBIDFactory.newArray(DBIDs existing) |
HashSetModifiableDBIDs |
SimpleDBIDFactory.newHashSet(DBIDs existing) |
HashSetModifiableDBIDs |
TrivialDBIDFactory.newHashSet(DBIDs existing) |
boolean |
TroveArrayModifiableDBIDs.removeDBIDs(DBIDs ids) |
boolean |
TroveHashSetModifiableDBIDs.removeDBIDs(DBIDs ids) |
boolean |
TroveHashSetModifiableDBIDs.retainAll(DBIDs set) |
Constructor and Description |
---|
TroveArrayModifiableDBIDs(DBIDs existing)
Constructor.
|
TroveHashSetModifiableDBIDs(DBIDs existing)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DBIDs |
DBIDView.ids
The ids object
|
private DBIDs |
ProxyView.idview
The DBIDs we contain
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
ConvertToStringView.getDBIDs() |
DBIDs |
ProxyView.getDBIDs() |
DBIDs |
DBIDView.getDBIDs() |
DBIDs |
ProjectedView.getDBIDs() |
DBIDs |
Relation.getDBIDs()
Get the IDs the query is defined for.
|
Modifier and Type | Method and Description |
---|---|
static <O> ProxyView<O> |
ProxyView.wrap(Database database,
DBIDs idview,
Relation<O> inner)
Constructor-like static method.
|
Constructor and Description |
---|
DBIDView(Database database,
DBIDs ids)
Constructor.
|
MaterializedRelation(Database database,
SimpleTypeInformation<O> type,
DBIDs ids)
Constructor.
|
MaterializedRelation(Database database,
SimpleTypeInformation<O> type,
DBIDs ids,
String name)
Constructor.
|
MaterializedRelation(Database database,
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(Database database,
DBIDs idview,
Relation<O> inner)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
protected static double |
SharedNearestNeighborJaccardDistanceFunction.Instance.jaccardCoefficient(DBIDs neighbors1,
DBIDs neighbors2)
Compute the Jaccard coefficient
|
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceDBIDResult<D extends Distance<D>>
Collection of objects and their distances.
|
interface |
KNNResult<D extends Distance<D>>
Interface for kNN results.
|
interface |
ModifiableDistanceDBIDResult<D extends Distance<D>>
Modifiable API for Distance-DBID results
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleDistanceDBIDList
Default class to keep a list of distance-object pairs.
|
class |
DoubleDistanceKNNList
Finalized KNN List.
|
class |
GenericDistanceDBIDList<D extends Distance<D>>
Default class to keep a list of distance-object pairs.
|
class |
GenericKNNList<D extends Distance<D>>
Finalized KNN List.
|
protected static class |
KNNUtil.KNNSubList<D extends Distance<D>>
Sublist of an existing result to contain only the first k elements.
|
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
|
Modifier and Type | Field and Description |
---|---|
protected DBIDs |
Segment.objIDs
IDs in segment, for object segments.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
Segment.getDBIDs()
Get the DBIDs of objects contained in this segment.
|
Modifier and Type | Method and Description |
---|---|
private void |
Segments.makeOrUpdateSegment(int[] path,
DBIDs ids,
int pairsize) |
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.
|
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
ROC.computeROCAUCDistanceResult(int size,
DBIDs ids,
DistanceDBIDResult<D> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
|
static double |
ROC.computeROCAUCSimple(int size,
DBIDs ids,
DBIDs nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractIndex.deleteAll(DBIDs id) |
void |
Index.deleteAll(DBIDs ids)
Deletes the specified objects from this index.
|
protected abstract void |
AbstractRefiningIndex.initialize(Relation<O> relation,
DBIDs ids)
Initialize the index.
|
void |
AbstractIndex.insertAll(DBIDs ids) |
void |
AbstractRefiningIndex.insertAll(DBIDs ids) |
void |
Index.insertAll(DBIDs ids)
Inserts the specified objects into this index.
|
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.
|
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.
|
Modifier and Type | Method and Description |
---|---|
protected ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.affectedkNN(List<? extends KNNResult<D>> extraxt,
DBIDs remove)
Extracts and removes the DBIDs in the given collections.
|
protected ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.affectedRkNN(List<? extends Collection<DistanceDBIDPair<D>>> extraxt,
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) |
void |
AbstractMaterializeKNNPreprocessor.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.
|
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.
|
Modifier and Type | Method and Description |
---|---|
private BitSet |
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.
|
Modifier and Type | Method and Description |
---|---|
protected List<DistanceEntry<D,E>> |
AbstractMTree.getSortedEntries(N node,
DBIDs ids)
Sorts the entries of the specified node according to their minimum distance
to the specified objects.
|
Modifier and Type | Method and Description |
---|---|
protected Map<DBID,KNNResult<D>> |
AbstractMkTree.batchNN(N node,
DBIDs ids,
int kmax)
Deprecated.
Change to use by-object NN lookups instead.
|
Modifier and Type | Method and Description |
---|---|
void |
MkAppTreeIndex.deleteAll(DBIDs ids)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
private List<D> |
MkAppTree.getMeanKNNList(DBIDs ids,
Map<DBID,KNNResult<D>> knnLists) |
void |
MkAppTreeIndex.insertAll(DBIDs ids) |
Modifier and Type | Method and Description |
---|---|
void |
MkCoPTreeIndex.deleteAll(DBIDs ids)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
void |
MkCoPTreeIndex.insertAll(DBIDs ids) |
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) |
Modifier and Type | Method and Description |
---|---|
void |
MkTabTreeIndex.deleteAll(DBIDs ids)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
void |
MkTabTreeIndex.insertAll(DBIDs ids) |
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) |
Modifier and Type | Method and Description |
---|---|
void |
DeLiCluTreeIndex.deleteAll(DBIDs ids) |
void |
DeLiCluTreeIndex.insertAll(DBIDs ids)
Inserts the specified objects into this index.
|
Modifier and Type | Method and Description |
---|---|
protected List<DoubleDistanceRStarTreeKNNQuery.DoubleDistanceEntry> |
DoubleDistanceRStarTreeKNNQuery.getSortedEntries(AbstractRStarTreeNode<?,?> node,
DBIDs ids)
Sorts the entries of the specified node according to their minimum distance
to the specified objects.
|
protected List<DistanceEntry<D,SpatialEntry>> |
GenericRStarTreeKNNQuery.getSortedEntries(AbstractRStarTreeNode<?,?> node,
DBIDs ids)
Sorts the entries of the specified node according to their minimum distance
to the specified objects.
|
Modifier and Type | Method and Description |
---|---|
void |
RStarTreeIndex.deleteAll(DBIDs ids) |
void |
RStarTreeIndex.insertAll(DBIDs ids)
Inserts the specified objects into this index.
|
Modifier and Type | Method and Description |
---|---|
protected void |
VAFile.initialize(Relation<V> relation,
DBIDs ids) |
void |
PartialVAFile.initialize(Relation<V> relation,
DBIDs ids) |
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.
|
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 |
SlopeDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector<?>> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
CovarianceDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector<?>> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
SlopeInversionDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector<?>> relation,
DBIDs subset,
DimensionSimilarityMatrix matrix) |
void |
SURFINGDimensionSimilarity.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 |
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
|
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.
|
Modifier and Type | Method and Description |
---|---|
static ProjectedCentroid |
ProjectedCentroid.make(BitSet dims,
Relation<? extends NumberVector<?>> relation,
DBIDs ids)
Static Constructor from a relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector<?>> relation,
DBIDs ids)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector<?>> relation,
DBIDs ids)
Static Constructor from a full relation.
|
Modifier and Type | Method and Description |
---|---|
PCAFilteredResult |
PCAFilteredAutotuningRunner.processIds(DBIDs ids,
Relation<? extends V> database) |
Matrix |
WeightedCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends V> relation)
Weighted Covariance Matrix for a set of IDs.
|
PCAResult |
PCARunner.processIds(DBIDs ids,
Relation<? extends V> database)
Run PCA on a collection of database IDs.
|
abstract Matrix |
AbstractCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends V> database) |
Matrix |
RANSACCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends V> relation) |
Matrix |
CovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends V> database)
Compute Covariance Matrix for a collection of database IDs.
|
Matrix |
StandardCovarianceMatrixBuilder.processIds(DBIDs ids,
Relation<? extends V> database)
Compute Covariance Matrix for a collection of database IDs.
|
PCAFilteredResult |
PCAFilteredRunner.processIds(DBIDs ids,
Relation<? extends V> database)
Run PCA on a collection of database IDs.
|
Constructor and Description |
---|
ZCurveTransformer(Relation<? extends NumberVector<?>> relation,
DBIDs ids)
Constructor.
|
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
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
OrderingFromDataStore.getDBIDs() |
DBIDs |
OrderingResult.getDBIDs()
Get the full set of DBIDs this ordering is defined for.
|
DBIDs |
SamplingResult.getSample() |
DBIDs |
DBIDSelection.getSelectedIds()
Getter for the selected IDs
|
Modifier and Type | Method and Description |
---|---|
ArrayModifiableDBIDs |
OrderingFromDataStore.iter(DBIDs ids) |
ArrayModifiableDBIDs |
OrderingResult.iter(DBIDs ids)
Sort the given ids according to this ordering and return an iterator.
|
void |
SamplingResult.setSample(DBIDs sample)
Note: trigger a resultchanged event!
|
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,
DoubleDoublePair[] ranges)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DBIDs |
ClusterOrderResult.ReachabilityDistanceAdapter.dbids
DBIDs
|
private DBIDs |
ClusterOrderResult.PredecessorAdapter.dbids
Database IDs
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
ClusterOrderResult.ClusterOrderAdapter.getDBIDs() |
DBIDs |
ClusterOrderResult.ReachabilityDistanceAdapter.getDBIDs() |
DBIDs |
ClusterOrderResult.PredecessorAdapter.getDBIDs() |
Modifier and Type | Method and Description |
---|---|
ArrayModifiableDBIDs |
ClusterOrderResult.ClusterOrderAdapter.iter(DBIDs ids)
Use the cluster order to sort the given collection ids.
|
Constructor and Description |
---|
ClusterOrderResult.PredecessorAdapter(DataStore<ClusterOrderEntry<D>> map,
DBIDs dbids)
Constructor.
|
ClusterOrderResult.ReachabilityDistanceAdapter(DataStore<ClusterOrderEntry<D>> map,
DBIDs dbids)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
OrderingFromRelation.getDBIDs() |
Modifier and Type | Method and Description |
---|---|
ArrayModifiableDBIDs |
OrderingFromRelation.iter(DBIDs ids) |
Modifier and Type | Method and Description |
---|---|
static <V extends NumberVector<?>> |
DatabaseUtil.exactMedian(Relation<V> relation,
DBIDs ids,
int dimension)
Returns the median of a data set in the given dimension.
|
static double[] |
DatabaseUtil.variances(Relation<? extends NumberVector<?>> database,
NumberVector<?> centroid,
DBIDs ids)
Determines the variances in each dimension of the specified objects stored
in the given database.
|
Modifier and Type | Field and Description |
---|---|
(package private) ArrayList<DBIDs> |
ClusterStylingPolicy.ids
Object IDs
|
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
|
Modifier and Type | Method and Description |
---|---|
protected ArrayModifiableDBIDs |
SameSizeKMeansAlgorithm.initialAssignment(List<ModifiableDBIDs> clusters,
WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas,
DBIDs ids) |