Package | Description |
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de.lmu.ifi.dbs.elki.data.model |
Cluster models classes for various algorithms.
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de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
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de.lmu.ifi.dbs.elki.database.ids |
Database object identification and ID group handling API.
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de.lmu.ifi.dbs.elki.database.ids.generic |
Database object identification and ID group handling - generic implementations.
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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.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
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de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries.
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de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
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de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel |
Kernel functions.
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de.lmu.ifi.dbs.elki.evaluation.similaritymatrix |
Render a distance matrix to visualize a clustering-distance-combination.
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de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
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de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query |
Classes for performing queries (knn, range, ...) on metrical trees.
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query |
Queries on the R-Tree family of indexes: kNN and range queries.
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de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
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de.lmu.ifi.dbs.elki.utilities.datastructures.heap |
Heap structures and variations such as bounded priority heaps.
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de.lmu.ifi.dbs.elki.utilities.scaling.outlier |
Scaling of Outlier scores, that require a statistical analysis of the occurring values
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Modifier and Type | Field and Description |
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private ArrayDBIDs |
Bicluster.rowIDs
The ids of the rows included in the bicluster.
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Constructor and Description |
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Bicluster(ArrayDBIDs rowIDs,
int[] colIDs,
Relation<V> database)
Defines a new bicluster for given parameters.
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BiclusterWithInverted(ArrayDBIDs rowIDs,
int[] colIDs,
Relation<V> database) |
Modifier and Type | Field and Description |
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private ArrayDBIDs |
StaticArrayDatabase.ids
IDs of this database
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Modifier and Type | Interface and Description |
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interface |
ArrayModifiableDBIDs
Array-oriented implementation of a modifiable DBID collection.
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interface |
ArrayStaticDBIDs
Unmodifiable, indexed DBIDs.
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interface |
DBID
Database ID object.
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interface |
DBIDRange
Static DBID range.
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Modifier and Type | Class and Description |
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(package private) class |
EmptyDBIDs
Empty DBID collection.
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Modifier and Type | Method and Description |
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static ArrayDBIDs |
DBIDUtil.ensureArray(DBIDs ids)
Ensure that the given DBIDs are array-indexable.
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Modifier and Type | Class and Description |
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class |
GenericArrayModifiableDBIDs
Array-oriented implementation of a modifiable DBID collection.
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Modifier and Type | Field and Description |
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protected ArrayDBIDs |
MaskedDBIDs.data
Data storage
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Constructor and Description |
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MaskedDBIDs(ArrayDBIDs data,
BitSet bits,
boolean inverse)
Constructor.
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Modifier and Type | Class and Description |
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class |
IntegerArrayStaticDBIDs
Static (no modifications allowed) set of Database Object IDs.
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(package private) class |
IntegerDBID
Database ID object.
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(package private) class |
IntegerDBIDRange
Representing a DBID range allocation
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Modifier and Type | Method and Description |
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List<List<DistanceResultPair<D>>> |
KNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method
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List<List<DistanceResultPair<D>>> |
PreprocessorKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<List<DistanceResultPair<D>>> |
LinearScanKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<List<DistanceResultPair<D>>> |
LinearScanPrimitiveDistanceKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
private void |
LinearScanKNNQuery.linearScanBatchKNN(ArrayDBIDs ids,
List<KNNHeap<D>> heaps)
Linear batch knn for arbitrary distance functions.
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Modifier and Type | Method and Description |
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List<List<DistanceResultPair<D>>> |
RangeQuery.getRangeForBulkDBIDs(ArrayDBIDs ids,
D range)
Bulk query method
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List<List<DistanceResultPair<D>>> |
AbstractDistanceRangeQuery.getRangeForBulkDBIDs(ArrayDBIDs ids,
D range) |
Modifier and Type | Method and Description |
---|---|
List<List<DistanceResultPair<D>>> |
LinearScanRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<List<DistanceResultPair<D>>> |
RKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method for reverse k nearest neighbors for ids.
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List<List<DistanceResultPair<D>>> |
PreprocessorRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Constructor and Description |
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KernelMatrix(PrimitiveSimilarityFunction<? super O,DoubleDistance> kernelFunction,
Relation<? extends O> database,
ArrayDBIDs ids)
Provides a new kernel matrix.
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Modifier and Type | Field and Description |
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(package private) ArrayDBIDs |
ComputeSimilarityMatrixImage.SimilarityMatrix.ids
The database IDs used
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Modifier and Type | Method and Description |
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ArrayDBIDs |
ComputeSimilarityMatrixImage.SimilarityMatrix.getIDs()
Get the IDs
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Constructor and Description |
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ComputeSimilarityMatrixImage.SimilarityMatrix(RenderedImage img,
Relation<?> relation,
ArrayDBIDs ids)
Constructor
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Modifier and Type | Method and Description |
---|---|
protected ArrayDBIDs |
MaterializeKNNPreprocessor.extractAndRemoveIDs(List<List<DistanceResultPair<D>>> extraxt,
ArrayDBIDs remove)
Extracts and removes the DBIDs in the given collections.
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private ArrayDBIDs |
MaterializeKNNPreprocessor.updateKNNsAfterDeletion(DBIDs ids)
Updates the kNNs of the RkNNs of the specified ids.
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private ArrayDBIDs |
MaterializeKNNPreprocessor.updateKNNsAfterInsertion(DBIDs ids)
Updates the kNNs of the RkNNs of the specified ids.
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private ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.updateKNNsAndRkNNs(DBIDs ids)
Updates the kNNs and RkNNs after insertion of the specified ids.
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Modifier and Type | Method and Description |
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protected ArrayDBIDs |
MaterializeKNNPreprocessor.extractAndRemoveIDs(List<List<DistanceResultPair<D>>> extraxt,
ArrayDBIDs remove)
Extracts and removes the DBIDs in the given collections.
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private void |
MaterializeKNNAndRKNNPreprocessor.materializeKNNAndRKNNs(ArrayDBIDs ids,
FiniteProgress progress)
Materializes the kNNs and RkNNs of the specified object IDs.
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Modifier and Type | Method and Description |
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List<List<DistanceResultPair<D>>> |
MetricalIndexKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<List<DistanceResultPair<D>>> |
MkTreeRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
List<List<DistanceResultPair<DoubleDistance>>> |
DoubleDistanceRStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<List<DistanceResultPair<D>>> |
GenericRStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
static <V extends NumberVector<?,?>> |
DatabaseUtil.quickMedian(Relation<V> relation,
ArrayDBIDs ids,
int dimension,
int numberOfSamples)
Returns the median of a data set in the given dimension by using a sampling
method.
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Modifier and Type | Class and Description |
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protected static class |
KNNList.DBIDView
A view on the DBIDs of the result
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Modifier and Type | Method and Description |
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ArrayDBIDs |
KNNList.asDBIDs()
View as ArrayDBIDs
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static ArrayDBIDs |
KNNList.asDBIDs(List<? extends DistanceResultPair<?>> list)
View as ArrayDBIDs
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Modifier and Type | Method and Description |
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private double[] |
SigmoidOutlierScalingFunction.MStepLevenbergMarquardt(double a,
double b,
ArrayDBIDs ids,
BitSet t,
Relation<Double> scores)
M-Step using a modified Levenberg-Marquardt method.
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