Modifier and Type | Field and Description |
---|---|
protected KNNQuery<O> |
KNNClassifier.knnq
kNN query class.
|
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.
|
Modifier and Type | Method and Description |
---|---|
protected WritableDoubleDataStore |
AbstractHDBSCAN.computeCoreDists(DBIDs ids,
KNNQuery<O> knnQ,
int minPts)
Compute the core distances for all objects.
|
Modifier and Type | Method and Description |
---|---|
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)
|
Modifier and Type | Method and Description |
---|---|
protected DoubleDataStore |
IDOS.computeIDOS(DBIDs ids,
KNNQuery<O> knnQ,
DoubleDataStore intDims,
DoubleMinMax idosminmax)
Computes all IDOS scores.
|
protected DoubleDataStore |
IDOS.computeIDs(DBIDs ids,
KNNQuery<O> knnQ)
Computes all IDs
|
Modifier and Type | Field and Description |
---|---|
private KNNQuery<O> |
FlexibleLOF.LOFResult.kNNReach
The kNN query w.r.t. the reachability distance.
|
private KNNQuery<O> |
FlexibleLOF.LOFResult.kNNRefer
The kNN query w.r.t. the reference neighborhood distance.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
FlexibleLOF.LOFResult.getKNNReach()
Get the knn query for the reachability set.
|
KNNQuery<O> |
FlexibleLOF.LOFResult.getKNNRefer()
Get the knn query for the reference set.
|
Modifier and Type | Method and Description |
---|---|
private Pair<Pair<KNNQuery<O>,KNNQuery<O>>,Pair<RKNNQuery<O>,RKNNQuery<O>>> |
OnlineLOF.getKNNAndRkNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN and rkNN queries for the algorithm.
|
private Pair<Pair<KNNQuery<O>,KNNQuery<O>>,Pair<RKNNQuery<O>,RKNNQuery<O>>> |
OnlineLOF.getKNNAndRkNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN and rkNN queries for the algorithm.
|
protected Pair<KNNQuery<O>,KNNQuery<O>> |
LoOP.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
protected Pair<KNNQuery<O>,KNNQuery<O>> |
LoOP.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
private Pair<KNNQuery<O>,KNNQuery<O>> |
FlexibleLOF.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
private Pair<KNNQuery<O>,KNNQuery<O>> |
FlexibleLOF.getKNNQueries(Database database,
Relation<O> relation,
StepProgress stepprog)
Get the kNN queries for the algorithm.
|
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.
|
protected double |
LOF.computeLOFScore(KNNQuery<O> knnq,
DBIDRef cur,
DoubleDataStore lrds)
Compute a single LOF score.
|
private void |
LOF.computeLOFScores(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore lrds,
WritableDoubleDataStore lofs,
DoubleMinMax lofminmax)
Compute local outlier factors.
|
protected double |
LOF.computeLRD(KNNQuery<O> knnq,
DBIDIter curr)
Compute a single local reachability distance.
|
private void |
LOF.computeLRDs(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore lrds)
Compute local reachability distances.
|
protected void |
FlexibleLOF.computeLRDs(KNNQuery<O> knnq,
DBIDs ids,
WritableDoubleDataStore lrds)
Computes the local reachability density (LRD) of the specified objects.
|
protected void |
INFLO.computeNeighborhoods(Relation<O> relation,
KNNQuery<O> knnQuery,
ModifiableDBIDs pruned,
WritableDataStore<ModifiableDBIDs> knns,
WritableDataStore<ModifiableDBIDs> rnns,
WritableDoubleDataStore density)
Compute neighborhoods
|
protected void |
KDEOS.computeOutlierScores(KNNQuery<O> knnq,
DBIDs ids,
WritableDataStore<double[]> densities,
WritableDoubleDataStore kdeos,
DoubleMinMax minmax)
Compute the final KDEOS scores.
|
protected void |
LoOP.computePDists(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists)
Compute the probabilistic distances used by LoOP.
|
protected double |
LoOP.computePLOFs(Relation<O> relation,
KNNQuery<O> knn,
WritableDoubleDataStore pdists,
WritableDoubleDataStore plofs)
Compute the LOF values, using the pdist distances.
|
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.
|
private void |
VarianceOfVolume.computeVolumes(KNNQuery<O> knnq,
int dim,
DBIDs ids,
WritableDoubleDataStore vols)
Compute volumes
|
private void |
VarianceOfVolume.computeVOVs(KNNQuery<O> knnq,
DBIDs ids,
DoubleDataStore vols,
WritableDoubleDataStore vovs,
DoubleMinMax vovminmax)
Compute variance of volumes.
|
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 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.
|
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
|
Constructor and Description |
---|
FlexibleLOF.LOFResult(OutlierResult result,
KNNQuery<O> kNNRefer,
KNNQuery<O> kNNReach,
WritableDoubleDataStore lrds,
WritableDoubleDataStore lofs)
Encapsulates information generated during a run of the
FlexibleLOF algorithm. |
FlexibleLOF.LOFResult(OutlierResult result,
KNNQuery<O> kNNRefer,
KNNQuery<O> kNNReach,
WritableDoubleDataStore lrds,
WritableDoubleDataStore lofs)
Encapsulates information generated during a run of the
FlexibleLOF algorithm. |
Modifier and Type | Method and Description |
---|---|
protected double |
HopkinsStatisticClusteringTendency.computeNNForRealData(KNNQuery<NumberVector> knnQuery,
Relation<NumberVector> relation,
int dim)
Search nearest neighbors for real data members.
|
protected double |
HopkinsStatisticClusteringTendency.computeNNForUniformData(KNNQuery<NumberVector> knnQuery,
double[] min,
double[] extend)
Search nearest neighbors for artificial, uniform data.
|
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
QueryUtil.getKNNQuery(Database database,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
<O> KNNQuery<O> |
Database.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
<O> KNNQuery<O> |
AbstractDatabase.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
static <O> KNNQuery<O> |
QueryUtil.getKNNQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance function.
|
static <O> KNNQuery<O> |
QueryUtil.getLinearScanKNNQuery(DistanceQuery<O> distanceQuery)
Get a linear scan query for the given distance query.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDistanceKNNQuery<O>
Instance for the query on a particular database.
|
class |
LinearScanDistanceKNNQuery<O>
Instance of this query for a particular database.
|
class |
LinearScanEuclideanDistanceKNNQuery<O extends NumberVector>
Instance of this query for a particular database.
|
class |
LinearScanPrimitiveDistanceKNNQuery<O>
Instance of this query for a particular database.
|
class |
PreprocessorKNNQuery<O>
Instance for a particular database, invoking the preprocessor.
|
Modifier and Type | Field and Description |
---|---|
protected KNNQuery<O> |
LinearScanRKNNQuery.knnQuery
KNN query we use.
|
Constructor and Description |
---|
LinearScanRKNNQuery(DistanceQuery<O> distanceQuery,
KNNQuery<O> knnQuery,
Integer maxk)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
Relation.getKNNQuery(DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance query.
|
KNNQuery<O> |
AbstractRelation.getKNNQuery(DistanceFunction<? super O> distanceFunction,
Object... hints) |
KNNQuery<O> |
Relation.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
KNNQuery<O> |
AbstractRelation.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
AbstractRefiningIndex.AbstractKNNQuery
KNN query for this index.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
KNNIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Modifier and Type | Class and Description |
---|---|
protected class |
InMemoryIDistanceIndex.IDistanceKNNQuery
kNN query implementation.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
InMemoryIDistanceIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
protected class |
InMemoryInvertedIndex.ArcCosineKNNQuery
kNN query object, for arc cosine distance.
|
protected class |
InMemoryInvertedIndex.CosineKNNQuery
kNN query object, for cosine distance.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
InMemoryInvertedIndex.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
protected class |
InMemoryLSHIndex.Instance.LSHKNNQuery
Class for handling kNN queries against the LSH index.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
InMemoryLSHIndex.Instance.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
protected class |
NaiveProjectedKNNPreprocessor.NaiveProjectedKNNQuery
KNN Query processor for naive projections.
|
protected class |
SpacefillingKNNPreprocessor.SpaceFillingKNNQuery
KNN Query processor for space filling curves
|
Modifier and Type | Field and Description |
---|---|
protected KNNQuery<O> |
MaterializeKNNPreprocessor.knnQuery
KNNQuery instance to use.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SpacefillingMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O> distQ,
Object... hints) |
KNNQuery<O> |
SpacefillingKNNPreprocessor.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
NaiveProjectedKNNPreprocessor.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
AbstractMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O> distQ,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
private KNNQuery<NV> |
KNNQueryFilteredPCAIndex.knnQuery
The kNN query instance we use.
|
Constructor and Description |
---|
KNNQueryFilteredPCAIndex(Relation<NV> relation,
PCAFilteredRunner pca,
KNNQuery<NV> knnQuery,
int k)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
(package private) class |
ProjectedIndex.ProjectedKNNQuery
Class to proxy kNN queries.
|
Modifier and Type | Field and Description |
---|---|
(package private) KNNQuery<I> |
ProjectedIndex.ProjectedKNNQuery.inner
Inner kNN query.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
ProjectedIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
LngLatAsECEFIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
LatLngAsECEFIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
ProjectedIndex.ProjectedKNNQuery(DistanceQuery<O> distanceQuery,
KNNQuery<I> inner)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
CoverTree.CoverTreeKNNQuery
KNN Query class.
|
class |
SimplifiedCoverTree.CoverTreeKNNQuery
KNN Query class.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SimplifiedCoverTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
CoverTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
protected KNNQuery<O> |
AbstractMkTree.knnq
Internal class for performing knn queries
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkAppTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkCoPTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkMaxTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkTabTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
MetricalIndexKNNQuery<O>
Instance of a KNN query for a particular spatial index.
|
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
MTreeQueryUtil.getKNNQuery(AbstractMTree<O,?,?,?> tree,
DistanceQuery<O> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
Modifier and Type | Class and Description |
---|---|
class |
MinimalisticMemoryKDTree.KDTreeKNNQuery
kNN query for the k-d-tree.
|
class |
SmallMemoryKDTree.KDTreeKNNQuery
kNN query for the k-d-tree.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SmallMemoryKDTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
MinimalisticMemoryKDTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
DeLiCluTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
FlatRStarTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
EuclideanRStarTreeKNNQuery<O extends NumberVector>
Instance of a KNN query for a particular spatial index.
|
class |
RStarTreeKNNQuery<O extends SpatialComparable>
Instance of a KNN query for a particular spatial index.
|
Modifier and Type | Method and Description |
---|---|
static <O extends SpatialComparable> |
RStarTreeUtil.getKNNQuery(AbstractRStarTree<?,?,?> tree,
SpatialDistanceQuery<O> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
Modifier and Type | Field and Description |
---|---|
protected KNNQuery<O> |
RdKNNTree.knnQuery
Internal knn query object, for updating the rKNN.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
RdKNNTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
List<ModifiableDoubleDBIDList> |
RdKNNTree.bulkReverseKNNQueryForID(DBIDs ids,
int k,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction,
KNNQuery<O> knnQuery) |
DoubleDBIDList |
RdKNNTree.reverseKNNQuery(DBID oid,
int k,
SpatialPrimitiveDistanceFunction<? super O> distanceFunction,
KNNQuery<O> knnQuery) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
RStarTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Class and Description |
---|---|
class |
PartialVAFile.PartialVAFileKNNQuery
KNN query for this index.
|
class |
VAFile.VAFileKNNQuery
KNN query for this index.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
VAFile.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
KNNQuery<V> |
PartialVAFile.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
(package private) KNNQuery<O> |
KNNProcessor.knnq
KNN query object
|
(package private) KNNQuery<O> |
KNNProcessor.Instance.knnq
kNN query
|
Constructor and Description |
---|
KNNProcessor.Instance(int k,
KNNQuery<O> knnq,
SharedObject.Instance<KNNList> out)
Constructor.
|
KNNProcessor(int k,
KNNQuery<O> knnq)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceFunction<? super O> distf,
int k)
Get (or create) a precomputed kNN query for the database.
|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceQuery<O> dq,
int k)
Get (or create) a precomputed kNN query for the database.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.