Uses of Interface
de.lmu.ifi.dbs.elki.database.query.DistanceResultPair

Packages that use DistanceResultPair

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.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.database.query	Database queries - computing distances, neighbors, similarities - API and general documentation.
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
de.lmu.ifi.dbs.elki.database.query.range	Prepared queries for ε-range queries.
de.lmu.ifi.dbs.elki.database.query.rknn	Prepared queries for reverse k nearest neighbor (rkNN) queries.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
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.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA.
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj	Index using a preprocessed local subspaces.
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.mkcop	MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query	Classes for performing queries (knn, range, ...) on metrical trees.
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.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.utilities.datastructures.heap	Heap structures and variations such as bounded priority heaps.

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

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

private Map<DBID,List<DistanceResultPair<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.

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

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

protected List<DistanceResultPair<D>>

ReferenceBasedOutlierDetection.computeDistanceVector(V refPoint, Relation<V> database, DistanceQuery<V,D> distFunc) Computes for each object the distance to one reference point.

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

protected double	ReferenceBasedOutlierDetection.computeDensity(List<DistanceResultPair<D>> referenceDists, int index) Computes the density of an object.
private ArrayModifiableDBIDs	OnlineLOF.LOFKNNListener.mergeIDs(List<List<DistanceResultPair<D>>> queryResults, DBIDs... ids) Merges the ids of the query result with the specified ids.

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.database.query

Classes in de.lmu.ifi.dbs.elki.database.query that implement DistanceResultPair

class	DoubleDistanceResultPair Optimized DistanceResultPair that avoids/postpones an extra layer of boxing for double values.
class	GenericDistanceResultPair<D extends Distance<D>> Trivial implementation using a generic pair.

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

int	DistanceResultPair.compareByDistance(DistanceResultPair<D> o) Compare value, but by distance only.
int	GenericDistanceResultPair.compareByDistance(DistanceResultPair<D> o)
int	DoubleDistanceResultPair.compareByDistance(DistanceResultPair<DoubleDistance> o)
int	GenericDistanceResultPair.compareTo(DistanceResultPair<D> o)
int	DoubleDistanceResultPair.compareTo(DistanceResultPair<DoubleDistance> o)

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.database.query.knn

Methods in de.lmu.ifi.dbs.elki.database.query.knn that return types with arguments of type DistanceResultPair

List<List<DistanceResultPair<D>>>	KNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids, int k) Bulk query method
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)
List<DistanceResultPair<D>>	KNNQuery.getKNNForDBID(DBID id, int k) Get the k nearest neighbors for a particular id.
List<DistanceResultPair<DoubleDistance>>	LinearScanRawDoubleDistanceKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	PreprocessorKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	LinearScanKNNQuery.getKNNForDBID(DBID id, int k)
abstract List<DistanceResultPair<D>>	AbstractDistanceKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	LinearScanPrimitiveDistanceKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	KNNQuery.getKNNForObject(O obj, int k) Get the k nearest neighbors for a particular id.
List<DistanceResultPair<DoubleDistance>>	LinearScanRawDoubleDistanceKNNQuery.getKNNForObject(O obj, int k)
List<DistanceResultPair<D>>	PreprocessorKNNQuery.getKNNForObject(O obj, int k)
List<DistanceResultPair<D>>	LinearScanKNNQuery.getKNNForObject(O obj, int k)
abstract List<DistanceResultPair<D>>	AbstractDistanceKNNQuery.getKNNForObject(O obj, int k)

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.database.query.range

Methods in de.lmu.ifi.dbs.elki.database.query.range that return types with arguments of type DistanceResultPair

List<List<DistanceResultPair<D>>>	RangeQuery.getRangeForBulkDBIDs(ArrayDBIDs ids, D range) Bulk query method
List<List<DistanceResultPair<D>>>	AbstractDistanceRangeQuery.getRangeForBulkDBIDs(ArrayDBIDs ids, D range)
List<DistanceResultPair<D>>	LinearScanPrimitiveDistanceRangeQuery.getRangeForDBID(DBID id, D range)
List<DistanceResultPair<D>>	RangeQuery.getRangeForDBID(DBID id, D range) Get the nearest neighbors for a particular id in a given query range
abstract List<DistanceResultPair<D>>	AbstractDistanceRangeQuery.getRangeForDBID(DBID id, D range)
List<DistanceResultPair<D>>	LinearScanRangeQuery.getRangeForDBID(DBID id, D range)
List<DistanceResultPair<DoubleDistance>>	LinearScanRawDoubleDistanceRangeQuery.getRangeForDBID(DBID id, DoubleDistance range)
List<DistanceResultPair<D>>	RangeQuery.getRangeForObject(O obj, D range) Get the nearest neighbors for a particular object in a given query range
abstract List<DistanceResultPair<D>>	AbstractDistanceRangeQuery.getRangeForObject(O obj, D range)
List<DistanceResultPair<D>>	LinearScanRangeQuery.getRangeForObject(O obj, D range)
List<DistanceResultPair<DoubleDistance>>	LinearScanRawDoubleDistanceRangeQuery.getRangeForObject(O obj, DoubleDistance range)

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.database.query.rknn

Methods in de.lmu.ifi.dbs.elki.database.query.rknn that return types with arguments of type DistanceResultPair

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.
List<List<DistanceResultPair<D>>>	PreprocessorRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids, int k)
List<DistanceResultPair<D>>	LinearScanRKNNQuery.getRKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	RKNNQuery.getRKNNForDBID(DBID id, int k) Get the reverse k nearest neighbors for a particular id.
List<DistanceResultPair<D>>	PreprocessorRKNNQuery.getRKNNForDBID(DBID id, int k)
abstract List<DistanceResultPair<D>>	AbstractRKNNQuery.getRKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	LinearScanRKNNQuery.getRKNNForObject(O obj, int k)
List<DistanceResultPair<D>>	RKNNQuery.getRKNNForObject(O obj, int k) Get the reverse k nearest neighbors for a particular object.
List<DistanceResultPair<D>>	PreprocessorRKNNQuery.getRKNNForObject(O obj, int k)

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

Method parameters in de.lmu.ifi.dbs.elki.distance.distancefunction with type arguments of type DistanceResultPair

protected D

MinKDistance.computeReachdist(List<DistanceResultPair<D>> neighborhood, D truedist) Actually compute the distance, whichever way we obtained the neighborhood above.

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.evaluation.roc

Fields in de.lmu.ifi.dbs.elki.evaluation.roc with type parameters of type DistanceResultPair

private Iterator<DistanceResultPair<D>>

ROC.DistanceResultAdapter.iter Original Iterator

Method parameters in de.lmu.ifi.dbs.elki.evaluation.roc with type arguments of type DistanceResultPair

static <D extends Distance<D>> double	ROC.computeROCAUCDistanceResult(int size, Cluster<?> clus, List<DistanceResultPair<D>> nei) Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.
static <D extends Distance<D>> double	ROC.computeROCAUCDistanceResult(int size, DBIDs ids, List<DistanceResultPair<D>> nei) Compute a ROC curves Area-under-curve for a QueryResult and a Cluster.

Constructor parameters in de.lmu.ifi.dbs.elki.evaluation.roc with type arguments of type DistanceResultPair

ROC.DistanceResultAdapter(Iterator<DistanceResultPair<D>> iter)
Constructor

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

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.knn with type parameters of type DistanceResultPair

private WritableDataStore<SortedSet<DistanceResultPair<D>>>

MaterializeKNNAndRKNNPreprocessor.materialized_RkNN Additional data storage for RkNN.

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

List<DistanceResultPair<D>>	MaterializeKNNPreprocessor.get(DBID objid) Get the k nearest neighbors.
List<DistanceResultPair<D>>	MaterializeKNNAndRKNNPreprocessor.getKNN(DBID id) Returns the materialized kNNs of the specified id.
List<DistanceResultPair<D>>	MaterializeKNNAndRKNNPreprocessor.getRKNN(DBID id) Returns the materialized RkNNs of the specified id.

Method parameters in de.lmu.ifi.dbs.elki.index.preprocessed.knn with type arguments of type DistanceResultPair

protected ArrayDBIDs

MaterializeKNNPreprocessor.extractAndRemoveIDs(List<List<DistanceResultPair<D>>> extraxt, ArrayDBIDs remove) Extracts and removes the DBIDs in the given collections.

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

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

protected abstract List<DistanceResultPair<DoubleDistance>>	AbstractFilteredPCAIndex.objectsForPCA(DBID id) Returns the objects to be considered within the PCA for the specified query object.
protected List<DistanceResultPair<DoubleDistance>>	KNNQueryFilteredPCAIndex.objectsForPCA(DBID id)
protected List<DistanceResultPair<DoubleDistance>>	RangeQueryFilteredPCAIndex.objectsForPCA(DBID id)

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj

Method parameters in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj with type arguments of type DistanceResultPair

protected abstract P	AbstractSubspaceProjectionIndex.computeProjection(DBID id, List<DistanceResultPair<D>> neighbors, Relation<NV> relation) This method implements the type of variance analysis to be computed for a given point.
protected SubspaceProjectionResult	PreDeConSubspaceIndex.computeProjection(DBID id, List<DistanceResultPair<D>> neighbors, Relation<V> database)
protected PCAFilteredResult	FourCSubspaceIndex.computeProjection(DBID id, List<DistanceResultPair<D>> neighbors, Relation<V> database)

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees that return types with arguments of type DistanceResultPair

abstract List<DistanceResultPair<D>>

AbstractMkTree.reverseKNNQuery(DBID id, int k) Performs a reverse k-nearest neighbor query for the given object ID.

Uses of DistanceResultPair 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 that return types with arguments of type DistanceResultPair

private List<DistanceResultPair<D>>	MkAppTree.doReverseKNNQuery(int k, DBID q) Performs a reverse knn query.
List<DistanceResultPair<D>>	MkAppTree.reverseKNNQuery(DBID id, int k) Performs a reverse k-nearest neighbor query for the given object ID.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop that return types with arguments of type DistanceResultPair

List<DistanceResultPair<D>>

MkCoPTree.reverseKNNQuery(DBID id, int k) Performs a reverse k-nearest neighbor query for the given object ID.

Method parameters in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop with type arguments of type DistanceResultPair

private void

MkCoPTree.doReverseKNNQuery(int k, DBID q, List<DistanceResultPair<D>> result, ModifiableDBIDs candidates) Performs a reverse knn query.

Uses of DistanceResultPair 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 that return types with arguments of type DistanceResultPair

List<DistanceResultPair<D>>

MkMaxTree.reverseKNNQuery(DBID id, int k) Performs a reverse k-nearest neighbor query for the given object ID.

Method parameters in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with type arguments of type DistanceResultPair

private void

MkMaxTree.doReverseKNNQuery(DBID q, MkMaxTreeNode<O,D> node, MkMaxEntry<D> node_entry, List<DistanceResultPair<D>> result) Performs a reverse k-nearest neighbor query in the specified subtree for the given query object with k = AbstractMkTreeUnified.k_max.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab that return types with arguments of type DistanceResultPair

List<DistanceResultPair<D>>

MkTabTree.reverseKNNQuery(DBID id, int k)

Method parameters in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with type arguments of type DistanceResultPair

private void

MkTabTree.doReverseKNNQuery(int k, DBID q, MkTabEntry<D> node_entry, MkTabTreeNode<O,D> node, List<DistanceResultPair<D>> result) Performs a k-nearest neighbor query in the specified subtree for the given query object and the given parameter k.

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query that return types with arguments of type DistanceResultPair

List<List<DistanceResultPair<D>>>	MetricalIndexKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids, int k)
List<DistanceResultPair<D>>	MetricalIndexKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	MetricalIndexKNNQuery.getKNNForObject(O obj, int k)
List<DistanceResultPair<D>>	MetricalIndexRangeQuery.getRangeForDBID(DBID id, D range)
List<DistanceResultPair<D>>	MetricalIndexRangeQuery.getRangeForObject(O obj, D range)
List<List<DistanceResultPair<D>>>	MkTreeRKNNQuery.getRKNNForBulkDBIDs(ArrayDBIDs ids, int k)
List<DistanceResultPair<D>>	MkTreeRKNNQuery.getRKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	MkTreeRKNNQuery.getRKNNForObject(O obj, int k)

Method parameters in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query with type arguments of type DistanceResultPair

private void	MetricalIndexRangeQuery.doRangeQuery(DBID o_p, AbstractMTreeNode<O,D,?,?> node, DBID q, D r_q, List<DistanceResultPair<D>> result) Performs a range query on the specified subtree.
private void	MetricalIndexRangeQuery.doRangeQuery(DBID o_p, AbstractMTreeNode<O,D,?,?> node, O q, D r_q, List<DistanceResultPair<D>> result) Performs a range query on the specified subtree.

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

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query that return types with arguments of type DistanceResultPair

protected List<DistanceResultPair<D>>	GenericRStarTreeRangeQuery.doRangeQuery(O object, D epsilon) Perform the actual query process.
protected List<DistanceResultPair<DoubleDistance>>	DoubleDistanceRStarTreeRangeQuery.doRangeQuery(O object, double epsilon) Perform the actual query process.
List<List<DistanceResultPair<DoubleDistance>>>	DoubleDistanceRStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids, int k)
List<List<DistanceResultPair<D>>>	GenericRStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids, int k)
List<DistanceResultPair<DoubleDistance>>	DoubleDistanceRStarTreeKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<D>>	GenericRStarTreeKNNQuery.getKNNForDBID(DBID id, int k)
List<DistanceResultPair<DoubleDistance>>	DoubleDistanceRStarTreeKNNQuery.getKNNForObject(O obj, int k)
List<DistanceResultPair<D>>	GenericRStarTreeKNNQuery.getKNNForObject(O obj, int k)
List<DistanceResultPair<D>>	GenericRStarTreeRangeQuery.getRangeForDBID(DBID id, D range)
List<DistanceResultPair<DoubleDistance>>	DoubleDistanceRStarTreeRangeQuery.getRangeForDBID(DBID id, DoubleDistance range)
List<DistanceResultPair<D>>	GenericRStarTreeRangeQuery.getRangeForObject(O obj, D range)
List<DistanceResultPair<DoubleDistance>>	DoubleDistanceRStarTreeRangeQuery.getRangeForObject(O obj, DoubleDistance range)

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

Method parameters in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with type arguments of type DistanceResultPair

<D extends NumberDistance<?,?>> PCAResult	PCARunner.processQueryResult(Collection<DistanceResultPair<D>> results, Relation<? extends V> database) Run PCA on a QueryResult Collection
<D extends NumberDistance<?,?>> PCAFilteredResult	PCAFilteredRunner.processQueryResult(Collection<DistanceResultPair<D>> results, Relation<? extends V> database) Run PCA on a QueryResult Collection
<D extends NumberDistance<?,?>> Matrix	AbstractCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database)
<D extends NumberDistance<?,?>> Matrix	CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
<D extends NumberDistance<?,?>> Matrix	WeightedCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database, int k) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
<D extends NumberDistance<?,?>> Matrix	AbstractCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database, int k)
<D extends NumberDistance<?,?>> Matrix	CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Relation<? extends V> database, int k) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds

Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.utilities.datastructures.heap

Fields in de.lmu.ifi.dbs.elki.utilities.datastructures.heap with type parameters of type DistanceResultPair

(package private) Iterator<? extends DistanceResultPair<?>>	KNNList.DBIDItr.itr The real iterator.
(package private) Iterator<? extends DistanceResultPair<D>>	KNNList.DistanceItr.itr The real iterator.
(package private) List<? extends DistanceResultPair<?>>	KNNList.DBIDView.parent The true list.
(package private) List<? extends DistanceResultPair<D>>	KNNList.DistanceView.parent The true list.

Methods in de.lmu.ifi.dbs.elki.utilities.datastructures.heap that return DistanceResultPair

DistanceResultPair<D>	KNNList.remove(int index)
DistanceResultPair<D>	KNNList.set(int index, DistanceResultPair<D> element)

Methods in de.lmu.ifi.dbs.elki.utilities.datastructures.heap that return types with arguments of type DistanceResultPair

ArrayList<DistanceResultPair<D>>

KNNHeap.toSortedArrayList()

Methods in de.lmu.ifi.dbs.elki.utilities.datastructures.heap with parameters of type DistanceResultPair

boolean	KNNList.add(DistanceResultPair<D> e)
void	KNNList.add(int index, DistanceResultPair<D> element)
int	KNNHeap.Comp.compare(DistanceResultPair<D> o1, DistanceResultPair<D> o2)
int	KNNHeap.Comp.compare(DistanceResultPair<D> o1, DistanceResultPair<D> o2)
DistanceResultPair<D>	KNNList.set(int index, DistanceResultPair<D> element)

Method parameters in de.lmu.ifi.dbs.elki.utilities.datastructures.heap with type arguments of type DistanceResultPair

boolean	KNNList.addAll(Collection<? extends DistanceResultPair<D>> c)
boolean	KNNList.addAll(int index, Collection<? extends DistanceResultPair<D>> c)
static ArrayDBIDs	KNNList.asDBIDs(List<? extends DistanceResultPair<?>> list) View as ArrayDBIDs
static <D extends Distance<D>> List<D>	KNNList.asDistanceList(List<? extends DistanceResultPair<D>> list) View as list of distances

Constructor parameters in de.lmu.ifi.dbs.elki.utilities.datastructures.heap with type arguments of type DistanceResultPair

KNNList.DBIDItr(Iterator<? extends DistanceResultPair<?>> itr)
Constructor.

KNNList.DBIDView(List<? extends DistanceResultPair<?>> parent)
Constructor.

KNNList.DistanceItr(Iterator<? extends DistanceResultPair<D>> itr)
Constructor.

KNNList.DistanceView(List<? extends DistanceResultPair<D>> parent)
Constructor.