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Packages that use DistanceResultPair | |
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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.data | Basic classes for different data types, database object types and label types. |
de.lmu.ifi.dbs.elki.database | ELKI database layer - loading, storing, indexing and accessing data |
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.tree.metrical | Tree-based index structures for metrical vector spaces. |
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants | M-Tree and variants. |
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.mtree | MTree |
de.lmu.ifi.dbs.elki.index.tree.spatial | Tree-based index structures for spatial indexing. |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants | R*-Tree and variants. |
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn | RdKNNTree |
de.lmu.ifi.dbs.elki.math.linearalgebra.pca | Principal Component Analysis (PCA) and Eigenvector processing. |
de.lmu.ifi.dbs.elki.preprocessing | Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return types with arguments of type DistanceResultPair | |
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private Map<Integer,List<DistanceResultPair<DoubleDistance>>> |
PROCLUS.getLocalities(Set<Integer> m_c,
Database<V> database)
Computes the localities of the specified medoids. |
Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type arguments of type DistanceResultPair | |
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private Map<Integer,Set<Integer>> |
PROCLUS.findDimensions(Set<Integer> medoids,
Database<V> database,
Map<Integer,List<DistanceResultPair<DoubleDistance>>> localities)
Determines the set of correlated dimensions for each medoid in the specified medoid set. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.algorithm.outlier |
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Methods in de.lmu.ifi.dbs.elki.algorithm.outlier that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<DoubleDistance>> |
ReferenceBasedOutlierDetection.computeDistanceVector(V refPoint,
Database<V> database)
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 | |
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double |
ReferenceBasedOutlierDetection.computeDensity(List<DistanceResultPair<DoubleDistance>> referenceDists,
int index)
Computes the density of an object. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.data |
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Fields in de.lmu.ifi.dbs.elki.data with type parameters of type DistanceResultPair | |
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private SortedSet<DistanceResultPair<D>> |
KNNList.list
The underlying set. |
Methods in de.lmu.ifi.dbs.elki.data that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<D>> |
KNNList.toList()
Returns a list representation of this KList. |
Methods in de.lmu.ifi.dbs.elki.data with parameters of type DistanceResultPair | |
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boolean |
KNNList.add(DistanceResultPair<D> o)
Adds a new object to this list. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.database |
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Methods in de.lmu.ifi.dbs.elki.database that return types with arguments of type DistanceResultPair | ||
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Database.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,D> distanceFunction)
Performs k-nearest neighbor queries for the given object IDs. |
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SequentialDatabase.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,D> distanceFunction)
Retrieves the k nearest neighbors for the query objects. |
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SpatialIndexDatabase.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.bulkKNNQueryForID(List<Integer> ids,
int k,
DistanceFunction<O,T> distanceFunction)
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Database.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a k-nearest neighbor query for the given object ID. |
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SequentialDatabase.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Retrieves the k nearest neighbors for the query object. |
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SpatialIndexDatabase.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.kNNQueryForID(Integer id,
int k,
DistanceFunction<O,T> distanceFunction)
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Database.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a k-nearest neighbor query for the given object. |
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SequentialDatabase.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,D> distanceFunction)
Retrieves the k nearest neighbors for the query object. |
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SpatialIndexDatabase.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.kNNQueryForObject(O queryObject,
int k,
DistanceFunction<O,T> distanceFunction)
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Database.rangeQuery(Integer id,
D epsilon,
DistanceFunction<O,D> distanceFunction)
Performs a range query for the given object ID with the given epsilon range and the according distance function. |
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SequentialDatabase.rangeQuery(Integer id,
D epsilon,
DistanceFunction<O,D> distanceFunction)
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SpatialIndexDatabase.rangeQuery(Integer id,
D epsilon,
DistanceFunction<O,D> distanceFunction)
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Database.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,D> distanceFunction)
Performs a range query for the given object ID with the given epsilon range and the according distance function. |
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SequentialDatabase.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,D> distanceFunction)
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SpatialIndexDatabase.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,D> distanceFunction)
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MetricalIndexDatabase.rangeQuery(Integer id,
String epsilon,
DistanceFunction<O,T> distanceFunction)
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MetricalIndexDatabase.rangeQuery(Integer id,
T epsilon,
DistanceFunction<O,T> distanceFunction)
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Database.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
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SequentialDatabase.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
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SpatialIndexDatabase.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,D> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
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MetricalIndexDatabase.reverseKNNQuery(Integer id,
int k,
DistanceFunction<O,T> distanceFunction)
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Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.evaluation.roc |
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Fields in de.lmu.ifi.dbs.elki.evaluation.roc with type parameters of type DistanceResultPair | |
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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 | ||
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static
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ROC.computeROCAUCDistanceResult(int size,
Cluster<?> clus,
List<DistanceResultPair<D>> nei)
Compute a ROC curves Area-under-curve for a QueryResult and a Cluster. |
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static
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ROC.computeROCAUCDistanceResult(int size,
Collection<Integer> 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 | |
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ROC.DistanceResultAdapter(Iterator<DistanceResultPair<D>> iter)
Constructor |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.metrical |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical that return types with arguments of type DistanceResultPair | |
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abstract List<DistanceResultPair<D>> |
MetricalIndex.kNNQuery(O object,
int k)
Performs a k-nearest neighbor query for the given object with the given parameter k and the according distance function. |
abstract List<DistanceResultPair<D>> |
MetricalIndex.rangeQuery(O object,
D epsilon)
Performs a range query for the given object with the given epsilon range and the according distance function. |
abstract List<DistanceResultPair<D>> |
MetricalIndex.rangeQuery(O object,
String epsilon)
Performs a range query for the given object with the given epsilon range and the according distance function. |
abstract List<DistanceResultPair<D>> |
MetricalIndex.reverseKNNQuery(O object,
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 |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<D>> |
AbstractMTree.kNNQuery(O object,
int k)
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List<DistanceResultPair<D>> |
AbstractMTree.rangeQuery(O object,
D epsilon)
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List<DistanceResultPair<D>> |
AbstractMTree.rangeQuery(O object,
String epsilon)
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Method parameters in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants with type arguments of type DistanceResultPair | |
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private void |
AbstractMTree.doRangeQuery(Integer o_p,
N node,
Integer 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.metrical.mtreevariants.mktrees.mkapp |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp that return types with arguments of type DistanceResultPair | |
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private List<DistanceResultPair<D>> |
MkAppTree.doReverseKNNQuery(int k,
Integer q)
Performs a reverse knn query. |
List<DistanceResultPair<D>> |
MkAppTree.reverseKNNQuery(O object,
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 |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<D>> |
MkCoPTree.reverseKNNQuery(O object,
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 | |
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private void |
MkCoPTree.doReverseKNNQuery(int k,
Integer q,
List<DistanceResultPair<D>> result,
List<Integer> candidates)
Performs a reverse knn query. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<D>> |
MkMaxTree.reverseKNNQuery(O object,
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 | |
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private void |
MkMaxTree.doReverseKNNQuery(Integer 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 = AbstractMkTree.k_max . |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<D>> |
MkTabTree.reverseKNNQuery(O object,
int k)
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Method parameters in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with type arguments of type DistanceResultPair | |
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private void |
MkTabTree.doReverseKNNQuery(int k,
Integer 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.mtree |
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Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree that return types with arguments of type DistanceResultPair | |
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List<DistanceResultPair<D>> |
MTree.reverseKNNQuery(O object,
int k)
Throws an UnsupportedOperationException since reverse knn queries are not yet supported by an M-Tree. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.spatial |
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Methods in de.lmu.ifi.dbs.elki.index.tree.spatial that return types with arguments of type DistanceResultPair | ||
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abstract
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SpatialIndex.bulkKNNQueryForIDs(List<Integer> ids,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a bulk k-nearest neighbor query for the given object IDs. |
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abstract
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SpatialIndex.kNNQuery(O obj,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a k-nearest neighbor query for the given object with the given parameter k and the according distance function. |
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abstract
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SpatialIndex.rangeQuery(O obj,
D epsilon,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a range query for the given object with the given epsilon range and the according distance function. |
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abstract
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SpatialIndex.rangeQuery(O obj,
String epsilon,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a range query for the given object with the given epsilon range and the according distance function. |
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abstract
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SpatialIndex.reverseKNNQuery(O object,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants |
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Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants that return types with arguments of type DistanceResultPair | ||
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AbstractRStarTree.bulkKNNQueryForIDs(List<Integer> ids,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a bulk k-nearest neighbor query for the given object IDs. |
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AbstractRStarTree.kNNQuery(O object,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a k-nearest neighbor query for the given NumberVector with the given parameter k and the according distance function. |
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AbstractRStarTree.rangeQuery(O object,
D epsilon,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a range query for the given spatial object with the given epsilon range and the according distance function. |
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AbstractRStarTree.rangeQuery(O object,
String epsilon,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a range query for the given spatial object with the given epsilon range and the according distance function. |
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AbstractRStarTree.reverseKNNQuery(O object,
int k,
SpatialDistanceFunction<O,D> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn |
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Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn that return types with arguments of type DistanceResultPair | ||
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RdKNNTree.reverseKNNQuery(O object,
int k,
SpatialDistanceFunction<O,T> distanceFunction)
Performs a reverse k-nearest neighbor query for the given object ID. |
Method parameters in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn with type arguments of type DistanceResultPair | |
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private void |
RdKNNTree.doReverseKNN(RdKNNNode<D,N> node,
O o,
List<DistanceResultPair<D>> result)
Performs a reverse knn query in the specified subtree. |
Uses of DistanceResultPair in de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
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Method parameters in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with type arguments of type DistanceResultPair | |
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PCAFilteredResult |
PCAFilteredRunner.processQueryResult(Collection<DistanceResultPair<D>> results,
Database<V> database)
Run PCA on a QueryResult Collection |
PCAResult |
PCARunner.processQueryResult(Collection<DistanceResultPair<D>> results,
Database<V> database)
Run PCA on a QueryResult Collection |
Matrix |
CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results,
Database<V> database)
Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds |
Matrix |
WeightedCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results,
Database<V> database,
int k)
Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds |
Matrix |
CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results,
Database<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.preprocessing |
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Fields in de.lmu.ifi.dbs.elki.preprocessing with type parameters of type DistanceResultPair | |
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protected HashMap<Integer,List<DistanceResultPair<D>>> |
MaterializeKNNPreprocessor.materialized
Materialized neighborhood |
Methods in de.lmu.ifi.dbs.elki.preprocessing that return types with arguments of type DistanceResultPair | |
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HashMap<Integer,List<DistanceResultPair<D>>> |
MaterializeKNNPreprocessor.getMaterialized()
Materialize a neighborhood. |
protected abstract List<DistanceResultPair<DoubleDistance>> |
LocalPCAPreprocessor.objectsForPCA(Integer id,
Database<V> database)
Returns the objects to be considered within the PCA for the specified query object. |
protected List<DistanceResultPair<DoubleDistance>> |
RangeQueryBasedLocalPCAPreprocessor.objectsForPCA(Integer id,
Database<V> database)
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protected List<DistanceResultPair<DoubleDistance>> |
KnnQueryBasedLocalPCAPreprocessor.objectsForPCA(Integer id,
Database<V> database)
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Method parameters in de.lmu.ifi.dbs.elki.preprocessing with type arguments of type DistanceResultPair | |
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protected void |
FourCPreprocessor.runVarianceAnalysis(Integer id,
List<DistanceResultPair<D>> neighbors,
Database<V> database)
This method implements the type of variance analysis to be computed for a given point. |
protected abstract void |
ProjectedDBSCANPreprocessor.runVarianceAnalysis(Integer id,
List<DistanceResultPair<D>> neighbors,
Database<V> database)
This method implements the type of variance analysis to be computed for a given point. |
protected void |
PreDeConPreprocessor.runVarianceAnalysis(Integer id,
List<DistanceResultPair<D>> neighbors,
Database<V> database)
TODO provide correct commentary This method implements the type of variance analysis to be computed for a given point. |
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