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.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
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.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.localpca |
Index using a preprocessed local PCA.
|
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.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.
|
Modifier and Type | Method and Description |
---|---|
WritableDataStore<KNNResult<D>> |
KNNJoin.run(Database database,
Relation<V> relation)
Joins in the given spatial database to each object its k-nearest neighbors.
|
Modifier and Type | Method and Description |
---|---|
private void |
DeLiClu.expandLeafNodes(SpatialPrimitiveDistanceFunction<NV,D> distFunction,
DeLiCluNode node1,
DeLiCluNode node2,
DataStore<KNNResult<D>> knns)
Expands the specified leaf nodes.
|
private void |
DeLiClu.expandNodes(DeLiCluTree index,
SpatialPrimitiveDistanceFunction<NV,D> distFunction,
DeLiClu.SpatialObjectPair nodePair,
DataStore<KNNResult<D>> knns)
Expands the spatial nodes of the specified pair.
|
private void |
DeLiClu.reinsertExpanded(SpatialPrimitiveDistanceFunction<NV,D> distFunction,
DeLiCluTree index,
List<TreeIndexPathComponent<DeLiCluEntry>> path,
DataStore<KNNResult<D>> knns)
Reinserts the objects of the already expanded nodes.
|
private void |
DeLiClu.reinsertExpanded(SpatialPrimitiveDistanceFunction<NV,D> distFunction,
DeLiCluTree index,
List<TreeIndexPathComponent<DeLiCluEntry>> path,
int pos,
SpatialDirectoryEntry parentEntry,
DataStore<KNNResult<D>> knns) |
Modifier and Type | Class and Description |
---|---|
class |
PreprocessorKNNQuery<O,D extends Distance<D>,T extends KNNResult<D>>
Instance for a particular database, invoking the preprocessor.
|
Modifier and Type | Method and Description |
---|---|
KNNResult<D> |
KNNQuery.getKNNForDBID(DBIDRef id,
int k)
Get the k nearest neighbors for a particular id.
|
KNNResult<DoubleDistance> |
LinearScanRawDoubleDistanceKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<D> |
PreprocessorKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<D> |
LinearScanKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
abstract KNNResult<D> |
AbstractDistanceKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<D> |
LinearScanPrimitiveDistanceKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<D> |
KNNQuery.getKNNForObject(O obj,
int k)
Get the k nearest neighbors for a particular id.
|
KNNResult<DoubleDistance> |
LinearScanRawDoubleDistanceKNNQuery.getKNNForObject(O obj,
int k) |
KNNResult<D> |
PreprocessorKNNQuery.getKNNForObject(O obj,
int k) |
KNNResult<D> |
LinearScanKNNQuery.getKNNForObject(O obj,
int k) |
abstract KNNResult<D> |
AbstractDistanceKNNQuery.getKNNForObject(O obj,
int k) |
(package private) KNNResult<DoubleDistance> |
LinearScanRawDoubleDistanceKNNQuery.getKNNForObjectBenchmarked(O obj,
int k)
It does not make sense, but this version is faster in our larger
benchmarks.
|
(package private) KNNResult<DoubleDistance> |
LinearScanRawDoubleDistanceKNNQuery.getKNNForObjectClean(O obj,
int k)
This is the cleaner, supposedly faster implementation.
|
Modifier and Type | Method and Description |
---|---|
List<? extends KNNResult<D>> |
KNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k)
Bulk query method
|
List<KNNResult<D>> |
PreprocessorKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNResult<D>> |
LinearScanKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNResult<D>> |
LinearScanPrimitiveDistanceKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
protected D |
MinKDistance.computeReachdist(KNNResult<D> neighborhood,
D truedist)
Actually compute the distance, whichever way we obtained the neighborhood
above.
|
Modifier and Type | Class and Description |
---|---|
class |
DoubleDistanceKNNList
Finalized KNN List.
|
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 | Field and Description |
---|---|
private KNNResult<D> |
KNNUtil.KNNSubList.inner
Wrapped inner result.
|
(package private) KNNResult<D> |
KNNUtil.DistanceView.parent
The true list.
|
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
KNNUtil.subList(KNNResult<D> list,
int k)
Get a subset of the KNN result.
|
KNNResult<D> |
KNNHeap.toKNNList()
Serialize to a
GenericKNNList . |
Modifier and Type | Method and Description |
---|---|
static <D extends Distance<D>> |
KNNUtil.asDistanceList(KNNResult<D> list)
View as list of distances.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(KNNResult<D> exist)
Build a new heap from a given list.
|
static <D extends Distance<D>> |
KNNUtil.subList(KNNResult<D> list,
int k)
Get a subset of the KNN result.
|
Constructor and Description |
---|
KNNUtil.DistanceView(KNNResult<D> parent)
Constructor.
|
KNNUtil.KNNSubList(KNNResult<D> inner,
int k)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNResult<D> |
AbstractRefiningIndex.AbstractKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
Modifier and Type | Method and Description |
---|---|
List<KNNResult<D>> |
AbstractRefiningIndex.AbstractKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Class and Description |
---|---|
class |
AbstractMaterializeKNNPreprocessor<O,D extends Distance<D>,T extends KNNResult<D>>
Abstract base class for KNN Preprocessors.
|
static class |
AbstractMaterializeKNNPreprocessor.Factory<O,D extends Distance<D>,T extends KNNResult<D>>
The parameterizable factory.
|
Modifier and Type | Method and Description |
---|---|
KNNResult<D> |
AbstractMaterializeKNNPreprocessor.get(DBIDRef id)
Get the k nearest neighbors.
|
KNNResult<D> |
MaterializeKNNAndRKNNPreprocessor.getKNN(DBID id)
Returns the materialized kNNs of the specified id.
|
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.
|
Modifier and Type | Method and Description |
---|---|
protected KNNResult<DoubleDistance> |
KNNQueryFilteredPCAIndex.objectsForPCA(DBIDRef id) |
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 |
---|---|
protected abstract void |
AbstractMkTreeUnified.kNNdistanceAdjustment(E entry,
Map<DBID,KNNResult<D>> knnLists)
Performs a distance adjustment in the subtree of the specified root entry.
|
Modifier and Type | Method and Description |
---|---|
private void |
MkAppTree.adjustApproximatedKNNDistances(MkAppEntry<D> entry,
Map<DBID,KNNResult<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
private List<D> |
MkAppTree.getMeanKNNList(DBIDs ids,
Map<DBID,KNNResult<D>> knnLists) |
Modifier and Type | Method and Description |
---|---|
private void |
MkCoPTree.approximateKnnDistances(MkCoPLeafEntry<D> entry,
KNNResult<D> knnDistances)
Computes logarithmic skew (fractal dimension ie. m) and in kappx[0] and
kappx[1] the non-logarithmic values of the approximated first and last
nearest neighbor distances
|
Modifier and Type | Method and Description |
---|---|
private void |
MkCoPTree.adjustApproximatedKNNDistances(MkCoPEntry<D> entry,
Map<DBID,KNNResult<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
Modifier and Type | Method and Description |
---|---|
protected void |
MkMaxTree.kNNdistanceAdjustment(MkMaxEntry<D> entry,
Map<DBID,KNNResult<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
Modifier and Type | Method and Description |
---|---|
protected void |
MkTabTree.kNNdistanceAdjustment(MkTabEntry<D> entry,
Map<DBID,KNNResult<D>> knnLists) |
Modifier and Type | Method and Description |
---|---|
KNNResult<D> |
MetricalIndexKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<DoubleDistance> |
DoubleDistanceMetricalIndexKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<D> |
MetricalIndexKNNQuery.getKNNForObject(O q,
int k) |
KNNResult<DoubleDistance> |
DoubleDistanceMetricalIndexKNNQuery.getKNNForObject(O q,
int k) |
Modifier and Type | Method and Description |
---|---|
List<KNNResult<D>> |
MetricalIndexKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
List<KNNResult<DoubleDistance>> |
DoubleDistanceMetricalIndexKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |
Modifier and Type | Method and Description |
---|---|
KNNResult<D> |
GenericRStarTreeKNNQuery.getKNNForDBID(DBIDRef id,
int k) |
KNNResult<D> |
GenericRStarTreeKNNQuery.getKNNForObject(O obj,
int k) |
Modifier and Type | Method and Description |
---|---|
List<KNNResult<D>> |
GenericRStarTreeKNNQuery.getKNNForBulkDBIDs(ArrayDBIDs ids,
int k) |