Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted |
Weighted Neighborhood definitions.
|
de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms.
|
de.lmu.ifi.dbs.elki.database.ids |
Database object identification and ID group handling API.
|
de.lmu.ifi.dbs.elki.database.ids.generic |
Database object identification and ID group handling - generic implementations.
|
de.lmu.ifi.dbs.elki.database.ids.integer |
Integer-based DBID implementation --
do not use directly - always use
DBIDUtil . |
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
Modifier and Type | Field and Description |
---|---|
ObjectHeap<DoubleDBIDPair> |
HilOut.HilFeature.nn
Heap with the nearest known neighbors
|
Constructor and Description |
---|
HilOut.HilFeature(DBID id,
ObjectHeap<DoubleDBIDPair> nn)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
Collection<DoubleDBIDPair> |
WeightedNeighborSetPredicate.getWeightedNeighbors(DBIDRef reference)
Get the neighbors of a reference object for DBSCAN.
|
Collection<DoubleDBIDPair> |
UnweightedNeighborhoodAdapter.getWeightedNeighbors(DBIDRef reference) |
Collection<DoubleDBIDPair> |
LinearWeightedExtendedNeighborhood.getWeightedNeighbors(DBIDRef reference) |
Modifier and Type | Method and Description |
---|---|
private static void |
DistanceStatisticsWithClasses.shrinkHeap(TreeSet<DoubleDBIDPair> hotset,
int k)
Shrink the heap of "hot" (extreme) items.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDPair |
KNNList.get(int index)
Direct object access.
|
DoubleDBIDPair |
DoubleDBIDList.get(int off)
Materialize a single pair.
|
DoubleDBIDPair |
DoubleDBIDListIter.getPair()
Materialize an object pair.
|
static DoubleDBIDPair |
DBIDUtil.newPair(double val,
DBIDRef id)
Make a DoubleDBIDPair.
|
DoubleDBIDPair |
DBIDFactory.newPair(double val,
DBIDRef id)
Make a double-DBID pair.
|
DoubleDBIDPair |
KNNHeap.peek()
Peek at the largest element in the heap.
|
DoubleDBIDPair |
KNNHeap.poll()
Poll the largest element from the heap.
|
Modifier and Type | Method and Description |
---|---|
void |
ModifiableDoubleDBIDList.add(DoubleDBIDPair pair)
Add an element.
|
void |
KNNHeap.insert(DoubleDBIDPair e)
Add a distance-id pair to the heap unless the distance is too large.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDPair |
KNNSubList.get(int index) |
DoubleDBIDPair |
KNNSubList.Itr.getPair() |
Modifier and Type | Class and Description |
---|---|
(package private) class |
DoubleIntegerDBIDPair
Pair containing a double value and an integer DBID.
|
Modifier and Type | Method and Description |
---|---|
DoubleDBIDPair |
IntegerDBIDKNNSubList.Itr.getPair() |
DoubleDBIDPair |
DoubleIntegerDBIDPairList.Itr.getPair() |
DoubleDBIDPair |
DoubleIntegerDBIDList.Itr.getPair() |
DoubleDBIDPair |
AbstractIntegerDBIDFactory.newPair(double val,
DBIDRef id) |
Modifier and Type | Method and Description |
---|---|
void |
DoubleIntegerDBIDPairList.add(DoubleDBIDPair pair) |
void |
DoubleIntegerDBIDList.add(DoubleDBIDPair pair) |
int |
DoubleIntegerDBIDPair.compareTo(DoubleDBIDPair o) |
void |
DoubleIntegerDBIDPairKNNListHeap.insert(DoubleDBIDPair e) |
void |
DoubleIntegerDBIDListKNNHeap.insert(DoubleDBIDPair e) |
void |
DoubleIntegerDBIDKNNHeap.insert(DoubleDBIDPair e) |
Modifier and Type | Field and Description |
---|---|
private WritableDataStore<TreeSet<DoubleDBIDPair>> |
MaterializeKNNAndRKNNPreprocessor.materialized_RkNN
Additional data storage for RkNN.
|
Modifier and Type | Method and Description |
---|---|
private DoubleDBIDPair |
MaterializeKNNAndRKNNPreprocessor.makePair(DoubleDBIDListIter iter,
DBIDIter id) |
Modifier and Type | Method and Description |
---|---|
protected ArrayDBIDs |
MaterializeKNNAndRKNNPreprocessor.affectedRkNN(List<? extends Collection<DoubleDBIDPair>> extract,
DBIDs remove)
Extracts and removes the DBIDs in the given collections.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.