Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
de.lmu.ifi.dbs.elki.database.ids |
Database object identification and ID group handling API.
|
de.lmu.ifi.dbs.elki.database.ids.integer |
Integer-based DBID implementation --
do not use directly - always use
DBIDUtil . |
de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax | |
de.lmu.ifi.dbs.elki.index.tree.spatial.kd |
K-d-tree and variants.
|
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.index.tree.spatial.rstarvariants.rdknn |
Modifier and Type | Method and Description |
---|---|
private List<KNNHeap> |
KNNJoin.initHeaps(SpatialPrimitiveDistanceFunction<V> distFunction,
N pr)
Initialize the heaps.
|
Modifier and Type | Method and Description |
---|---|
private double |
KNNJoin.computeStopDistance(List<KNNHeap> heaps)
Compute the maximum stop distance.
|
private void |
KNNJoin.processDataPages(SpatialPrimitiveDistanceFunction<? super V> df,
List<KNNHeap> pr_heaps,
List<KNNHeap> ps_heaps,
N pr,
N ps)
Processes the two data pages pr and ps and determines the k-nearest
neighbors of pr in ps.
|
private void |
KNNJoin.processDataPages(SpatialPrimitiveDistanceFunction<? super V> df,
List<KNNHeap> pr_heaps,
List<KNNHeap> ps_heaps,
N pr,
N ps)
Processes the two data pages pr and ps and determines the k-nearest
neighbors of pr in ps.
|
Modifier and Type | Method and Description |
---|---|
static KNNHeap |
DBIDUtil.newHeap(int k)
Create an appropriate heap for the distance type.
|
KNNHeap |
DBIDFactory.newHeap(int k)
Create an heap for kNN search.
|
static KNNHeap |
DBIDUtil.newHeap(KNNList exist)
Build a new heap from a given list.
|
KNNHeap |
DBIDFactory.newHeap(KNNList exist)
Build a new heap from a given list.
|
Modifier and Type | Class and Description |
---|---|
(package private) class |
DoubleIntegerDBIDKNNHeap
Class to efficiently manage a kNN heap.
|
(package private) class |
DoubleIntegerDBIDListKNNHeap
Track the k nearest neighbors, with insertion sort to ensure the correct
order.
|
(package private) class |
DoubleIntegerDBIDPairKNNListHeap
KNN Heap implemented using a list of DoubleInt pair objects.
|
Modifier and Type | Method and Description |
---|---|
KNNHeap |
AbstractIntegerDBIDFactory.newHeap(int k) |
KNNHeap |
AbstractIntegerDBIDFactory.newHeap(KNNList exist) |
Modifier and Type | Method and Description |
---|---|
private KNNHeap |
LinearScanPrimitiveDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
private KNNHeap |
LinearScanEuclideanDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
Modifier and Type | Method and Description |
---|---|
private KNNHeap |
LinearScanPrimitiveDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
private KNNHeap |
LinearScanEuclideanDistanceKNNQuery.linearScan(Relation<? extends O> relation,
DBIDIter iter,
O obj,
KNNHeap heap)
Main loop of the linear scan.
|
Modifier and Type | Method and Description |
---|---|
private void |
LinearScanDistanceKNNQuery.linearScanBatchKNN(ArrayDBIDs ids,
List<KNNHeap> heaps)
Linear batch knn for arbitrary distance functions.
|
protected void |
LinearScanPrimitiveDistanceKNNQuery.linearScanBatchKNN(List<O> objs,
List<KNNHeap> heaps)
Perform a linear scan batch kNN for primitive distance functions.
|
protected void |
LinearScanEuclideanDistanceKNNQuery.linearScanBatchKNN(List<O> objs,
List<KNNHeap> heaps)
Perform a linear scan batch kNN for primitive distance functions.
|
Modifier and Type | Method and Description |
---|---|
private void |
MkMaxTree.preInsert(MkMaxEntry q,
MkMaxEntry nodeEntry,
KNNHeap knns_q)
Adapts the knn distances before insertion of entry q.
|
Modifier and Type | Method and Description |
---|---|
private double |
MinimalisticMemoryKDTree.KDTreeKNNQuery.kdKNNSearch(int left,
int right,
int axis,
O query,
KNNHeap knns,
DBIDArrayIter iter,
double maxdist)
Perform a kNN search on the kd-tree.
|
private double |
SmallMemoryKDTree.KDTreeKNNQuery.kdKNNSearch(int left,
int right,
int axis,
O query,
KNNHeap knns,
DoubleDBIDListIter iter,
double maxdist)
Perform a kNN search on the kd-tree.
|
Modifier and Type | Method and Description |
---|---|
private double |
RStarTreeKNNQuery.expandNode(O object,
KNNHeap knnList,
ComparableMinHeap<DoubleDistanceSearchCandidate> pq,
double maxDist,
int nodeID) |
private double |
EuclideanRStarTreeKNNQuery.expandNode(O object,
KNNHeap knnList,
ComparableMinHeap<DoubleDistanceSearchCandidate> pq,
double maxDist,
int nodeID) |
Modifier and Type | Method and Description |
---|---|
protected void |
RStarTreeKNNQuery.batchNN(AbstractRStarTreeNode<?,?> node,
Map<DBID,KNNHeap> knnLists)
Performs a batch knn query.
|
Modifier and Type | Method and Description |
---|---|
private void |
RdKNNTree.preInsert(RdKNNEntry q,
RdKNNEntry nodeEntry,
KNNHeap knns_q)
Adapts the knn distances before insertion of entry q.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.