| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
| de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
| 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.tree.metrical.mtreevariants.mktrees.mkmax | |
| 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 |
|---|---|
private List<KNNHeap<D>> |
KNNJoin.initHeaps(SpatialPrimitiveDistanceFunction<V,D> distFunction,
N pr)
Initialize the heaps.
|
| Modifier and Type | Method and Description |
|---|---|
private D |
KNNJoin.computeStopDistance(List<KNNHeap<D>> heaps)
Compute the maximum stop distance.
|
private void |
KNNJoin.processDataPagesOptimize(SpatialPrimitiveDistanceFunction<V,D> distFunction,
List<? extends KNNHeap<D>> pr_heaps,
List<? extends KNNHeap<D>> 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.processDataPagesOptimize(SpatialPrimitiveDistanceFunction<V,D> distFunction,
List<? extends KNNHeap<D>> pr_heaps,
List<? extends KNNHeap<D>> 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 |
|---|---|
private void |
LinearScanKNNQuery.linearScanBatchKNN(ArrayDBIDs ids,
List<KNNHeap<D>> heaps)
Linear batch knn for arbitrary distance functions.
|
protected void |
LinearScanPrimitiveDistanceKNNQuery.linearScanBatchKNN(List<O> objs,
List<KNNHeap<D>> heaps)
Perform a linear scan batch kNN for primitive distance functions.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKNNHeap<P extends DistanceDBIDPair<D>,D extends Distance<D>>
Heap used for KNN management.
|
class |
DoubleDistanceKNNHeap
Heap for collecting double-valued KNN instances.
|
(package private) class |
GenericKNNHeap<D extends Distance<D>>
Heap for collecting kNN candiates with arbitrary distance types.
|
| Modifier and Type | Method and Description |
|---|---|
static <D extends Distance<D>> |
KNNUtil.newHeap(D factory,
int k)
Create an appropriate heap for the distance function.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(DistanceFunction<?,D> df,
int k)
Create an appropriate heap for the distance function.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(DistanceQuery<?,D> df,
int k)
Create an appropriate heap for the distance function.
|
static <D extends Distance<D>> |
KNNUtil.newHeap(KNNResult<D> exist)
Build a new heap from a given list.
|
| Constructor and Description |
|---|
GenericKNNList(KNNHeap<D> heap)
Constructor, to be called from KNNHeap only.
|
| Modifier and Type | Method and Description |
|---|---|
private void |
MkMaxTree.preInsert(MkMaxEntry<D> q,
MkMaxEntry<D> nodeEntry,
KNNHeap<D> knns_q)
Adapts the knn distances before insertion of entry q.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
GenericRStarTreeKNNQuery.doKNNQuery(O object,
KNNHeap<D> knnList)
Performs a k-nearest neighbor query for the given NumberVector with the
given parameter k and the according distance function.
|
private D |
GenericRStarTreeKNNQuery.expandNode(O object,
KNNHeap<D> knnList,
Heap<GenericDistanceSearchCandidate<D>> pq,
D maxDist,
int nodeID) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
GenericRStarTreeKNNQuery.batchNN(AbstractRStarTreeNode<?,?> node,
Map<DBID,KNNHeap<D>> knnLists)
Performs a batch knn query.
|