Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical | |
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
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.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
de.lmu.ifi.dbs.elki.database.query |
Database queries - computing distances, neighbors, similarities - API and general documentation.
|
de.lmu.ifi.dbs.elki.database.query.distance |
Prepared queries for distances.
|
de.lmu.ifi.dbs.elki.database.query.knn |
Prepared queries for k nearest neighbor (kNN) queries.
|
de.lmu.ifi.dbs.elki.database.query.range |
Prepared queries for ε-range queries.
|
de.lmu.ifi.dbs.elki.database.query.rknn |
Prepared queries for reverse k nearest neighbor (rkNN) queries.
|
de.lmu.ifi.dbs.elki.distance | |
de.lmu.ifi.dbs.elki.distance.distancefunction |
Distance functions for use within ELKI.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter |
Distance functions deriving distances from e.g. similarity measures
|
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
Distance functions using correlations.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.index |
Index structure implementations
|
de.lmu.ifi.dbs.elki.index.lsh |
Locality Sensitive Hashing
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.projected |
Projected indexes for data.
|
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.mtree | |
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.kd |
K-d-tree and variants.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu | |
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.rstar | |
de.lmu.ifi.dbs.elki.index.vafile |
Vector Approximation File
|
Modifier and Type | Method and Description |
---|---|
protected DistanceQuery<V,DoubleDistance> |
AbstractProjectedClustering.getDistanceQuery(Database database)
Returns the distance function.
|
Modifier and Type | Method and Description |
---|---|
private void |
ORCLUS.assign(Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
List<ORCLUS.ORCLUSCluster> clusters)
Creates a partitioning of the database by assigning each object to its
closest seed.
|
private void |
ERiC.buildHierarchy(Clustering<CorrelationModel<V>> clustering,
List<List<Cluster<CorrelationModel<V>>>> clusterMap,
DistanceQuery<V,IntegerDistance> query) |
private Matrix |
ORCLUS.findBasis(Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
ORCLUS.ORCLUSCluster cluster,
int dim)
Finds the basis of the subspace of dimensionality
dim for the
specified cluster. |
ClusteringAlgorithm<Clustering<Model>> |
COPAC.getPartitionAlgorithm(DistanceQuery<V,D> query)
Returns the partition algorithm.
|
private void |
ORCLUS.merge(Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
List<ORCLUS.ORCLUSCluster> clusters,
int k_new,
int d_new,
IndefiniteProgress cprogress)
Reduces the number of seeds to k_new
|
private ORCLUS.ProjectedEnergy |
ORCLUS.projectedEnergy(Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
ORCLUS.ORCLUSCluster c_i,
ORCLUS.ORCLUSCluster c_j,
int i,
int j,
int dim)
Computes the projected energy of the specified clusters.
|
private Clustering<Model> |
COPAC.runPartitionAlgorithm(Relation<V> relation,
Map<Integer,DBIDs> partitionMap,
DistanceQuery<V,D> query)
Runs the partition algorithm and creates the result.
|
private ORCLUS.ORCLUSCluster |
ORCLUS.union(Relation<V> relation,
DistanceQuery<V,DoubleDistance> distFunc,
ORCLUS.ORCLUSCluster c1,
ORCLUS.ORCLUSCluster c2,
int dim)
Returns the union of the two specified clusters.
|
Modifier and Type | Method and Description |
---|---|
private void |
SLINK.step2(DBIDRef id,
DBIDs processedIDs,
DistanceQuery<O,D> distFunc,
WritableDataStore<D> m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
Modifier and Type | Method and Description |
---|---|
protected boolean |
KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means,
DBIDs ids,
WritableDoubleDataStore second,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V,D> distQ)
Returns a list of clusters.
|
protected boolean |
KMedoidsEM.assignToNearestCluster(ArrayDBIDs means,
Mean[] mdist,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V,D> distQ)
Returns a list of clusters.
|
DBIDs |
RandomlyChosenInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distanceFunction) |
DBIDs |
FirstKInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distanceFunction) |
DBIDs |
KMeansPlusPlusInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distQ2) |
DBIDs |
KMedoidsInitialization.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distanceFunction)
Choose initial means
|
DBIDs |
PAMInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distQ2) |
DBIDs |
FarthestPointsInitialMeans.chooseInitialMedoids(int k,
DistanceQuery<? super V,?> distQ2) |
protected double |
KMeansPlusPlusInitialMeans.initialWeights(double[] weights,
ArrayDBIDs ids,
DBID latest,
DistanceQuery<? super V,D> distQ)
Initialize the weight list.
|
protected double |
KMeansPlusPlusInitialMeans.updateWeights(double[] weights,
ArrayDBIDs ids,
DBID latest,
DistanceQuery<? super V,D> distQ)
Update the weight list.
|
Modifier and Type | Method and Description |
---|---|
private Map<DBID,gnu.trove.set.TIntSet> |
PROCLUS.findDimensions(DBIDs medoids,
Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
RangeQuery<V,DoubleDistance> rangeQuery)
Determines the set of correlated dimensions for each medoid in the
specified medoid set.
|
private Map<DBID,DistanceDBIDList<DoubleDistance>> |
PROCLUS.getLocalities(DBIDs medoids,
Relation<V> database,
DistanceQuery<V,DoubleDistance> distFunc,
RangeQuery<V,DoubleDistance> rangeQuery)
Computes the localities of the specified medoids: for each medoid m the
objects in the sphere centered at m with radius minDist are determined,
where minDist is the minimum distance between medoid m and any other medoid
m_i.
|
private ModifiableDBIDs |
PROCLUS.greedy(DistanceQuery<V,DoubleDistance> distFunc,
DBIDs sampleSet,
int m,
Random random)
Returns a piercing set of k medoids from the specified sample set.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O,DoubleDistance> |
HilOut.distq
Distance query
|
Modifier and Type | Method and Description |
---|---|
protected DistanceDBIDList<D> |
ReferenceBasedOutlierDetection.computeDistanceVector(V refPoint,
Relation<V> database,
DistanceQuery<V,D> distFunc)
Computes for each object the distance to one reference point.
|
private void |
DWOF.initializeRadii(DBIDs ids,
KNNQuery<O,D> knnq,
DistanceQuery<O,D> distFunc,
WritableDoubleDataStore radii)
This method prepares a container for the radii of the objects and
initializes radii according to the equation:
initialRadii of a certain object = (absoluteMinDist of all objects) *
(avgDist of the object) / (minAvgDist of all objects)
|
Modifier and Type | Method and Description |
---|---|
private DoubleMinMax |
DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation,
DistanceQuery<O,D> distFunc)
Compute the exact maximum and minimum.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation,
DistanceQuery<O,D> distFunc)
Estimate minimum and maximum via sampling.
|
Modifier and Type | Method and Description |
---|---|
static <O,D extends Distance<D>> |
QueryUtil.getDistanceQuery(Database database,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get a distance query for a given distance function, automatically choosing
a relation.
|
<O,D extends Distance<D>> |
Database.getDistanceQuery(Relation<O> relation,
DistanceFunction<? super O,D> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
<O,D extends Distance<D>> |
AbstractDatabase.getDistanceQuery(Relation<O> objQuery,
DistanceFunction<? super O,D> distanceFunction,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<O,D extends Distance<D>> |
Database.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
<O,D extends Distance<D>> |
AbstractDatabase.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
static <O,D extends Distance<D>> |
QueryUtil.getLinearScanKNNQuery(DistanceQuery<O,D> distanceQuery)
Get a linear scan query for the given distance query.
|
static <O,D extends Distance<D>> |
QueryUtil.getLinearScanRangeQuery(DistanceQuery<O,D> distanceQuery)
Get a linear scan query for the given distance query.
|
<O,D extends Distance<D>> |
Database.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a range query object for the given distance query.
|
<O,D extends Distance<D>> |
AbstractDatabase.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<O,D extends Distance<D>> |
Database.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a rKNN query object for the given distance query.
|
<O,D extends Distance<D>> |
AbstractDatabase.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceSimilarityQuery<O,D extends Distance<D>>
Interface that is a combination of distance and a similarity function.
|
Modifier and Type | Interface and Description |
---|---|
interface |
SpatialDistanceQuery<V extends SpatialComparable,D extends Distance<D>>
Query interface for spatial distance queries.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabaseDistanceQuery<O,D extends Distance<D>>
Run a database query in a database context.
|
class |
AbstractDistanceQuery<O,D extends Distance<D>>
A distance query serves as adapter layer for database and primitive
distances.
|
class |
DBIDDistanceQuery<D extends Distance<D>>
Run a distance query based on DBIDs
|
class |
PrimitiveDistanceQuery<O,D extends Distance<D>>
Run a database query in a database context.
|
class |
PrimitiveDistanceSimilarityQuery<O,D extends Distance<D>>
Combination query class, for convenience.
|
class |
SpatialPrimitiveDistanceQuery<V extends SpatialComparable,D extends Distance<D>>
Distance query for spatial distance functions
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O,D> |
AbstractDistanceKNNQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractDistanceKNNQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
LinearScanDistanceKNNQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O,D> |
AbstractDistanceRangeQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractDistanceRangeQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
DoubleOptimizedDistanceRangeQuery(DistanceQuery<O,DoubleDistance> distanceQuery)
Constructor.
|
LinearScanDistanceRangeQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O,D> |
AbstractRKNNQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractRKNNQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
LinearScanRKNNQuery(DistanceQuery<O,D> distanceQuery,
KNNQuery<O,D> knnQuery,
Integer maxk)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static boolean |
DistanceUtil.isDoubleDistanceFunction(DistanceQuery<?,?> df)
Test whether a distance query is double-valued.
|
Modifier and Type | Interface and Description |
---|---|
static interface |
FilteredLocalPCABasedDistanceFunction.Instance<T extends NumberVector<?>,I extends Index,D extends Distance<D>>
Instance produced by the distance function.
|
static interface |
IndexBasedDistanceFunction.Instance<T,I extends Index,D extends Distance<D>>
Instance interface for Index based distance functions.
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractDatabaseDistanceFunction.Instance<O,D extends Distance<D>>
The actual instance bound to a particular database.
|
static class |
AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,D extends Distance<D>,F extends DistanceFunction<? super O,D>>
The actual instance bound to a particular database.
|
static class |
LocallyWeightedDistanceFunction.Instance<V extends NumberVector<?>>
Instance of this distance for a particular database.
|
class |
MinKDistance.Instance<T extends O>
Instance for an actual database.
|
static class |
SharedNearestNeighborJaccardDistanceFunction.Instance<T>
Actual instance for a dataset.
|
Modifier and Type | Field and Description |
---|---|
(package private) DistanceQuery<O,D> |
ProxyDistanceFunction.inner
Distance query
|
private DistanceQuery<T,D> |
MinKDistance.Instance.parentDistanceQuery
Distance query for parent distance.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O,D> |
ProxyDistanceFunction.getDistanceQuery()
Get the inner query
|
<O extends DBID> |
AbstractDBIDDistanceFunction.instantiate(Relation<O> database) |
<T extends O> |
MinKDistance.instantiate(Relation<T> relation) |
<T extends O> |
AbstractPrimitiveDistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends O> |
DistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
Modifier and Type | Method and Description |
---|---|
static <O,D extends Distance<D>> |
ProxyDistanceFunction.proxy(DistanceQuery<O,D> inner)
Static method version.
|
void |
ProxyDistanceFunction.setDistanceQuery(DistanceQuery<O,D> inner) |
Constructor and Description |
---|
ProxyDistanceFunction(DistanceQuery<O,D> inner)
Constructor
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractSimilarityAdapter.Instance<O>
Inner proxy class for SNN distance function.
|
static class |
ArccosSimilarityAdapter.Instance<O>
Distance function instance
|
static class |
LinearAdapterLinear.Instance<O>
Distance function instance
|
static class |
LnSimilarityAdapter.Instance<O>
Distance function instance
|
Modifier and Type | Method and Description |
---|---|
<T extends O> |
ArccosSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LnSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LinearAdapterLinear.instantiate(Relation<T> database) |
abstract <T extends O> |
AbstractSimilarityAdapter.instantiate(Relation<T> database) |
Modifier and Type | Class and Description |
---|---|
static class |
ERiCDistanceFunction.Instance<V extends NumberVector<?>>
The actual instance bound to a particular database.
|
static class |
PCABasedCorrelationDistanceFunction.Instance<V extends NumberVector<?>>
The actual instance bound to a particular database.
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance<V extends NumberVector<?>,P extends PreferenceVectorIndex<V>>
Instance to compute the distances on an actual database.
|
static class |
DiSHDistanceFunction.Instance<V extends NumberVector<?>>
The actual instance bound to a particular database.
|
static class |
HiSCDistanceFunction.Instance<V extends NumberVector<?>>
The actual instance bound to a particular database.
|
static class |
LocalSubspaceDistanceFunction.Instance<V extends NumberVector<?>>
The actual instance bound to a particular database.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
KNNIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
<D extends Distance<D>> |
RangeIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a range query object for the given distance query and k.
|
<D extends Distance<D>> |
RKNNIndex.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Constructor and Description |
---|
AbstractRefiningIndex.AbstractKNNQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
AbstractRefiningIndex.AbstractRangeQuery(DistanceQuery<O,D> distanceQuery)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
InMemoryLSHIndex.Instance.getKNNQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
InMemoryLSHIndex.Instance.getRangeQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
Constructor and Description |
---|
InMemoryLSHIndex.Instance.LSHKNNQuery(DistanceQuery<V,D> distanceQuery)
Constructor.
|
InMemoryLSHIndex.Instance.LSHRangeQuery(DistanceQuery<V,D> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O,D> |
AbstractMaterializeKNNPreprocessor.distanceQuery
The distance query we used.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O,D> |
AbstractMaterializeKNNPreprocessor.getDistanceQuery()
The distance query we used.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
AbstractMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O,S> distQ,
Object... hints) |
<S extends Distance<S>> |
MaterializeKNNAndRKNNPreprocessor.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
(package private) DistanceQuery<O,D> |
ProjectedIndex.ProjectedKNNQuery.distq
Distance query for refinement.
|
(package private) DistanceQuery<O,D> |
ProjectedIndex.ProjectedRKNNQuery.distq
Distance query for refinement.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
LatLngAsECEFIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
ProjectedIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
LngLatAsECEFIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
LatLngAsECEFIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
ProjectedIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
LngLatAsECEFIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
LatLngAsECEFIndex.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
ProjectedIndex.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
LngLatAsECEFIndex.getRKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Constructor and Description |
---|
ProjectedIndex.ProjectedKNNQuery(DistanceQuery<O,D> distanceQuery,
KNNQuery<I,D> inner)
Constructor.
|
ProjectedIndex.ProjectedRangeQuery(DistanceQuery<O,D> distanceQuery,
RangeQuery<I,D> inner)
Constructor.
|
ProjectedIndex.ProjectedRKNNQuery(DistanceQuery<O,D> distanceQuery,
RKNNQuery<I,D> inner)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O,D> |
AbstractMkTree.distanceQuery
Distance query to use.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkAppTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkAppTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkAppTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkCoPTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkCoPTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkCoPTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkMaxTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkMaxTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkMaxTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MkTabTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkTabTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MkTabTreeIndex.getRKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O,D> |
MTreeIndex.distanceQuery
The distance query.
|
Modifier and Type | Method and Description |
---|---|
<S extends Distance<S>> |
MTreeIndex.getKNNQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
<S extends Distance<S>> |
MTreeIndex.getRangeQuery(DistanceQuery<O,S> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
static <O,D extends NumberDistance<D,?>> |
MTreeQueryUtil.getKNNQuery(AbstractMTree<O,D,?,?,?> tree,
DistanceQuery<O,D> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
static <O,D extends NumberDistance<D,?>> |
MTreeQueryUtil.getRangeQuery(AbstractMTree<O,D,?,?,?> tree,
DistanceQuery<O,D> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
Constructor and Description |
---|
DoubleDistanceMetricalIndexKNNQuery(AbstractMTree<O,DoubleDistance,?,?,?> index,
DistanceQuery<O,DoubleDistance> distanceQuery,
PrimitiveDoubleDistanceFunction<? super O> distf)
Constructor.
|
DoubleDistanceMetricalIndexRangeQuery(AbstractMTree<O,DoubleDistance,?,?,?> index,
DistanceQuery<O,DoubleDistance> distanceQuery,
PrimitiveDoubleDistanceFunction<? super O> distf)
Constructor.
|
MetricalIndexKNNQuery(AbstractMTree<O,D,?,?,?> index,
DistanceQuery<O,D> distanceQuery)
Constructor.
|
MetricalIndexRangeQuery(AbstractMTree<O,D,?,?,?> index,
DistanceQuery<O,D> distanceQuery)
Constructor.
|
MkTreeRKNNQuery(AbstractMkTree<O,D,?,?,?> index,
DistanceQuery<O,D> distanceQuery)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
MinimalisticMemoryKDTree.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
MinimalisticMemoryKDTree.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Constructor and Description |
---|
MinimalisticMemoryKDTree.KDTreeKNNQuery(DistanceQuery<O,DoubleDistance> distanceQuery,
DoubleNorm<? super O> norm)
Constructor.
|
MinimalisticMemoryKDTree.KDTreeRangeQuery(DistanceQuery<O,DoubleDistance> distanceQuery,
DoubleNorm<? super O> norm)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
DeLiCluTreeIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
DeLiCluTreeIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Constructor and Description |
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DoubleDistanceRStarTreeKNNQuery(AbstractRStarTree<?,?,?> tree,
DistanceQuery<O,DoubleDistance> distanceQuery,
SpatialPrimitiveDoubleDistanceFunction<? super O> distanceFunction)
Constructor.
|
DoubleDistanceRStarTreeRangeQuery(AbstractRStarTree<?,?,?> tree,
DistanceQuery<O,DoubleDistance> distanceQuery,
SpatialPrimitiveDoubleDistanceFunction<? super O> distanceFunction)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
RStarTreeIndex.getKNNQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
RStarTreeIndex.getRangeQuery(DistanceQuery<O,D> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<D extends Distance<D>> |
PartialVAFile.getKNNQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
VAFile.getKNNQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
PartialVAFile.getRangeQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
<D extends Distance<D>> |
VAFile.getRangeQuery(DistanceQuery<V,D> distanceQuery,
Object... hints) |
Constructor and Description |
---|
PartialVAFile.PartialVAFileKNNQuery(DistanceQuery<V,DoubleDistance> ddq,
double p,
BitSet subspace)
Constructor.
|
PartialVAFile.PartialVAFileRangeQuery(DistanceQuery<V,DoubleDistance> ddq,
double p,
BitSet subspace)
Constructor.
|
VAFile.VAFileKNNQuery(DistanceQuery<V,DoubleDistance> distanceQuery,
double p)
Constructor.
|
VAFile.VAFileRangeQuery(DistanceQuery<V,DoubleDistance> distanceQuery,
double p)
Constructor.
|