Modifier and Type | Method and Description |
---|---|
protected DistanceQuery<V> |
AbstractProjectedClustering.getDistanceQuery(Database database)
Returns the distance function.
|
Modifier and Type | Method and Description |
---|---|
private void |
ORCLUS.assign(Relation<V> database,
DistanceQuery<V> distFunc,
List<ORCLUS.ORCLUSCluster> clusters)
Creates a partitioning of the database by assigning each object to its
closest seed.
|
private Matrix |
ORCLUS.findBasis(Relation<V> database,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster cluster,
int dim)
Finds the basis of the subspace of dimensionality
dim for the
specified cluster. |
private void |
ORCLUS.merge(Relation<V> database,
DistanceQuery<V> 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> distFunc,
ORCLUS.ORCLUSCluster c_i,
ORCLUS.ORCLUSCluster c_j,
int i,
int j,
int dim)
Computes the projected energy of the specified clusters.
|
private ORCLUS.ORCLUSCluster |
ORCLUS.union(Relation<V> relation,
DistanceQuery<V> distFunc,
ORCLUS.ORCLUSCluster c1,
ORCLUS.ORCLUSCluster c2,
int dim)
Returns the union of the two specified clusters.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<?> |
AbstractHDBSCAN.HDBSCANAdapter.distq
Distance query for exact distances.
|
Modifier and Type | Method and Description |
---|---|
protected static <O> void |
AGNES.initializeDistanceMatrix(double[] scratch,
DistanceQuery<O> dq,
DBIDArrayIter ix,
DBIDArrayIter iy,
boolean square)
Initialize a distance matrix.
|
private void |
SLINK.step2(DBIDRef id,
DBIDArrayIter it,
int n,
DistanceQuery<? super O> distQuery,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
private void |
SLINKHDBSCANLinearMemory.step2(DBIDRef id,
DBIDs processedIDs,
DistanceQuery<? super O> distQuery,
DoubleDataStore coredists,
WritableDoubleDataStore m)
Second step: Determine the pairwise distances from all objects in the
pointer representation to the new object with the specified id.
|
Constructor and Description |
---|
AbstractHDBSCAN.HDBSCANAdapter(ArrayDBIDs ids,
DoubleDataStore coredists,
DistanceQuery<?> distq)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
protected double |
CLARA.assignRemainingToNearestCluster(ArrayDBIDs means,
DBIDs ids,
DBIDs rids,
WritableIntegerDataStore assignment,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected double |
KMedoidsPAM.assignToNearestCluster(ArrayDBIDs means,
DBIDs ids,
WritableDoubleDataStore nearest,
WritableDoubleDataStore second,
WritableIntegerDataStore assignment,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected boolean |
KMedoidsEM.assignToNearestCluster(ArrayDBIDs means,
Mean[] mdist,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
protected void |
KMedoidsPAM.runPAMOptimization(DistanceQuery<V> distQ,
DBIDs ids,
ArrayModifiableDBIDs medoids,
WritableIntegerDataStore assignment)
Run the PAM optimization phase.
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
RandomlyChosenInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distanceFunction) |
DBIDs |
PAMInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
KMeansPlusPlusInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
FirstKInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distanceFunction) |
DBIDs |
FarthestSumPointsInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
FarthestPointsInitialMeans.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super O> distQ) |
DBIDs |
KMedoidsInitialization.chooseInitialMedoids(int k,
DBIDs ids,
DistanceQuery<? super V> distanceFunction)
Choose initial means
|
protected <T> double |
KMeansPlusPlusInitialMeans.initialWeights(WritableDoubleDataStore weights,
DBIDs ids,
T latest,
DistanceQuery<? super T> distQ)
Initialize the weight list.
|
protected <T> double |
KMeansPlusPlusInitialMeans.updateWeights(WritableDoubleDataStore weights,
DBIDs ids,
T latest,
DistanceQuery<? super T> distQ)
Update the weight list.
|
Modifier and Type | Method and Description |
---|---|
protected void |
FastOPTICS.expandClusterOrder(DBID ipt,
ClusterOrder order,
DistanceQuery<V> dq,
FiniteProgress prog)
OPTICS algorithm for processing a point, but with different density
estimates
|
Modifier and Type | Method and Description |
---|---|
private long[][] |
PROCLUS.findDimensions(ArrayDBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> rangeQuery)
Determines the set of correlated dimensions for each medoid in the
specified medoid set.
|
private DataStore<DoubleDBIDList> |
PROCLUS.getLocalities(DBIDs medoids,
Relation<V> database,
DistanceQuery<V> distFunc,
RangeQuery<V> 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 ArrayDBIDs |
PROCLUS.greedy(DistanceQuery<V> distFunc,
DBIDs sampleSet,
int m,
Random random)
Returns a piercing set of k medoids from the specified sample set.
|
Modifier and Type | Method and Description |
---|---|
private void |
DWOF.initializeRadii(DBIDs ids,
KNNQuery<O> knnq,
DistanceQuery<O> 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 | Field and Description |
---|---|
private DistanceQuery<O> |
HilOut.distq
Distance query
|
Modifier and Type | Method and Description |
---|---|
protected void |
COF.computeAverageChainingDistances(KNNQuery<O> knnq,
DistanceQuery<O> dq,
DBIDs ids,
WritableDoubleDataStore acds)
Computes the average chaining distance, the average length of a path
through the given set of points to each target.
|
Modifier and Type | Method and Description |
---|---|
private void |
EvaluateRetrievalPerformance.computeDistances(ModifiableDoubleDBIDList nlist,
DBIDIter query,
DistanceQuery<O> distQuery,
Relation<O> relation)
Compute the distances to the neighbor objects.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Compute the exact maximum and minimum.
|
private DoubleMinMax |
DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation,
DistanceQuery<O> distFunc)
Estimate minimum and maximum via sampling.
|
Modifier and Type | Method and Description |
---|---|
static <O> DistanceQuery<O> |
QueryUtil.getDistanceQuery(Database database,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a distance query for a given distance function, automatically choosing
a relation.
|
<O> DistanceQuery<O> |
Database.getDistanceQuery(Relation<O> relation,
DistanceFunction<? super O> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
<O> DistanceQuery<O> |
AbstractDatabase.getDistanceQuery(Relation<O> objQuery,
DistanceFunction<? super O> distanceFunction,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
<O> KNNQuery<O> |
Database.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
<O> KNNQuery<O> |
AbstractDatabase.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
static <O> KNNQuery<O> |
QueryUtil.getLinearScanKNNQuery(DistanceQuery<O> distanceQuery)
Get a linear scan query for the given distance query.
|
static <O> RangeQuery<O> |
QueryUtil.getLinearScanRangeQuery(DistanceQuery<O> distanceQuery)
Get a linear scan query for the given distance query.
|
<O> RangeQuery<O> |
Database.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a range query object for the given distance query for radius-based
neighbor search.
|
<O> RangeQuery<O> |
AbstractDatabase.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
<O> RKNNQuery<O> |
Database.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a rKNN query object for the given distance query.
|
<O> RKNNQuery<O> |
AbstractDatabase.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceSimilarityQuery<O>
Interface that is a combination of distance and a similarity function.
|
Modifier and Type | Interface and Description |
---|---|
interface |
SpatialDistanceQuery<V extends SpatialComparable>
Query interface for spatial distance queries.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDatabaseDistanceQuery<O>
Run a database query in a database context.
|
class |
AbstractDistanceQuery<O>
A distance query serves as adapter layer for database and primitive
distances.
|
class |
DBIDDistanceQuery
Run a distance query based on DBIDs
|
class |
DBIDRangeDistanceQuery
Run a distance query based on DBIDRanges
|
class |
PrimitiveDistanceQuery<O>
Run a database query in a database context.
|
class |
PrimitiveDistanceSimilarityQuery<O>
Combination query class, for convenience.
|
class |
SpatialPrimitiveDistanceQuery<V extends SpatialComparable>
Distance query for spatial distance functions
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractDistanceKNNQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractDistanceKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
LinearScanDistanceKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractDistanceRangeQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractDistanceRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
LinearScanDistanceRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractRKNNQuery.distanceQuery
Hold the distance function to be used.
|
Constructor and Description |
---|
AbstractRKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
LinearScanRKNNQuery(DistanceQuery<O> distanceQuery,
KNNQuery<O> knnQuery,
Integer maxk)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
Relation.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints)
Get the distance query for a particular distance function.
|
DistanceQuery<O> |
AbstractRelation.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
Relation.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query.
|
KNNQuery<O> |
AbstractRelation.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
Relation.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a range query object for the given distance query.
|
RangeQuery<O> |
AbstractRelation.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
Relation.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a rKNN query object for the given distance query.
|
RKNNQuery<O> |
AbstractRelation.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Interface and Description |
---|---|
static interface |
IndexBasedDistanceFunction.Instance<T,I extends Index>
Instance interface for Index based distance functions.
|
Modifier and Type | Class and Description |
---|---|
static class |
AbstractDatabaseDistanceFunction.Instance<O>
The actual instance bound to a particular database.
|
static class |
AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,F extends DistanceFunction<? super O>>
The actual instance bound to a particular database.
|
static class |
SharedNearestNeighborJaccardDistanceFunction.Instance<T>
Actual instance for a dataset.
|
Modifier and Type | Method and Description |
---|---|
<O extends DBID> |
AbstractDBIDRangeDistanceFunction.instantiate(Relation<O> database) |
<T extends DBID> |
RandomStableDistanceFunction.instantiate(Relation<T> relation) |
<T extends O> |
DistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
<T extends O> |
AbstractPrimitiveDistanceFunction.instantiate(Relation<T> relation)
Instantiate with a database to get the actual distance query.
|
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> |
LnSimilarityAdapter.instantiate(Relation<T> database) |
<T extends O> |
LinearAdapterLinear.instantiate(Relation<T> database) |
<T extends O> |
ArccosSimilarityAdapter.instantiate(Relation<T> database) |
abstract <T extends O> |
AbstractSimilarityAdapter.instantiate(Relation<T> database) |
Modifier and Type | Method and Description |
---|---|
<O extends DBID> |
FileBasedFloatDistanceFunction.instantiate(Relation<O> database) |
<O extends DBID> |
FileBasedDoubleDistanceFunction.instantiate(Relation<O> database) |
Modifier and Type | Method and Description |
---|---|
double |
EvaluateCIndex.evaluateClustering(Database db,
Relation<? extends O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
double |
EvaluateSilhouette.evaluateClustering(Database db,
Relation<O> rel,
DistanceQuery<O> dq,
Clustering<?> c)
Evaluate a single clustering.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
DistanceIndex.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
KNNIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
RangeQuery<O> |
RangeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a range query object for the given distance query and k.
|
RKNNQuery<O> |
RKNNIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints)
Get a KNN query object for the given distance query and k.
|
Constructor and Description |
---|
AbstractRefiningIndex.AbstractKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
AbstractRefiningIndex.AbstractRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
private class |
PrecomputedDistanceMatrix.PrecomputedDistanceQuery
Distance query using the precomputed matrix.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
PrecomputedDistanceMatrix.distanceQuery
Nested distance query.
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
PrecomputedDistanceMatrix.getDistanceQuery(DistanceFunction<? super O> distanceFunction,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
InMemoryIDistanceIndex.distanceQuery
Distance query.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
InMemoryIDistanceIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
InMemoryIDistanceIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
protected static <O> DoubleIntPair[] |
InMemoryIDistanceIndex.rankReferencePoints(DistanceQuery<O> distanceQuery,
O obj,
ArrayDBIDs referencepoints)
Sort the reference points by distance to the query object
|
Constructor and Description |
---|
InMemoryIDistanceIndex.IDistanceKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
InMemoryIDistanceIndex.IDistanceRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
InMemoryIDistanceIndex(Relation<O> relation,
DistanceQuery<O> distance,
KMedoidsInitialization<O> initialization,
int numref)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
InMemoryInvertedIndex.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
InMemoryInvertedIndex.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Constructor and Description |
---|
InMemoryInvertedIndex.ArcCosineKNNQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
InMemoryInvertedIndex.ArcCosineRangeQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
InMemoryInvertedIndex.CosineKNNQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
InMemoryInvertedIndex.CosineRangeQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
InMemoryLSHIndex.Instance.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
InMemoryLSHIndex.Instance.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Constructor and Description |
---|
InMemoryLSHIndex.Instance.LSHKNNQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
InMemoryLSHIndex.Instance.LSHRangeQuery(DistanceQuery<V> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
AbstractMaterializeKNNPreprocessor.distanceQuery
The distance query we used.
|
(package private) DistanceQuery<O> |
SpacefillingKNNPreprocessor.SpaceFillingKNNQuery.distq
Distance query to use for refinement
|
(package private) DistanceQuery<O> |
NaiveProjectedKNNPreprocessor.NaiveProjectedKNNQuery.distq
Distance query to use for refinement
|
Modifier and Type | Method and Description |
---|---|
DistanceQuery<O> |
AbstractMaterializeKNNPreprocessor.getDistanceQuery()
The distance query we used.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SpacefillingMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O> distQ,
Object... hints) |
KNNQuery<O> |
SpacefillingKNNPreprocessor.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
NaiveProjectedKNNPreprocessor.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
AbstractMaterializeKNNPreprocessor.getKNNQuery(DistanceQuery<O> distQ,
Object... hints) |
RKNNQuery<O> |
MaterializeKNNAndRKNNPreprocessor.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
NaiveProjectedKNNPreprocessor.NaiveProjectedKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
SpacefillingKNNPreprocessor.SpaceFillingKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
(package private) DistanceQuery<O> |
ProjectedIndex.ProjectedKNNQuery.distq
Distance query for refinement.
|
(package private) DistanceQuery<O> |
ProjectedIndex.ProjectedRKNNQuery.distq
Distance query for refinement.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
ProjectedIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
LngLatAsECEFIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
LatLngAsECEFIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
ProjectedIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
LngLatAsECEFIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
LatLngAsECEFIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
ProjectedIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
LngLatAsECEFIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
LatLngAsECEFIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
ProjectedIndex.ProjectedKNNQuery(DistanceQuery<O> distanceQuery,
KNNQuery<I> inner)
Constructor.
|
ProjectedIndex.ProjectedRangeQuery(DistanceQuery<O> distanceQuery,
RangeQuery<I> inner)
Constructor.
|
ProjectedIndex.ProjectedRKNNQuery(DistanceQuery<O> distanceQuery,
RKNNQuery<I> inner)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
AbstractCoverTree.distanceQuery
Distance query, on the data relation.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SimplifiedCoverTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
CoverTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
SimplifiedCoverTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
CoverTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
CoverTree.CoverTreeKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
CoverTree.CoverTreeRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
SimplifiedCoverTree.CoverTreeKNNQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
SimplifiedCoverTree.CoverTreeRangeQuery(DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private DistanceQuery<O> |
AbstractMkTree.distanceQuery
Distance query to use.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkAppTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkAppTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkAppTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkCoPTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkCoPTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkCoPTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkMaxTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkMaxTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkMaxTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MkTabTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MkTabTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
MkTabTreeIndex.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Field and Description |
---|---|
protected DistanceQuery<O> |
MTreeIndex.distanceQuery
The distance query.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
MTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
MTreeQueryUtil.getKNNQuery(AbstractMTree<O,?,?,?> tree,
DistanceQuery<O> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
static <O> RangeQuery<O> |
MTreeQueryUtil.getRangeQuery(AbstractMTree<O,?,?,?> tree,
DistanceQuery<O> distanceQuery,
Object... hints)
Get an RTree knn query, using an optimized double implementation when
possible.
|
Constructor and Description |
---|
MetricalIndexKNNQuery(AbstractMTree<O,?,?,?> index,
DistanceQuery<O> distanceQuery)
Constructor.
|
MetricalIndexRangeQuery(AbstractMTree<O,?,?,?> index,
DistanceQuery<O> distanceQuery)
Constructor.
|
MkTreeRKNNQuery(AbstractMkTree<O,?,?,?> index,
DistanceQuery<O> distanceQuery)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
SmallMemoryKDTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
KNNQuery<O> |
MinimalisticMemoryKDTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
SmallMemoryKDTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
MinimalisticMemoryKDTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Constructor and Description |
---|
MinimalisticMemoryKDTree.KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
MinimalisticMemoryKDTree.KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
SmallMemoryKDTree.KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
SmallMemoryKDTree.KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
DeLiCluTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
DeLiCluTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
FlatRStarTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
FlatRStarTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
RdKNNTree.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
RdKNNTree.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RKNNQuery<O> |
RdKNNTree.getRKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<O> |
RStarTreeIndex.getKNNQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
RangeQuery<O> |
RStarTreeIndex.getRangeQuery(DistanceQuery<O> distanceQuery,
Object... hints) |
Modifier and Type | Method and Description |
---|---|
KNNQuery<V> |
VAFile.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
KNNQuery<V> |
PartialVAFile.getKNNQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
VAFile.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
RangeQuery<V> |
PartialVAFile.getRangeQuery(DistanceQuery<V> distanceQuery,
Object... hints) |
Constructor and Description |
---|
PartialVAFile.PartialVAFileKNNQuery(DistanceQuery<V> ddq,
double p,
long[] subspace)
Constructor.
|
PartialVAFile.PartialVAFileRangeQuery(DistanceQuery<V> ddq,
double p,
long[] subspace)
Constructor.
|
VAFile.VAFileKNNQuery(DistanceQuery<V> distanceQuery,
double p)
Constructor.
|
VAFile.VAFileRangeQuery(DistanceQuery<V> distanceQuery,
double p)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static <O> KNNQuery<O> |
DatabaseUtil.precomputedKNNQuery(Database database,
Relation<O> relation,
DistanceQuery<O> dq,
int k)
Get (or create) a precomputed kNN query for the database.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.