Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms
Clustering algorithms are supposed to implement the
Algorithm -Interface. |
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
Correlation clustering algorithms
|
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash |
Helper classes for the
CASH algorithm. |
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.clustering.subspace.clique |
Helper classes for the
CLIQUE algorithm. |
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial |
Trivial clustering algorithms: all in one, no clusters, label clusterings
These methods are mostly useful for providing a reference result in evaluation.
|
de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood |
Spatial outlier neighborhood classes
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted |
Weighted Neighborhood definitions.
|
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.application.jsmap |
JavaScript based map client - server architecture.
|
de.lmu.ifi.dbs.elki.application.visualization |
Visualization applications in ELKI.
|
de.lmu.ifi.dbs.elki.data.type |
Data type information, also used for type restrictions.
|
de.lmu.ifi.dbs.elki.database |
ELKI database layer - loading, storing, indexing and accessing data
|
de.lmu.ifi.dbs.elki.database.datastore |
General data store layer API (along the lines of
Map<DBID, T> - use everywhere!) |
de.lmu.ifi.dbs.elki.database.datastore.memory |
Memory data store implementation for ELKI.
|
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.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.database.query.similarity |
Prepared queries for similarity functions.
|
de.lmu.ifi.dbs.elki.database.relation |
Relations, materialized and virtual (views).
|
de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types.
|
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.external |
Distance functions using external data sources.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
|
de.lmu.ifi.dbs.elki.distance.similarityfunction |
Similarity functions.
|
de.lmu.ifi.dbs.elki.evaluation.roc |
Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
|
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix |
Render a distance matrix to visualize a clustering-distance-combination.
|
de.lmu.ifi.dbs.elki.index |
Index structure implementations
|
de.lmu.ifi.dbs.elki.index.preprocessed |
Index structure based on preprocessors
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
|
de.lmu.ifi.dbs.elki.index.preprocessed.preference |
Indexes storing preference vectors.
|
de.lmu.ifi.dbs.elki.index.preprocessed.snn |
Indexes providing nearest neighbor sets
|
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj |
Index using a preprocessed local subspaces.
|
de.lmu.ifi.dbs.elki.index.tree |
Tree-based index structures
|
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants |
M-Tree and variants.
|
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.metrical.mtreevariants.split |
Splitting strategies of nodes in an M-Tree (and variants).
|
de.lmu.ifi.dbs.elki.index.tree.query |
Classes related to generic tree queries.
|
de.lmu.ifi.dbs.elki.index.tree.spatial |
Tree-based index structures for spatial indexing.
|
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants |
R*-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.math.linearalgebra |
Linear Algebra package provides classes and computational methods for operations on matrices.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.result.optics |
Result classes for OPTICS.
|
de.lmu.ifi.dbs.elki.result.outlier |
Outlier result classes
|
de.lmu.ifi.dbs.elki.result.textwriter |
Text serialization (CSV, Gnuplot, Console, ...)
|
de.lmu.ifi.dbs.elki.utilities |
Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
|
de.lmu.ifi.dbs.elki.utilities.datastructures.heap |
Heap structures and variations such as bounded priority heaps.
|
de.lmu.ifi.dbs.elki.utilities.exceptions |
Exception classes and common exception messages.
|
de.lmu.ifi.dbs.elki.visualization.opticsplot |
Code for drawing OPTICS plots
|
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d |
Visualizers based on 2D projections.
|
Modifier and Type | Method and Description |
---|---|
CollectionResult<CTriple<DBID,DBID,Double>> |
MaterializeDistances.run(Database database,
Relation<O> relation)
Iterates over all points in the database.
|
CollectionResult<CTriple<DBID,DBID,Double>> |
MaterializeDistances.run(Database database,
Relation<O> relation)
Iterates over all points in the database.
|
Modifier and Type | Field and Description |
---|---|
private WritableDataStore<DBID> |
SLINK.pi
The values of the function Pi of the pointer representation.
|
Modifier and Type | Method and Description |
---|---|
private DBID |
DeLiClu.getStartObject(Relation<NV> relation)
Returns the id of the start object for the run method.
|
private DBID |
SLINK.lastObjectInCluster(DBID id,
D stopdist,
DataStore<DBID> pi,
DataStore<D> lambda) |
Modifier and Type | Method and Description |
---|---|
protected List<DBID> |
SNNClustering.findSNNNeighbors(SimilarityQuery<O,IntegerDistance> snnInstance,
DBID queryObject)
Returns the shared nearest neighbors of the specified query object in the
given database.
|
Modifier and Type | Method and Description |
---|---|
int |
SLINK.CompareByLambda.compare(DBID id1,
DBID id2) |
protected void |
DBSCAN.expandCluster(Database database,
RangeQuery<O,D> rangeQuery,
DBID startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog)
DBSCAN-function expandCluster.
|
protected void |
AbstractProjectedDBSCAN.expandCluster(LocallyWeightedDistanceFunction.Instance<V> distFunc,
RangeQuery<V,DoubleDistance> rangeQuery,
DBID startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog)
ExpandCluster function of DBSCAN.
|
protected void |
SNNClustering.expandCluster(SimilarityQuery<O,IntegerDistance> snnInstance,
DBID startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog)
DBSCAN-function expandCluster adapted to SNN criterion.
|
protected void |
OPTICS.expandClusterOrder(ClusterOrderResult<D> clusterOrder,
Database database,
RangeQuery<O,D> rangeQuery,
DBID objectID,
D epsilon,
FiniteProgress progress)
OPTICS-function expandClusterOrder.
|
protected void |
OPTICS.expandClusterOrderDouble(ClusterOrderResult<DoubleDistance> clusterOrder,
Database database,
RangeQuery<O,DoubleDistance> rangeQuery,
DBID objectID,
DoubleDistance epsilon,
FiniteProgress progress)
OPTICS-function expandClusterOrder.
|
protected List<DBID> |
SNNClustering.findSNNNeighbors(SimilarityQuery<O,IntegerDistance> snnInstance,
DBID queryObject)
Returns the shared nearest neighbors of the specified query object in the
given database.
|
double[] |
EM.getProbClusterIGivenX(DBID index)
Get the probabilities for a given point.
|
private DBID |
SLINK.lastObjectInCluster(DBID id,
D stopdist,
DataStore<DBID> pi,
DataStore<D> lambda) |
private void |
SLINK.step1(DBID newID)
First step: Initialize P(id) = id, L(id) = infinity.
|
private void |
SLINK.step2(DBID newID,
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.
|
private void |
SLINK.step3(DBID newID,
DBIDs processedIDs,
WritableDataStore<D> m)
Third step: Determine the values for P and L
|
private void |
SLINK.step4(DBID newID,
DBIDs processedIDs)
Fourth step: Actualize the clusters if necessary
|
Modifier and Type | Method and Description |
---|---|
private Clustering<Model> |
SLINK.extractClusters_erich(DBIDs ids,
DataStore<DBID> pi,
DataStore<D> lambda,
int minclusters)
Extract all clusters from the pi-lambda-representation.
|
private Clustering<DendrogramModel<D>> |
SLINK.extractClusters(DBIDs ids,
DataStore<DBID> pi,
DataStore<D> lambda,
int minclusters)
Extract all clusters from the pi-lambda-representation.
|
private DBID |
SLINK.lastObjectInCluster(DBID id,
D stopdist,
DataStore<DBID> pi,
DataStore<D> lambda) |
private Cluster<DendrogramModel<D>> |
SLINK.root(Map<DBID,ModifiableDBIDs> cluster_ids,
Map<DBID,D> cluster_distances,
DataStore<DBID> pi,
DataStore<D> lambda,
ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier,
FiniteProgress progress) |
private Cluster<DendrogramModel<D>> |
SLINK.root(Map<DBID,ModifiableDBIDs> cluster_ids,
Map<DBID,D> cluster_distances,
DataStore<DBID> pi,
DataStore<D> lambda,
ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier,
FiniteProgress progress) |
private Cluster<DendrogramModel<D>> |
SLINK.root(Map<DBID,ModifiableDBIDs> cluster_ids,
Map<DBID,D> cluster_distances,
DataStore<DBID> pi,
DataStore<D> lambda,
ModifiableHierarchy<Cluster<DendrogramModel<D>>> hier,
FiniteProgress progress) |
Constructor and Description |
---|
ORCLUS.ORCLUSCluster(V o,
DBID id,
V factory)
Creates a new cluster containing the specified object o.
|
Modifier and Type | Field and Description |
---|---|
private Map<HyperBoundingBox,Map<DBID,Double>> |
CASHIntervalSplit.f_maxima
Caches maximum function values for given intervals, used for better split
performance.
|
private Map<HyperBoundingBox,Map<DBID,Double>> |
CASHIntervalSplit.f_minima
Caches minimum function values for given intervals, used for better split
performance.
|
Modifier and Type | Method and Description |
---|---|
private Map<DBID,PROCLUS.PROCLUSCluster> |
PROCLUS.assignPoints(Map<DBID,Set<Integer>> dimensions,
Relation<V> database)
Assigns the objects to the clusters.
|
private Map<DBID,Set<Integer>> |
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,List<DistanceResultPair<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.
|
Modifier and Type | Method and Description |
---|---|
private Map<DBID,PROCLUS.PROCLUSCluster> |
PROCLUS.assignPoints(Map<DBID,Set<Integer>> dimensions,
Relation<V> database)
Assigns the objects to the clusters.
|
private ModifiableDBIDs |
PROCLUS.computeBadMedoids(Map<DBID,PROCLUS.PROCLUSCluster> clusters,
int threshold)
Computes the bad medoids, where the medoid of a cluster with less than the
specified threshold of objects is bad.
|
private double |
PROCLUS.evaluateClusters(Map<DBID,PROCLUS.PROCLUSCluster> clusters,
Map<DBID,Set<Integer>> dimensions,
Relation<V> database)
Evaluates the quality of the clusters.
|
private double |
PROCLUS.evaluateClusters(Map<DBID,PROCLUS.PROCLUSCluster> clusters,
Map<DBID,Set<Integer>> dimensions,
Relation<V> database)
Evaluates the quality of the clusters.
|
Modifier and Type | Method and Description |
---|---|
boolean |
CLIQUEUnit.addFeatureVector(DBID id,
V vector)
Adds the id of the specified feature vector to this unit, if this unit
contains the feature vector.
|
Modifier and Type | Method and Description |
---|---|
private void |
ByLabelClustering.assign(HashMap<String,ModifiableDBIDs> labelMap,
String label,
DBID id)
Assigns the specified id to the labelMap according to its label
|
Modifier and Type | Method and Description |
---|---|
private PriorityQueue<FCPair<Double,DBID>> |
ABOD.calcDistsandNN(Relation<V> data,
KernelMatrix kernelMatrix,
int sampleSize,
DBID aKey,
HashMap<DBID,Double> dists) |
private PriorityQueue<FCPair<Double,DBID>> |
ABOD.calcDistsandRNDSample(Relation<V> data,
KernelMatrix kernelMatrix,
int sampleSize,
DBID aKey,
HashMap<DBID,Double> dists) |
IterableIterator<DBID> |
SOD.SODProxyScoreResult.iterDBIDs() |
Modifier and Type | Method and Description |
---|---|
private double |
ABOD.calcCos(KernelMatrix kernelMatrix,
DBID aKey,
DBID bKey)
Compute the cosinus value between vectors aKey and bKey.
|
private double |
ABOD.calcDenominator(KernelMatrix kernelMatrix,
DBID aKey,
DBID bKey,
DBID cKey) |
private PriorityQueue<FCPair<Double,DBID>> |
ABOD.calcDistsandNN(Relation<V> data,
KernelMatrix kernelMatrix,
int sampleSize,
DBID aKey,
HashMap<DBID,Double> dists) |
private PriorityQueue<FCPair<Double,DBID>> |
ABOD.calcDistsandRNDSample(Relation<V> data,
KernelMatrix kernelMatrix,
int sampleSize,
DBID aKey,
HashMap<DBID,Double> dists) |
private double[] |
ABOD.calcFastNormalization(DBID x,
HashMap<DBID,Double> dists) |
private double |
ABOD.calcNumerator(KernelMatrix kernelMatrix,
DBID aKey,
DBID bKey,
DBID cKey) |
void |
SOD.SODProxyScoreResult.delete(DBID id) |
private void |
ABOD.generateExplanation(Relation<V> data,
DBID key,
LinkedList<DBID> expList) |
Double |
SOD.SODProxyScoreResult.get(DBID objID) |
private double |
ABOD.getAbofFilter(KernelMatrix kernelMatrix,
DBID aKey,
HashMap<DBID,Double> dists,
double fulCounter,
double counter,
DBIDs neighbors) |
private KNNList<DoubleDistance> |
SOD.getKNN(Relation<V> database,
SimilarityQuery<V,IntegerDistance> snnInstance,
DBID queryObject)
Provides the k nearest neighbors in terms of the shared nearest neighbor
distance.
|
private int |
ABOD.mapDBID(DBID aKey) |
void |
SOD.SODProxyScoreResult.set(DBID id,
Double val) |
Modifier and Type | Method and Description |
---|---|
private PriorityQueue<FCPair<Double,DBID>> |
ABOD.calcDistsandNN(Relation<V> data,
KernelMatrix kernelMatrix,
int sampleSize,
DBID aKey,
HashMap<DBID,Double> dists) |
private PriorityQueue<FCPair<Double,DBID>> |
ABOD.calcDistsandRNDSample(Relation<V> data,
KernelMatrix kernelMatrix,
int sampleSize,
DBID aKey,
HashMap<DBID,Double> dists) |
private double[] |
ABOD.calcFastNormalization(DBID x,
HashMap<DBID,Double> dists) |
private void |
ABOD.generateExplanation(Relation<V> data,
DBID key,
LinkedList<DBID> expList) |
private double |
ABOD.getAbofFilter(KernelMatrix kernelMatrix,
DBID aKey,
HashMap<DBID,Double> dists,
double fulCounter,
double counter,
DBIDs neighbors) |
Modifier and Type | Method and Description |
---|---|
private Pair<DBID,Double> |
CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx,
Relation<? extends NumberVector<?,?>> relationy)
Run a single iteration of the GLS-SOD modeling step
|
Modifier and Type | Method and Description |
---|---|
DBIDs |
AbstractPrecomputedNeighborhood.getNeighborDBIDs(DBID reference) |
DBIDs |
NeighborSetPredicate.getNeighborDBIDs(DBID reference)
Get the neighbors of a reference object for DBSCAN.
|
Modifier and Type | Method and Description |
---|---|
Collection<DoubleObjPair<DBID>> |
UnweightedNeighborhoodAdapter.getWeightedNeighbors(DBID reference) |
Collection<DoubleObjPair<DBID>> |
LinearWeightedExtendedNeighborhood.getWeightedNeighbors(DBID reference) |
Collection<DoubleObjPair<DBID>> |
WeightedNeighborSetPredicate.getWeightedNeighbors(DBID reference)
Get the neighbors of a reference object for DBSCAN.
|
Modifier and Type | Method and Description |
---|---|
Collection<DoubleObjPair<DBID>> |
UnweightedNeighborhoodAdapter.getWeightedNeighbors(DBID reference) |
Collection<DoubleObjPair<DBID>> |
LinearWeightedExtendedNeighborhood.getWeightedNeighbors(DBID reference) |
Collection<DoubleObjPair<DBID>> |
WeightedNeighborSetPredicate.getWeightedNeighbors(DBID reference)
Get the neighbors of a reference object for DBSCAN.
|
Modifier and Type | Method and Description |
---|---|
private void |
DistanceStatisticsWithClasses.shrinkHeap(TreeSet<FCPair<Double,DBID>> hotset,
int k) |
Modifier and Type | Method and Description |
---|---|
private DBID |
JSONWebServer.stringToDBID(String query)
Parse a string into a DBID.
|
Modifier and Type | Method and Description |
---|---|
protected void |
JSONWebServer.bundleToJSON(JSONBuffer re,
DBID id)
Serialize an object bundle to JSON.
|
Modifier and Type | Field and Description |
---|---|
protected HashMap<DBID,Double> |
KNNExplorer.ExplorerWindow.distancecache |
Modifier and Type | Method and Description |
---|---|
private Element |
KNNExplorer.ExplorerWindow.plotSeries(DBID idx,
int resolution)
Plot a single time series.
|
Modifier and Type | Field and Description |
---|---|
static SimpleTypeInformation<DBID> |
TypeUtil.DBID
Database IDs
|
Modifier and Type | Method and Description |
---|---|
private void |
HashmapDatabase.doDelete(DBID id)
Removes the object with the specified id from this database.
|
void |
DatabaseEventManager.fireObjectInserted(DBID insertion)
Convenience method, calls
fireObjectChanged(insertion,
DataStoreEvent.Type.INSERT) . |
protected void |
DatabaseEventManager.fireObjectRemoved(DBID deletion)
Convenience method, calls
fireObjectChanged(deletion,
DataStoreEvent.Type.DELETE) . |
SingleObjectBundle |
AbstractDatabase.getBundle(DBID id) |
SingleObjectBundle |
Database.getBundle(DBID id)
Returns the DatabaseObject represented by the specified id.
|
private void |
HashmapDatabase.restoreID(DBID id)
Makes the given id reusable for new insertion operations.
|
Modifier and Type | Method and Description |
---|---|
void |
WritableDataStore.delete(DBID id)
Delete the contents for a particular ID and notifies the registered
listeners.
|
T |
DataStore.get(DBID id)
Retrieves an object from the storage.
|
int |
RangeIDMap.map(DBID dbid) |
int |
DataStoreIDMap.map(DBID dbid)
Map a DBID to a database id.
|
T |
WritableDataStore.put(DBID id,
T value)
Associates the specified value with the specified id in this storage.
|
boolean |
WritableRecordStore.remove(DBID id)
Remove an object from the store, all columns.
|
Modifier and Type | Field and Description |
---|---|
private Map<DBID,Object[]> |
MapRecordStore.data
Storage Map
|
private Map<DBID,T> |
MapStore.data
Storage Map
|
Modifier and Type | Method and Description |
---|---|
void |
ArrayStore.delete(DBID id) |
void |
MapRecordStore.StorageAccessor.delete(DBID id) |
void |
MapStore.delete(DBID id) |
void |
ArrayRecordStore.StorageAccessor.delete(DBID id) |
T |
ArrayStore.get(DBID id) |
T |
MapRecordStore.StorageAccessor.get(DBID id) |
T |
MapStore.get(DBID id) |
T |
ArrayRecordStore.StorageAccessor.get(DBID id) |
protected <T> T |
MapRecordStore.get(DBID id,
int index)
Actual getter
|
protected <T> T |
ArrayRecordStore.get(DBID id,
int index)
Actual getter
|
T |
ArrayStore.put(DBID id,
T value) |
T |
MapRecordStore.StorageAccessor.put(DBID id,
T value) |
T |
MapStore.put(DBID id,
T value) |
T |
ArrayRecordStore.StorageAccessor.put(DBID id,
T value) |
boolean |
MapRecordStore.remove(DBID id) |
boolean |
ArrayRecordStore.remove(DBID id) |
protected <T> T |
MapRecordStore.set(DBID id,
int index,
T value)
Actual setter
|
protected <T> T |
ArrayRecordStore.set(DBID id,
int index,
T value)
Actual setter
|
Constructor and Description |
---|
MapRecordStore(int rlen,
Map<DBID,Object[]> data)
Constructor with existing data.
|
MapStore(Map<DBID,T> data)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static DBID |
DBIDUtil.generateSingleDBID()
Generate a single DBID
|
DBID |
DBIDFactory.generateSingleDBID()
Generate a single DBID
|
DBID |
EmptyDBIDs.get(int i) |
DBID |
DBIDPair.getFirst()
Getter for first
|
DBID |
DBIDPair.getSecond()
Getter for second element in pair
|
static DBID |
DBIDUtil.importInteger(int id)
Import an Integer DBID.
|
DBID |
DBIDFactory.importInteger(int id)
Import an integer ID
|
Modifier and Type | Method and Description |
---|---|
Collection<DBID> |
EmptyDBIDs.asCollection() |
Collection<DBID> |
DBIDs.asCollection()
Retrieve collection access to the IDs
|
ByteBufferSerializer<DBID> |
DBIDUtil.getDBIDSerializer()
Get a serializer for DBIDs
|
ByteBufferSerializer<DBID> |
DBIDFactory.getDBIDSerializer()
Get a serializer for DBIDs
|
ByteBufferSerializer<DBID> |
DBIDUtil.getDBIDSerializerStatic()
Get a serializer for DBIDs with static size
|
FixedSizeByteBufferSerializer<DBID> |
DBIDFactory.getDBIDSerializerStatic()
Get a serializer for DBIDs with static size
|
Class<? extends DBID> |
DBIDFactory.getTypeRestriction()
Get type restriction
|
Iterator<DBID> |
EmptyDBIDs.iterator() |
Iterator<DBID> |
DBIDs.iterator()
Retrieve Iterator access to the IDs.
|
Modifier and Type | Method and Description |
---|---|
static void |
DBIDUtil.deallocateSingleDBID(DBID id)
Return a single DBID for reuse.
|
void |
DBIDFactory.deallocateSingleDBID(DBID id)
Return a single DBID for reuse.
|
int |
DBIDRange.getOffset(DBID dbid)
Get offset in the array for a particular DBID.
|
DBIDPair |
DBIDFactory.makePair(DBID first,
DBID second)
Make a DBID pair from two existing DBIDs.
|
static DBIDPair |
DBIDUtil.newPair(DBID id1,
DBID id2)
Make a DBID pair.
|
Modifier and Type | Method and Description |
---|---|
DBID |
MaskedDBIDs.Itr.next() |
DBID |
MaskedDBIDs.InvItr.next() |
Modifier and Type | Method and Description |
---|---|
Collection<DBID> |
GenericTreeSetModifiableDBIDs.asCollection() |
Collection<DBID> |
UnmodifiableDBIDs.asCollection() |
Collection<DBID> |
GenericArrayModifiableDBIDs.asCollection() |
Collection<DBID> |
MergedDBIDs.asCollection() |
Collection<DBID> |
MaskedDBIDs.asCollection() |
Collection<DBID> |
GenericHashSetModifiableDBIDs.asCollection() |
Iterator<DBID> |
UnmodifiableDBIDs.iterator() |
Iterator<DBID> |
MergedDBIDs.iterator() |
Iterator<DBID> |
MaskedDBIDs.iterator() |
Modifier and Type | Method and Description |
---|---|
boolean |
MergedDBIDs.add(DBID e) |
boolean |
MaskedDBIDs.add(DBID e) |
Modifier and Type | Method and Description |
---|---|
boolean |
MergedDBIDs.addAll(Collection<? extends DBID> c) |
Modifier and Type | Class and Description |
---|---|
(package private) class |
IntegerDBID
Database ID object.
|
Modifier and Type | Method and Description |
---|---|
DBID |
IntegerDBID.DynamicSerializer.fromByteBuffer(ByteBuffer buffer) |
DBID |
IntegerDBID.StaticSerializer.fromByteBuffer(ByteBuffer buffer) |
DBID |
ReusingDBIDFactory.generateSingleDBID() |
DBID |
SimpleDBIDFactory.generateSingleDBID() |
DBID |
TrivialDBIDFactory.generateSingleDBID() |
DBID |
IntegerArrayStaticDBIDs.get(int i) |
DBID |
IntegerDBID.get(int i) |
DBID |
IntegerDBIDRange.get(int i) |
DBID |
SimpleDBIDFactory.importInteger(int id) |
DBID |
TrivialDBIDFactory.importInteger(int id) |
DBID |
IntegerArrayStaticDBIDs.Itr.next() |
DBID |
IntegerDBID.Itr.next() |
DBID |
IntegerDBIDRange.Itr.next() |
Modifier and Type | Method and Description |
---|---|
Collection<DBID> |
IntegerArrayStaticDBIDs.asCollection() |
Collection<DBID> |
IntegerDBID.asCollection() |
Collection<DBID> |
IntegerDBIDRange.asCollection() |
ByteBufferSerializer<DBID> |
SimpleDBIDFactory.getDBIDSerializer() |
ByteBufferSerializer<DBID> |
TrivialDBIDFactory.getDBIDSerializer() |
FixedSizeByteBufferSerializer<DBID> |
SimpleDBIDFactory.getDBIDSerializerStatic() |
FixedSizeByteBufferSerializer<DBID> |
TrivialDBIDFactory.getDBIDSerializerStatic() |
Class<? extends DBID> |
SimpleDBIDFactory.getTypeRestriction() |
Class<? extends DBID> |
TrivialDBIDFactory.getTypeRestriction() |
Iterator<DBID> |
IntegerArrayStaticDBIDs.iterator() |
Iterator<DBID> |
IntegerDBID.iterator() |
Iterator<DBID> |
IntegerDBIDRange.iterator() |
Modifier and Type | Method and Description |
---|---|
int |
IntegerDBID.compareTo(DBID o) |
void |
ReusingDBIDFactory.deallocateSingleDBID(DBID id) |
void |
SimpleDBIDFactory.deallocateSingleDBID(DBID id) |
void |
TrivialDBIDFactory.deallocateSingleDBID(DBID id) |
int |
IntegerDBID.DynamicSerializer.getByteSize(DBID object) |
int |
IntegerDBID.StaticSerializer.getByteSize(DBID object) |
int |
IntegerDBIDRange.getOffset(DBID dbid)
For storage array offsets.
|
DBIDPair |
SimpleDBIDFactory.makePair(DBID first,
DBID second) |
DBIDPair |
TrivialDBIDFactory.makePair(DBID first,
DBID second) |
void |
IntegerDBID.DynamicSerializer.toByteBuffer(ByteBuffer buffer,
DBID object) |
void |
IntegerDBID.StaticSerializer.toByteBuffer(ByteBuffer buffer,
DBID object) |
Modifier and Type | Field and Description |
---|---|
(package private) DBID |
DoubleDistanceResultPair.id
Object ID
|
Modifier and Type | Method and Description |
---|---|
DBID |
DistanceResultPair.getDBID()
Getter for second element in pair
|
DBID |
DoubleDistanceResultPair.getDBID() |
DBID |
GenericDistanceResultPair.getDBID()
Getter for second element in pair
|
DBID |
DoubleDistanceResultPair.getSecond()
Deprecated.
Use
DoubleDistanceResultPair.getDBID() for clearness. |
Modifier and Type | Method and Description |
---|---|
void |
DistanceResultPair.setID(DBID second)
Setter for second
|
void |
DoubleDistanceResultPair.setID(DBID id) |
void |
GenericDistanceResultPair.setID(DBID second)
Setter for second
|
Constructor and Description |
---|
DoubleDistanceResultPair(double distance,
DBID id)
Constructor.
|
GenericDistanceResultPair(D first,
DBID second)
Canonical constructor
|
Modifier and Type | Method and Description |
---|---|
D |
PrimitiveDistanceQuery.distance(DBID id1,
DBID id2) |
D |
DistanceQuery.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
abstract D |
AbstractDistanceQuery.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
D |
DBIDDistanceQuery.distance(DBID id1,
DBID id2) |
D |
PrimitiveDistanceQuery.distance(DBID id1,
O o2) |
D |
AbstractDatabaseDistanceQuery.distance(DBID id1,
O o2) |
D |
DistanceQuery.distance(DBID id1,
O o2)
Returns the distance between the two objects specified by their object ids.
|
abstract D |
AbstractDistanceQuery.distance(DBID id1,
O o2)
Returns the distance between the two objects specified by their object ids.
|
D |
PrimitiveDistanceQuery.distance(O o1,
DBID id2) |
D |
AbstractDatabaseDistanceQuery.distance(O o1,
DBID id2) |
D |
DistanceQuery.distance(O o1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
abstract D |
AbstractDistanceQuery.distance(O o1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
D |
SpatialDistanceQuery.minDist(SpatialComparable mbr,
DBID id)
Computes the minimum distance between the given MBR and the FeatureVector
object according to this distance function.
|
D |
SpatialPrimitiveDistanceQuery.minDist(SpatialComparable mbr,
DBID id) |
D |
PrimitiveDistanceSimilarityQuery.similarity(DBID id1,
DBID id2) |
D |
PrimitiveDistanceSimilarityQuery.similarity(DBID id1,
O o2) |
D |
PrimitiveDistanceSimilarityQuery.similarity(O o1,
DBID id2) |
Constructor and Description |
---|
DBIDDistanceQuery(Relation<DBID> relation,
DBIDDistanceFunction<D> distanceFunction)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
KNNQuery.getKNNForDBID(DBID id,
int k)
Get the k nearest neighbors for a particular id.
|
List<DistanceResultPair<DoubleDistance>> |
LinearScanRawDoubleDistanceKNNQuery.getKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
PreprocessorKNNQuery.getKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
LinearScanKNNQuery.getKNNForDBID(DBID id,
int k) |
abstract List<DistanceResultPair<D>> |
AbstractDistanceKNNQuery.getKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
LinearScanPrimitiveDistanceKNNQuery.getKNNForDBID(DBID id,
int k) |
Modifier and Type | Method and Description |
---|---|
void |
KNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps)
Bulk query method configured by a map.
|
void |
PreprocessorKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
void |
LinearScanKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
void |
LinearScanPrimitiveDistanceKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
LinearScanPrimitiveDistanceRangeQuery.getRangeForDBID(DBID id,
D range) |
List<DistanceResultPair<D>> |
RangeQuery.getRangeForDBID(DBID id,
D range)
Get the nearest neighbors for a particular id in a given query range
|
abstract List<DistanceResultPair<D>> |
AbstractDistanceRangeQuery.getRangeForDBID(DBID id,
D range) |
List<DistanceResultPair<D>> |
LinearScanRangeQuery.getRangeForDBID(DBID id,
D range) |
List<DistanceResultPair<DoubleDistance>> |
LinearScanRawDoubleDistanceRangeQuery.getRangeForDBID(DBID id,
DoubleDistance range) |
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<D>> |
LinearScanRKNNQuery.getRKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
RKNNQuery.getRKNNForDBID(DBID id,
int k)
Get the reverse k nearest neighbors for a particular id.
|
List<DistanceResultPair<D>> |
PreprocessorRKNNQuery.getRKNNForDBID(DBID id,
int k) |
abstract List<DistanceResultPair<D>> |
AbstractRKNNQuery.getRKNNForDBID(DBID id,
int k) |
Modifier and Type | Method and Description |
---|---|
D |
SimilarityQuery.similarity(DBID id1,
DBID id2)
Returns the similarity between the two objects specified by their object
ids.
|
D |
PrimitiveSimilarityQuery.similarity(DBID id1,
DBID id2) |
abstract D |
AbstractSimilarityQuery.similarity(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
D |
SimilarityQuery.similarity(DBID id1,
O o2)
Returns the similarity between the two objects specified by their object
ids.
|
D |
PrimitiveSimilarityQuery.similarity(DBID id1,
O o2) |
abstract D |
AbstractSimilarityQuery.similarity(DBID id1,
O o2)
Returns the distance between the two objects specified by their object ids.
|
D |
AbstractDBIDSimilarityQuery.similarity(DBID id1,
O o2) |
D |
SimilarityQuery.similarity(O o1,
DBID id2)
Returns the similarity between the two objects specified by their object
ids.
|
D |
PrimitiveSimilarityQuery.similarity(O o1,
DBID id2) |
abstract D |
AbstractSimilarityQuery.similarity(O o1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
D |
AbstractDBIDSimilarityQuery.similarity(O o1,
DBID id2) |
Modifier and Type | Method and Description |
---|---|
DBID |
DBIDView.get(DBID id) |
Modifier and Type | Method and Description |
---|---|
SimpleTypeInformation<DBID> |
DBIDView.getDataTypeInformation() |
IterableIterator<DBID> |
ConvertToStringView.iterDBIDs() |
IterableIterator<DBID> |
ProxyView.iterDBIDs() |
IterableIterator<DBID> |
MaterializedRelation.iterDBIDs() |
IterableIterator<DBID> |
DBIDView.iterDBIDs() |
IterableIterator<DBID> |
Relation.iterDBIDs()
Get an iterator access to the DBIDs.
|
Modifier and Type | Method and Description |
---|---|
void |
ConvertToStringView.delete(DBID id) |
void |
ProxyView.delete(DBID id) |
void |
MaterializedRelation.delete(DBID id)
Delete an objects values.
|
void |
DBIDView.delete(DBID id) |
void |
Relation.delete(DBID id)
Delete an objects values.
|
String |
ConvertToStringView.get(DBID id) |
O |
ProxyView.get(DBID id) |
O |
MaterializedRelation.get(DBID id) |
DBID |
DBIDView.get(DBID id) |
O |
Relation.get(DBID id)
Get the representation of an object.
|
void |
DBIDView.set(DBID id,
DBID val) |
void |
ProxyView.set(DBID id,
O val) |
void |
MaterializedRelation.set(DBID id,
O val) |
void |
Relation.set(DBID id,
O val)
Set an object representation.
|
void |
ConvertToStringView.set(DBID id,
String val) |
Modifier and Type | Method and Description |
---|---|
boolean |
NumberDistanceParser.containsKey(DBID id1,
DBID id2,
Map<DBIDPair,D> cache)
Returns true if the specified distance cache contains a distance
value for the specified ids.
|
private void |
NumberDistanceParser.put(DBID id1,
DBID id2,
D distance,
Map<DBIDPair,D> cache)
Puts the specified distance value for the given ids to the distance cache.
|
Modifier and Type | Method and Description |
---|---|
<O extends DBID> |
AbstractDBIDDistanceFunction.instantiate(Relation<O> database) |
Modifier and Type | Method and Description |
---|---|
SimpleTypeInformation<DBID> |
AbstractDBIDDistanceFunction.getInputTypeRestriction() |
Modifier and Type | Method and Description |
---|---|
abstract D |
AbstractDBIDDistanceFunction.distance(DBID o1,
DBID o2) |
D |
ProxyDistanceFunction.distance(DBID o1,
DBID o2) |
D |
MinKDistance.Instance.distance(DBID id1,
DBID id2) |
DoubleDistance |
SharedNearestNeighborJaccardDistanceFunction.Instance.distance(DBID id1,
DBID id2) |
D |
DBIDDistanceFunction.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their object ids.
|
DoubleDistance |
LocallyWeightedDistanceFunction.Instance.distance(DBID id1,
DBID id2)
Computes the distance between two given real vectors according to this
distance function.
|
DoubleDistance |
RandomStableDistanceFunction.distance(DBID o1,
DBID o2) |
Modifier and Type | Method and Description |
---|---|
DoubleDistance |
AbstractSimilarityAdapter.Instance.distance(DBID id1,
DBID id2) |
Modifier and Type | Method and Description |
---|---|
PCACorrelationDistance |
PCABasedCorrelationDistanceFunction.Instance.distance(DBID id1,
DBID id2) |
BitDistance |
ERiCDistanceFunction.Instance.distance(DBID id1,
DBID id2)
Note, that the pca of o1 must have equal ore more strong eigenvectors
than the pca of o2.
|
Modifier and Type | Method and Description |
---|---|
DoubleDistance |
FileBasedDoubleDistanceFunction.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their objects
ids.
|
FloatDistance |
FileBasedFloatDistanceFunction.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their objects
ids.
|
DoubleDistance |
DiskCacheBasedDoubleDistanceFunction.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their objects
ids.
|
FloatDistance |
DiskCacheBasedFloatDistanceFunction.distance(DBID id1,
DBID id2)
Returns the distance between the two objects specified by their objects
ids.
|
Modifier and Type | Method and Description |
---|---|
PreferenceVectorBasedCorrelationDistance |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance.distance(DBID id1,
DBID id2) |
SubspaceDistance |
SubspaceDistanceFunction.Instance.distance(DBID id1,
DBID id2)
Note, that the pca of o1 must have equal ore more strong eigenvectors
than the pca of o2.
|
double |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance.weightedDistance(DBID id1,
DBID id2,
BitSet weightVector)
Computes the weighted distance between the two specified vectors
according to the given preference vector.
|
double |
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance.weightedPrefereneceVectorDistance(DBID id1,
DBID id2)
Computes the weighted distance between the two specified data vectors
according to their preference vectors.
|
Modifier and Type | Field and Description |
---|---|
protected Relation<? extends DBID> |
AbstractDBIDSimilarityFunction.database
The database we work on
|
Modifier and Type | Method and Description |
---|---|
D |
DBIDSimilarityFunction.similarity(DBID id1,
DBID id2)
Computes the similarity between two given DatabaseObjects according to this
similarity function.
|
DoubleDistance |
FractionalSharedNearestNeighborSimilarityFunction.Instance.similarity(DBID id1,
DBID id2) |
IntegerDistance |
SharedNearestNeighborSimilarityFunction.Instance.similarity(DBID id1,
DBID id2) |
Constructor and Description |
---|
AbstractDBIDSimilarityFunction(Relation<? extends DBID> database)
Constructor.
|
Modifier and Type | Field and Description |
---|---|
private Iterator<DBID> |
ROC.SimpleAdapter.iter
Original Iterator
|
private Iterator<DBID> |
ROC.OutlierScoreAdapter.iter
Original Iterator
|
Modifier and Type | Method and Description |
---|---|
private Iterator<DBID> |
ComputeROCCurve.getDBIDIterator(IterableResult<?> ir)
Wrap the uncheckable cast with the manual check.
|
Pair<D,DBID> |
ROC.DistanceResultAdapter.next() |
DoubleObjPair<DBID> |
ROC.OutlierScoreAdapter.next() |
Modifier and Type | Method and Description |
---|---|
private ComputeROCCurve.ROCResult |
ComputeROCCurve.computeROCResult(int size,
SetDBIDs positiveids,
Iterator<DBID> iter) |
Constructor and Description |
---|
ROC.SimpleAdapter(Iterator<DBID> iter)
Constructor
|
Modifier and Type | Method and Description |
---|---|
private Iterator<DBID> |
ComputeSimilarityMatrixImage.getDBIDIterator(IterableResult<?> ir)
Wrap the uncheckable cast with the manual check.
|
Modifier and Type | Method and Description |
---|---|
private ComputeSimilarityMatrixImage.SimilarityMatrix |
ComputeSimilarityMatrixImage.computeSimilarityMatrixImage(Relation<O> relation,
Iterator<DBID> iter)
Compute the actual similarity image.
|
Modifier and Type | Method and Description |
---|---|
boolean |
AbstractIndex.delete(DBID id) |
boolean |
Index.delete(DBID id)
Deletes the specified object from this index.
|
void |
AbstractIndex.insert(DBID id) |
void |
Index.insert(DBID id)
Inserts the specified object into this index.
|
Modifier and Type | Method and Description |
---|---|
P |
LocalProjectionIndex.getLocalProjection(DBID objid)
Get the precomputed local projection for a particular object ID.
|
Modifier and Type | Method and Description |
---|---|
boolean |
MaterializeKNNPreprocessor.delete(DBID id) |
List<DistanceResultPair<D>> |
MaterializeKNNPreprocessor.get(DBID objid)
Get the k nearest neighbors.
|
List<DistanceResultPair<D>> |
MaterializeKNNAndRKNNPreprocessor.getKNN(DBID id)
Returns the materialized kNNs of the specified id.
|
List<DistanceResultPair<D>> |
MaterializeKNNAndRKNNPreprocessor.getRKNN(DBID id)
Returns the materialized RkNNs of the specified id.
|
void |
MaterializeKNNPreprocessor.insert(DBID id) |
Modifier and Type | Method and Description |
---|---|
PCAFilteredResult |
AbstractFilteredPCAIndex.getLocalProjection(DBID objid) |
PCAFilteredResult |
FilteredLocalPCAIndex.getLocalProjection(DBID objid)
Get the precomputed local PCA for a particular object ID.
|
protected abstract List<DistanceResultPair<DoubleDistance>> |
AbstractFilteredPCAIndex.objectsForPCA(DBID id)
Returns the objects to be considered within the PCA for the specified query
object.
|
protected List<DistanceResultPair<DoubleDistance>> |
KNNQueryFilteredPCAIndex.objectsForPCA(DBID id) |
protected List<DistanceResultPair<DoubleDistance>> |
RangeQueryFilteredPCAIndex.objectsForPCA(DBID id) |
Modifier and Type | Method and Description |
---|---|
private BitSet |
HiSCPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation,
DBID id,
DBIDs neighborIDs,
StringBuffer msg)
Determines the preference vector according to the specified neighbor ids.
|
BitSet |
AbstractPreferenceVectorIndex.getPreferenceVector(DBID objid) |
BitSet |
PreferenceVectorIndex.getPreferenceVector(DBID objid)
Get the precomputed preference vector for a particular object ID.
|
Modifier and Type | Method and Description |
---|---|
TreeSetDBIDs |
SharedNearestNeighborIndex.getNearestNeighborSet(DBID objid)
Get the precomputed nearest neighbors
|
TreeSetDBIDs |
SharedNearestNeighborPreprocessor.getNearestNeighborSet(DBID objid) |
Modifier and Type | Method and Description |
---|---|
protected abstract P |
AbstractSubspaceProjectionIndex.computeProjection(DBID id,
List<DistanceResultPair<D>> neighbors,
Relation<NV> relation)
This method implements the type of variance analysis to be computed for a
given point.
|
protected SubspaceProjectionResult |
PreDeConSubspaceIndex.computeProjection(DBID id,
List<DistanceResultPair<D>> neighbors,
Relation<V> database) |
protected PCAFilteredResult |
FourCSubspaceIndex.computeProjection(DBID id,
List<DistanceResultPair<D>> neighbors,
Relation<V> database) |
P |
SubspaceProjectionIndex.getLocalProjection(DBID objid)
Get the precomputed local subspace for a particular object ID.
|
P |
AbstractSubspaceProjectionIndex.getLocalProjection(DBID objid) |
Modifier and Type | Field and Description |
---|---|
private DBID |
AbstractLeafEntry.id
Holds the id of the object (node or data object) represented by this entry.
|
Modifier and Type | Method and Description |
---|---|
DBID |
AbstractLeafEntry.getDBID() |
DBID |
LeafEntry.getDBID()
Get the DBID of this leaf entry.
|
Constructor and Description |
---|
AbstractLeafEntry(DBID id)
Provides a new AbstractEntry with the specified id.
|
Modifier and Type | Field and Description |
---|---|
private DBID |
MTreeDirectoryEntry.routingObjectID
The id of routing object of this entry.
|
Modifier and Type | Method and Description |
---|---|
DBID |
MTreeEntry.getRoutingObjectID()
Returns the id of the underlying database object of this entry, if this
entry is a leaf entry, the id of the routing object, otherwise.
|
DBID |
MTreeDirectoryEntry.getRoutingObjectID()
Returns the id of the routing object of this entry.
|
DBID |
MTreeLeafEntry.getRoutingObjectID()
Returns the id of the underlying data object of this entry.
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractMTreeNode.adjustEntry(E entry,
DBID routingObjectID,
D parentDistance,
AbstractMTree<O,D,N,E> mTree)
Adjusts the parameters of the entry representing this node (e.g. after
insertion of new objects).
|
D |
AbstractMTreeNode.coveringRadius(DBID routingObjectID,
AbstractMTree<O,D,N,E> mTree)
Determines and returns the covering radius of this node.
|
protected abstract E |
AbstractMTree.createNewDirectoryEntry(N node,
DBID routingObjectID,
D parentDistance)
Creates a new directory entry representing the specified node.
|
private IndexTreePath<E> |
AbstractMTree.createNewRoot(N oldRoot,
N newNode,
DBID firstRoutingObjectID,
DBID secondRoutingObjectID)
Creates a new root node that points to the two specified child nodes and
return the path to the new root.
|
protected D |
AbstractMTree.distance(DBID id1,
DBID id2)
Returns the distance between the two specified ids.
|
protected D |
AbstractMTree.distance(DBID id1,
O o2)
Returns the distance between the given object and the id.
|
protected void |
AbstractMTree.doKNNQuery(DBID q,
KNNHeap<D> knnList)
Performs a k-nearest neighbor query for the given FeatureVector with the
given parameter k and the according distance function.
|
protected List<DistanceEntry<D,E>> |
AbstractMTree.getSortedEntries(N node,
DBID q)
Sorts the entries of the specified node according to their minimum distance
to the specified object.
|
void |
MTreeEntry.setRoutingObjectID(DBID objectID)
Sets the id of the underlying database object of this entry, if this entry
is a leaf entry, the id of the routing object, otherwise.
|
void |
MTreeDirectoryEntry.setRoutingObjectID(DBID objectID)
Sets the id of the routing object of this entry.
|
void |
MTreeLeafEntry.setRoutingObjectID(DBID objectID)
todo ok
|
Modifier and Type | Method and Description |
---|---|
protected void |
AbstractMTree.batchNN(N node,
DBIDs ids,
Map<DBID,KNNHeap<D>> knnLists)
Deprecated.
Change to use by-object NN lookups instead.
|
Constructor and Description |
---|
MTreeDirectoryEntry(DBID objectID,
D parentDistance,
Integer nodeID,
D coveringRadius)
Provides a new MTreeDirectoryEntry with the given parameters.
|
MTreeLeafEntry(DBID objectID,
D parentDistance)
Provides a new MTreeLeafEntry object with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
abstract List<DistanceResultPair<D>> |
AbstractMkTree.reverseKNNQuery(DBID id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
protected abstract void |
AbstractMkTreeUnified.kNNdistanceAdjustment(E entry,
Map<DBID,KNNHeap<D>> knnLists)
Performs a distance adjustment in the subtree of the specified root entry.
|
Modifier and Type | Method and Description |
---|---|
void |
MkAppTreeNode.adjustEntry(MkAppEntry<D> entry,
DBID routingObjectID,
D parentDistance,
AbstractMTree<O,D,MkAppTreeNode<O,D>,MkAppEntry<D>> mTree)
Adjusts the parameters of the entry representing this node.
|
protected MkAppEntry<D> |
MkAppTree.createNewDirectoryEntry(MkAppTreeNode<O,D> node,
DBID routingObjectID,
D parentDistance)
Creates a new directory entry representing the specified node.
|
protected MkAppEntry<D> |
MkAppTreeIndex.createNewLeafEntry(DBID id,
O object,
D parentDistance)
Creates a new leaf entry representing the specified data object in the
specified subtree.
|
boolean |
MkAppTreeIndex.delete(DBID id)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
private List<DistanceResultPair<D>> |
MkAppTree.doReverseKNNQuery(int k,
DBID q)
Performs a reverse knn query.
|
void |
MkAppTreeIndex.insert(DBID id) |
List<DistanceResultPair<D>> |
MkAppTree.reverseKNNQuery(DBID id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
private void |
MkAppTree.adjustApproximatedKNNDistances(MkAppEntry<D> entry,
Map<DBID,KNNList<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
private List<D> |
MkAppTree.getMeanKNNList(DBIDs ids,
Map<DBID,KNNList<D>> knnLists) |
Constructor and Description |
---|
MkAppDirectoryEntry(DBID objectID,
D parentDistance,
Integer nodeID,
D coveringRadius,
PolynomialApproximation approximation)
Provides a new MkCoPDirectoryEntry with the given parameters.
|
MkAppLeafEntry(DBID objectID,
D parentDistance,
PolynomialApproximation approximation)
Provides a new MkAppLeafEntry with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
void |
MkCoPTreeNode.adjustEntry(MkCoPEntry<D> entry,
DBID routingObjectID,
D parentDistance,
AbstractMTree<O,D,MkCoPTreeNode<O,D>,MkCoPEntry<D>> mTree) |
protected MkCoPEntry<D> |
MkCoPTree.createNewDirectoryEntry(MkCoPTreeNode<O,D> node,
DBID routingObjectID,
D parentDistance)
Creates a new directory entry representing the specified node.
|
protected MkCoPEntry<D> |
MkCoPTreeIndex.createNewLeafEntry(DBID id,
O object,
D parentDistance)
Creates a new leaf entry representing the specified data object in the
specified subtree.
|
boolean |
MkCoPTreeIndex.delete(DBID id)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
private void |
MkCoPTree.doReverseKNNQuery(int k,
DBID q,
List<DistanceResultPair<D>> result,
ModifiableDBIDs candidates)
Performs a reverse knn query.
|
void |
MkCoPTreeIndex.insert(DBID id) |
List<DistanceResultPair<D>> |
MkCoPTree.reverseKNNQuery(DBID id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
private void |
MkCoPTree.adjustApproximatedKNNDistances(MkCoPEntry<D> entry,
Map<DBID,KNNList<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
Constructor and Description |
---|
MkCoPDirectoryEntry(DBID objectID,
D parentDistance,
Integer nodeID,
D coveringRadius,
ApproximationLine conservativeApproximation)
Provides a new MkCoPDirectoryEntry with the given parameters.
|
MkCoPLeafEntry(DBID objectID,
D parentDistance,
ApproximationLine conservativeApproximation,
ApproximationLine progressiveApproximation)
Provides a new MkCoPLeafEntry with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
void |
MkMaxTreeNode.adjustEntry(MkMaxEntry<D> entry,
DBID routingObjectID,
D parentDistance,
AbstractMTree<O,D,MkMaxTreeNode<O,D>,MkMaxEntry<D>> mTree)
Calls the super method and adjust additionally the k-nearest neighbor
distance of this node as the maximum of the k-nearest neighbor distances of
all its entries.
|
protected MkMaxEntry<D> |
MkMaxTree.createNewDirectoryEntry(MkMaxTreeNode<O,D> node,
DBID routingObjectID,
D parentDistance) |
protected MkMaxLeafEntry<D> |
MkMaxTreeIndex.createNewLeafEntry(DBID id,
O object,
D parentDistance) |
boolean |
MkMaxTreeIndex.delete(DBID id)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
private void |
MkMaxTree.doReverseKNNQuery(DBID q,
MkMaxTreeNode<O,D> node,
MkMaxEntry<D> node_entry,
List<DistanceResultPair<D>> result)
Performs a reverse k-nearest neighbor query in the specified subtree for
the given query object with k =
AbstractMkTreeUnified.k_max . |
void |
MkMaxTreeIndex.insert(DBID id) |
List<DistanceResultPair<D>> |
MkMaxTree.reverseKNNQuery(DBID id,
int k)
Performs a reverse k-nearest neighbor query for the given object ID.
|
Modifier and Type | Method and Description |
---|---|
protected void |
MkMaxTree.kNNdistanceAdjustment(MkMaxEntry<D> entry,
Map<DBID,KNNHeap<D>> knnLists)
Adjusts the knn distance in the subtree of the specified root entry.
|
Constructor and Description |
---|
MkMaxDirectoryEntry(DBID objectID,
D parentDistance,
Integer nodeID,
D coveringRadius,
D knnDistance)
Provides a new MkMaxDirectoryEntry with the given parameters.
|
MkMaxLeafEntry(DBID objectID,
D parentDistance,
D knnDistance)
Provides a new MkMaxLeafEntry with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
void |
MkTabTreeNode.adjustEntry(MkTabEntry<D> entry,
DBID routingObjectID,
D parentDistance,
AbstractMTree<O,D,MkTabTreeNode<O,D>,MkTabEntry<D>> mTree) |
protected MkTabEntry<D> |
MkTabTree.createNewDirectoryEntry(MkTabTreeNode<O,D> node,
DBID routingObjectID,
D parentDistance)
Creates a new directory entry representing the specified node.
|
protected MkTabEntry<D> |
MkTabTreeIndex.createNewLeafEntry(DBID id,
O object,
D parentDistance)
Creates a new leaf entry representing the specified data object in the
specified subtree.
|
boolean |
MkTabTreeIndex.delete(DBID id)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
private void |
MkTabTree.doReverseKNNQuery(int k,
DBID q,
MkTabEntry<D> node_entry,
MkTabTreeNode<O,D> node,
List<DistanceResultPair<D>> result)
Performs a k-nearest neighbor query in the specified subtree for the given
query object and the given parameter k.
|
void |
MkTabTreeIndex.insert(DBID id) |
List<DistanceResultPair<D>> |
MkTabTree.reverseKNNQuery(DBID id,
int k) |
Modifier and Type | Method and Description |
---|---|
protected void |
MkTabTree.kNNdistanceAdjustment(MkTabEntry<D> entry,
Map<DBID,KNNHeap<D>> knnLists) |
Constructor and Description |
---|
MkTabDirectoryEntry(DBID objectID,
D parentDistance,
Integer nodeID,
D coveringRadius,
List<D> knnDistances)
Provides a new MkMaxDirectoryEntry with the given parameters.
|
MkTabLeafEntry(DBID objectID,
D parentDistance,
List<D> knnDistances)
Provides a new MkMaxLeafEntry with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
protected MTreeEntry<D> |
MTree.createNewDirectoryEntry(MTreeNode<O,D> node,
DBID routingObjectID,
D parentDistance) |
protected MTreeEntry<D> |
MTreeIndex.createNewLeafEntry(DBID id,
O object,
D parentDistance) |
boolean |
MTreeIndex.delete(DBID id)
Throws an UnsupportedOperationException since deletion of objects is not
yet supported by an M-Tree.
|
void |
MTreeIndex.insert(DBID id) |
Modifier and Type | Method and Description |
---|---|
private void |
MetricalIndexRangeQuery.doRangeQuery(DBID o_p,
AbstractMTreeNode<O,D,?,?> node,
DBID q,
D r_q,
List<DistanceResultPair<D>> result)
Performs a range query on the specified subtree.
|
private void |
MetricalIndexRangeQuery.doRangeQuery(DBID o_p,
AbstractMTreeNode<O,D,?,?> node,
O q,
D r_q,
List<DistanceResultPair<D>> result)
Performs a range query on the specified subtree.
|
List<DistanceResultPair<D>> |
MetricalIndexKNNQuery.getKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
MetricalIndexRangeQuery.getRangeForDBID(DBID id,
D range) |
List<DistanceResultPair<D>> |
MkTreeRKNNQuery.getRKNNForDBID(DBID id,
int k) |
Modifier and Type | Method and Description |
---|---|
void |
MetricalIndexKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
Modifier and Type | Field and Description |
---|---|
private DBID |
Assignments.id1
The id of the first routing object.
|
private DBID |
Assignments.id2
The id of the second routing object.
|
Modifier and Type | Method and Description |
---|---|
DBID |
Assignments.getFirstRoutingObject()
Returns the id of the first routing object.
|
DBID |
Assignments.getSecondRoutingObject()
Returns the id of the second routing object.
|
Modifier and Type | Method and Description |
---|---|
(package private) Assignments<D,E> |
MTreeSplit.balancedPartition(N node,
DBID routingObject1,
DBID routingObject2,
DistanceQuery<O,D> distanceFunction)
Creates a balanced partition of the entries of the specified node.
|
Constructor and Description |
---|
Assignments(DBID id1,
DBID id2,
D firstCoveringRadius,
D secondCoveringRadius,
Set<E> firstAssignments,
Set<E> secondAssignments)
Provides an assignment during a split of an MTree node.
|
Modifier and Type | Field and Description |
---|---|
DBID |
GenericMTreeDistanceSearchCandidate.routingObjectID
The id of the routing object.
|
Constructor and Description |
---|
GenericMTreeDistanceSearchCandidate(D mindist,
Integer nodeID,
DBID routingObjectID)
Creates a new heap node with the specified parameters.
|
Constructor and Description |
---|
SpatialPointLeafEntry(DBID id,
double[] values)
Constructs a new LeafEntry object with the given parameters.
|
SpatialPointLeafEntry(DBID id,
NumberVector<?,?> vector)
Constructor from number vector
|
Modifier and Type | Method and Description |
---|---|
protected IndexTreePath<E> |
AbstractRStarTree.findPathToObject(IndexTreePath<E> subtree,
SpatialComparable mbr,
DBID id)
Returns the path to the leaf entry in the specified subtree that represents
the data object with the specified mbr and id.
|
Modifier and Type | Method and Description |
---|---|
protected DeLiCluLeafEntry |
DeLiCluTreeIndex.createNewLeafEntry(DBID id)
Creates a new leaf entry representing the specified data object.
|
boolean |
DeLiCluTreeIndex.delete(DBID id)
Deletes the specified object from this index.
|
void |
DeLiCluTreeIndex.insert(DBID id)
Inserts the specified real vector object into this index.
|
List<TreeIndexPathComponent<DeLiCluEntry>> |
DeLiCluTreeIndex.setHandled(DBID id,
O obj)
Marks the specified object as handled and returns the path of node ids from
the root to the objects's parent.
|
Constructor and Description |
---|
DeLiCluLeafEntry(DBID id,
NumberVector<?,?> vector)
Constructs a new LeafEntry object with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
List<DistanceResultPair<DoubleDistance>> |
DoubleDistanceRStarTreeKNNQuery.getKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
GenericRStarTreeKNNQuery.getKNNForDBID(DBID id,
int k) |
List<DistanceResultPair<D>> |
GenericRStarTreeRangeQuery.getRangeForDBID(DBID id,
D range) |
List<DistanceResultPair<DoubleDistance>> |
DoubleDistanceRStarTreeRangeQuery.getRangeForDBID(DBID id,
DoubleDistance range) |
Modifier and Type | Method and Description |
---|---|
protected void |
GenericRStarTreeKNNQuery.batchNN(AbstractRStarTreeNode<?,?> node,
Map<DBID,KNNHeap<D>> knnLists)
Performs a batch knn query.
|
protected void |
DoubleDistanceRStarTreeKNNQuery.batchNN(AbstractRStarTreeNode<?,?> node,
Map<DBID,KNNHeap<DoubleDistance>> knnLists)
Performs a batch knn query.
|
void |
GenericRStarTreeKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<D>> heaps) |
void |
DoubleDistanceRStarTreeKNNQuery.getKNNForBulkHeaps(Map<DBID,KNNHeap<DoubleDistance>> heaps) |
Modifier and Type | Method and Description |
---|---|
protected SpatialPointLeafEntry |
RStarTreeIndex.createNewLeafEntry(DBID id)
Create a new leaf entry.
|
boolean |
RStarTreeIndex.delete(DBID id)
Deletes the specified object from this index.
|
void |
RStarTreeIndex.insert(DBID id)
Inserts the specified reel vector object into this index.
|
Modifier and Type | Method and Description |
---|---|
static ProjectedCentroid |
ProjectedCentroid.make(BitSet dims,
Relation<? extends NumberVector<?,?>> relation,
Iterable<DBID> ids)
Static Constructor from a relation.
|
static Centroid |
Centroid.make(Relation<? extends NumberVector<?,?>> relation,
Iterable<DBID> ids)
Static constructor from an existing relation.
|
static CovarianceMatrix |
CovarianceMatrix.make(Relation<? extends NumberVector<?,?>> relation,
Iterable<DBID> ids)
Static Constructor from a full relation.
|
Modifier and Type | Method and Description |
---|---|
IterableIterator<DBID> |
OrderingFromDataStore.iter(DBIDs ids) |
IterableIterator<DBID> |
OrderingResult.iter(DBIDs ids)
Sort the given ids according to this ordering and return an iterator.
|
Modifier and Type | Method and Description |
---|---|
int |
OrderingFromDataStore.ImpliedComparator.compare(DBID id1,
DBID id2) |
int |
OrderingFromDataStore.DerivedComparator.compare(DBID id1,
DBID id2) |
private StringBuffer |
KMLOutputHandler.makeDescription(Collection<Relation<?>> relations,
DBID id)
Make an HTML description.
|
Modifier and Type | Field and Description |
---|---|
private DBID |
GenericClusterOrderEntry.objectID
The id of the entry.
|
private DBID |
DoubleDistanceClusterOrderEntry.objectID
The id of the entry.
|
private DBID |
GenericClusterOrderEntry.predecessorID
The id of the entry's predecessor.
|
private DBID |
DoubleDistanceClusterOrderEntry.predecessorID
The id of the entry's predecessor.
|
Modifier and Type | Field and Description |
---|---|
private HashMap<DBID,ClusterOrderEntry<D>> |
ClusterOrderResult.map
Map of object IDs to their cluster order entry
|
private HashMap<DBID,ClusterOrderEntry<D>> |
ClusterOrderResult.ReachabilityDistanceAdapter.map
Access reference.
|
private HashMap<DBID,ClusterOrderEntry<D>> |
ClusterOrderResult.PredecessorAdapter.map
Access reference.
|
Modifier and Type | Method and Description |
---|---|
DBID |
ClusterOrderResult.PredecessorAdapter.get(DBID objID) |
DBID |
GenericClusterOrderEntry.getID()
Returns the object id of this entry.
|
DBID |
DoubleDistanceClusterOrderEntry.getID()
Returns the object id of this entry.
|
DBID |
ClusterOrderEntry.getID()
Returns the object id of this entry.
|
DBID |
GenericClusterOrderEntry.getPredecessorID()
Returns the id of the predecessor of this entry if this entry has a
predecessor, null otherwise.
|
DBID |
DoubleDistanceClusterOrderEntry.getPredecessorID()
Returns the id of the predecessor of this entry if this entry has a
predecessor, null otherwise.
|
DBID |
ClusterOrderEntry.getPredecessorID()
Returns the id of the predecessor of this entry if this entry has a
predecessor, null otherwise.
|
Modifier and Type | Method and Description |
---|---|
SimpleTypeInformation<DBID> |
ClusterOrderResult.PredecessorAdapter.getDataTypeInformation() |
IterableIterator<DBID> |
ClusterOrderResult.ClusterOrderAdapter.iter(DBIDs ids)
Use the cluster order to sort the given collection ids.
|
IterableIterator<DBID> |
ClusterOrderResult.ReachabilityDistanceAdapter.iterDBIDs() |
IterableIterator<DBID> |
ClusterOrderResult.PredecessorAdapter.iterDBIDs() |
Modifier and Type | Method and Description |
---|---|
void |
ClusterOrderResult.add(DBID id,
DBID predecessor,
D reachability)
Add an object to the cluster order.
|
void |
ClusterOrderResult.ReachabilityDistanceAdapter.delete(DBID id) |
void |
ClusterOrderResult.PredecessorAdapter.delete(DBID id) |
D |
ClusterOrderResult.ReachabilityDistanceAdapter.get(DBID objID) |
DBID |
ClusterOrderResult.PredecessorAdapter.get(DBID objID) |
void |
ClusterOrderResult.ReachabilityDistanceAdapter.set(DBID id,
D val) |
void |
ClusterOrderResult.PredecessorAdapter.set(DBID id,
DBID val) |
Constructor and Description |
---|
DoubleDistanceClusterOrderEntry(DBID objectID,
DBID predecessorID,
double reachability)
Creates a new entry in a cluster order with the specified parameters.
|
GenericClusterOrderEntry(DBID objectID,
DBID predecessorID,
D reachability)
Creates a new entry in a cluster order with the specified parameters.
|
Constructor and Description |
---|
ClusterOrderResult.PredecessorAdapter(HashMap<DBID,ClusterOrderEntry<D>> map,
DBIDs dbids)
Constructor.
|
ClusterOrderResult.ReachabilityDistanceAdapter(HashMap<DBID,ClusterOrderEntry<D>> map,
DBIDs dbids)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
IterableIterator<DBID> |
OrderingFromRelation.iter(DBIDs ids) |
Modifier and Type | Method and Description |
---|---|
int |
OrderingFromRelation.ImpliedComparator.compare(DBID id1,
DBID id2) |
Modifier and Type | Method and Description |
---|---|
private void |
TextWriter.printObject(TextWriterStream out,
Database db,
DBID objID,
List<Relation<?>> ra) |
Modifier and Type | Field and Description |
---|---|
(package private) Iterator<DBID> |
DatabaseUtil.RelationObjectIterator.iter
The real iterator.
|
Constructor and Description |
---|
DatabaseUtil.RelationObjectIterator(Iterator<DBID> iter,
Relation<? extends O> database)
Full Constructor.
|
Modifier and Type | Method and Description |
---|---|
DBID |
KNNList.DBIDView.get(int i) |
DBID |
KNNList.DBIDItr.next() |
Modifier and Type | Method and Description |
---|---|
Collection<DBID> |
KNNList.DBIDView.asCollection() |
Iterator<DBID> |
KNNList.DBIDView.iterator() |
Modifier and Type | Method and Description |
---|---|
boolean |
KNNHeap.add(D distance,
DBID id)
Add a distance-id pair to the heap unless the distance is too large.
|
Constructor and Description |
---|
ObjectNotFoundException(DBID id)
Object
|
Modifier and Type | Field and Description |
---|---|
private HashMap<DBID,Integer> |
OPTICSColorFromClustering.idToColor
The final mapping of object IDs to colors.
|
Modifier and Type | Method and Description |
---|---|
protected Double |
BubbleVisualization.getScaledForId(DBID id)
Convenience method to apply scalings in the right order.
|
protected Element |
TooltipScoreVisualization.makeTooltip(DBID id,
double x,
double y,
double dotsize) |
protected abstract Element |
AbstractTooltipVisualization.makeTooltip(DBID id,
double x,
double y,
double dotsize) |
protected Element |
TooltipStringVisualization.makeTooltip(DBID id,
double x,
double y,
double dotsize) |