Package | Description |
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de.lmu.ifi.dbs.elki.data.model |
Cluster models classes for various algorithms.
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de.lmu.ifi.dbs.elki.distance.distancefunction.correlation |
Distance functions using correlations.
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de.lmu.ifi.dbs.elki.distance.distancefunction.subspace |
Distance functions based on subspaces.
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de.lmu.ifi.dbs.elki.index.preprocessed.localpca |
Index using a preprocessed local PCA.
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de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj |
Index using a preprocessed local subspaces.
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de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
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Modifier and Type | Field and Description |
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private PCAFilteredResult |
CorrelationModel.pcaresult
The computed PCA result of this cluster.
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Modifier and Type | Method and Description |
---|---|
PCAFilteredResult |
CorrelationModel.getPCAResult()
Get assigned PCA result
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Modifier and Type | Method and Description |
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void |
CorrelationModel.setPCAResult(PCAFilteredResult pcaresult)
Assign new PCA result
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Constructor and Description |
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CorrelationModel(PCAFilteredResult pcaresult,
V centroid)
Constructor
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Modifier and Type | Method and Description |
---|---|
private boolean |
ERiCDistanceFunction.approximatelyLinearDependent(PCAFilteredResult pca1,
PCAFilteredResult pca2)
Returns true, if the strong eigenvectors of the two specified pcas span up
the same space.
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int |
PCABasedCorrelationDistanceFunction.Instance.correlationDistance(PCAFilteredResult pca1,
PCAFilteredResult pca2,
int dimensionality)
Computes the correlation distance between the two subspaces defined by
the specified PCAs.
|
BitDistance |
ERiCDistanceFunction.distance(NumberVector<?,?> v1,
NumberVector<?,?> v2,
PCAFilteredResult pca1,
PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this
distance function.
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Modifier and Type | Method and Description |
---|---|
SubspaceDistance |
LocalSubspaceDistanceFunction.Instance.distance(V o1,
V o2,
PCAFilteredResult pca1,
PCAFilteredResult pca2)
Computes the distance between two given DatabaseObjects according to this
distance function.
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Modifier and Type | Method and Description |
---|---|
PCAFilteredResult |
AbstractFilteredPCAIndex.getLocalProjection(DBIDRef objid) |
PCAFilteredResult |
FilteredLocalPCAIndex.getLocalProjection(DBIDRef objid)
Get the precomputed local PCA for a particular object ID.
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Modifier and Type | Method and Description |
---|---|
protected PCAFilteredResult |
FourCSubspaceIndex.computeProjection(DBIDRef id,
DistanceDBIDResult<D> neighbors,
Relation<V> database) |
Modifier and Type | Method and Description |
---|---|
PCAFilteredResult |
PCAFilteredRunner.processCovarMatrix(Matrix covarMatrix)
Process an existing Covariance Matrix
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PCAFilteredResult |
PCAFilteredRunner.processEVD(EigenvalueDecomposition evd)
Process an existing eigenvalue decomposition
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PCAFilteredResult |
PCAFilteredAutotuningRunner.processIds(DBIDs ids,
Relation<? extends V> database) |
PCAFilteredResult |
PCAFilteredRunner.processIds(DBIDs ids,
Relation<? extends V> database)
Run PCA on a collection of database IDs
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<D extends NumberDistance<?,?>> |
PCAFilteredAutotuningRunner.processQueryResult(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database) |
<D extends NumberDistance<?,?>> |
PCAFilteredRunner.processQueryResult(Collection<? extends DistanceResultPair<D>> results,
Relation<? extends V> database)
Run PCA on a QueryResult Collection
|