Package | Description |
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gov.sandia.cognition.learning.algorithm.clustering |
Provides clustering algorithms.
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gov.sandia.cognition.learning.algorithm.clustering.cluster |
Provides implementations of different types of clusters.
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gov.sandia.cognition.learning.algorithm.clustering.divergence |
Provides divergence functions for use in clustering.
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gov.sandia.cognition.learning.algorithm.clustering.initializer |
Provides implementations of methods for selecting initial clusters.
|
gov.sandia.cognition.learning.function.vector |
Provides functions that output vectors.
|
gov.sandia.cognition.statistics.distribution |
Provides statistical distributions.
|
Modifier and Type | Method and Description |
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java.util.ArrayList<GaussianCluster> |
DirichletProcessClustering.getResult() |
java.util.ArrayList<GaussianCluster> |
DirichletProcessClustering.learn(java.util.Collection<? extends Vector> data) |
Modifier and Type | Method and Description |
---|---|
GaussianCluster |
GaussianClusterCreator.createCluster(java.util.Collection<? extends Vector> members)
Creates a GaussianCluster from a given set of vectors by fitting a
Gaussian to those vectors.
|
Modifier and Type | Method and Description |
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double |
GaussianClusterDivergenceFunction.evaluate(GaussianCluster first,
Vector second)
Evaluates the divergence between the Gaussian cluster and the given
vector, which is the negative of likelihood that the cluster was
generated.
|
Modifier and Type | Method and Description |
---|---|
java.util.ArrayList<GaussianCluster> |
NeighborhoodGaussianClusterInitializer.initializeClusters(int numClusters,
java.util.Collection<? extends Vector> elements) |
Modifier and Type | Method and Description |
---|---|
void |
GaussianContextRecognizer.consumeClusters(java.util.Collection<GaussianCluster> clusters)
Uses the given clusters to populate the internal clusters of this
|
Constructor and Description |
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GaussianContextRecognizer(java.util.Collection<GaussianCluster> clusters)
Creates a new instance of GaussianContextRecognizer
|
Learner(KMeansClusterer<Vector,GaussianCluster> algorithm)
Creates a new instance of Learner
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Constructor and Description |
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Learner(KMeansClusterer<Vector,GaussianCluster> algorithm)
Creates a new Learner
|