MOA 12.03
Real Time Analytics for Data Streams
moa.cluster.Clustering Class Reference

Represents a collection of clusters. More...

Inheritance diagram for moa.cluster.Clustering:
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List of all members.

Public Member Functions

 Clustering ()
 Clustering (Cluster[] clusters)
 Clustering (List<?extends Instance > points)
 Clustering (ArrayList< DataPoint > points, double overlapThreshold, int initMinPoints)
 Clustering (AutoExpandVector< Cluster > clusters)
void add (Cluster cluster)
 add a cluster to the clustering
void remove (int index)
 remove a cluster from the clustering
Cluster get (int index)
 remove a cluster from the clustering
AutoExpandVector< ClustergetClustering ()
AutoExpandVector< ClustergetClusteringCopy ()
int size ()
int dimension ()
void getDescription (StringBuilder sb, int i)
 Returns a string representation of this object.
double getMaxInclusionProbability (Instance point)

Static Public Member Functions

static HashMap< Integer, Integer > classValues (List<?extends Instance > points)

Detailed Description

Represents a collection of clusters.

Definition at line 34 of file Clustering.java.


Constructor & Destructor Documentation

moa.cluster.Clustering.Clustering ( )

Definition at line 38 of file Clustering.java.

moa.cluster.Clustering.Clustering ( Cluster[]  clusters)

Definition at line 42 of file Clustering.java.

moa.cluster.Clustering.Clustering ( List<?extends Instance >  points)

Definition at line 49 of file Clustering.java.

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moa.cluster.Clustering.Clustering ( ArrayList< DataPoint points,
double  overlapThreshold,
int  initMinPoints 
)

Definition at line 75 of file Clustering.java.

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moa.cluster.Clustering.Clustering ( AutoExpandVector< Cluster clusters)

Definition at line 188 of file Clustering.java.


Member Function Documentation

static HashMap<Integer, Integer> moa.cluster.Clustering.classValues ( List<?extends Instance >  points) [static]
Parameters:
points
Returns:
an array with the min and max class label value

Definition at line 167 of file Clustering.java.

Referenced by moa.cluster.Clustering.Clustering(), and moa.evaluation.MembershipMatrix.MembershipMatrix().

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int moa.cluster.Clustering.dimension ( )
Returns:
the number of dimensions of this clustering

Definition at line 244 of file Clustering.java.

AutoExpandVector<Cluster> moa.cluster.Clustering.getClustering ( )
Returns:
the Clustering as an AutoExpandVector

Definition at line 222 of file Clustering.java.

Referenced by moa.clusterers.denstream.DenStream.trainOnInstanceImpl().

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AutoExpandVector<Cluster> moa.cluster.Clustering.getClusteringCopy ( )
Returns:
A deepcopy of the Clustering as an AutoExpandVector

Definition at line 229 of file Clustering.java.

void moa.cluster.Clustering.getDescription ( StringBuilder  sb,
int  indent 
)

Returns a string representation of this object.

Used in AbstractMOAObject.toString to give a string representation of the object.

Parameters:
sbthe stringbuilder to add the description
indentthe number of characters to indent

Implements moa.MOAObject.

Definition at line 250 of file Clustering.java.

double moa.cluster.Clustering.getMaxInclusionProbability ( Instance  point)

Definition at line 256 of file Clustering.java.

void moa.cluster.Clustering.remove ( int  index)

remove a cluster from the clustering

Definition at line 203 of file Clustering.java.


The documentation for this class was generated from the following file:
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