org.openimaj.ml.clustering.kmeans

Class SphericalKMeansResult

java.lang.Object

org.openimaj.ml.clustering.kmeans.SphericalKMeansResult

All Implemented Interfaces:

Readable, ReadableASCII, ReadableBinary, ReadWriteable, Writeable, WriteableASCII, WriteableBinary, CentroidsProvider<double[]>, Clusters, SpatialClusters<double[]>

public class SphericalKMeansResult
extends Object
implements SpatialClusters<double[]>, CentroidsProvider<double[]>

The result of a SpatialClusterer that just produces a flat set of centroids.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Field Summary

Fields

Modifier and Type	Field and Description
int[]	assignments The assignments of the training data to clusters
double[][]	centroids The centroids of the clusters

Fields inherited from interface org.openimaj.ml.clustering.Clusters

CLUSTER_HEADER

Constructor Summary

Constructors

Constructor and Description
SphericalKMeansResult()

Method Summary

Modifier and Type	Method and Description
String	asciiHeader()
byte[]	binaryHeader()
HardAssigner<double[],double[],IntDoublePair>	defaultHardAssigner() Get the default hard assigner for this clusterer.
boolean	equals(Object obj)
double[][]	filter(int threshold) Filter the cluster centroids be removing those with less than threshold items
int[]	getAssignmentHistogram() Compute the histogram of number of assignments to each cluster
double[][]	getCentroids()
int	numClusters() Get the number of clusters.
int	numDimensions() Get the data dimensionality
void	readASCII(Scanner br)
void	readBinary(DataInput in)
String	toString()
void	writeASCII(PrintWriter writer)
void	writeBinary(DataOutput out)

Methods inherited from class java.lang.Object

clone, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

centroids

public double[][] centroids

The centroids of the clusters

assignments

public int[] assignments

The assignments of the training data to clusters

Constructor Detail

SphericalKMeansResult

public SphericalKMeansResult()

Method Detail

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

asciiHeader

public String asciiHeader()

Specified by:

asciiHeader in interface ReadableASCII

Specified by:

asciiHeader in interface WriteableASCII

binaryHeader

public byte[] binaryHeader()

Specified by:

binaryHeader in interface ReadableBinary

Specified by:

binaryHeader in interface WriteableBinary

readASCII

public void readASCII(Scanner br)
               throws IOException

Specified by:

readASCII in interface ReadableASCII

Throws:

IOException

readBinary

public void readBinary(DataInput in)
                throws IOException

Specified by:

readBinary in interface ReadableBinary

Throws:

IOException

writeASCII

public void writeASCII(PrintWriter writer)
                throws IOException

Specified by:

writeASCII in interface WriteableASCII

Throws:

IOException

writeBinary

public void writeBinary(DataOutput out)
                 throws IOException

Specified by:

writeBinary in interface WriteableBinary

Throws:

IOException

toString

public String toString()

Overrides:

toString in class Object

getCentroids

public double[][] getCentroids()

Specified by:

getCentroids in interface CentroidsProvider<double[]>

Returns:

the centroids generated by the clustering

defaultHardAssigner

public HardAssigner<double[],double[],IntDoublePair> defaultHardAssigner()

Description copied from interface: SpatialClusters

Get the default hard assigner for this clusterer. This method is potentially expensive, so callers should only call it once, and hold on to the result (and reuse it).

Specified by:

defaultHardAssigner in interface SpatialClusters<double[]>

Returns:

a hard assigner.

numDimensions

public int numDimensions()

Description copied from interface: SpatialClusters

Get the data dimensionality

Specified by:

numDimensions in interface SpatialClusters<double[]>

Returns:

the data dimensionality.

numClusters

public int numClusters()

Description copied from interface: SpatialClusters

Get the number of clusters.

Specified by:

numClusters in interface SpatialClusters<double[]>

Returns:

number of clusters.

getAssignmentHistogram

public int[] getAssignmentHistogram()

Compute the histogram of number of assignments to each cluster

Returns:

the histogram

filter

public double[][] filter(int threshold)

Filter the cluster centroids be removing those with less than threshold items

Parameters:

threshold - minimum number of items

Returns:

the filtered clusters