org.openimaj.ml.clustering.assignment.hard

Class KDTreeLongEuclideanAssigner

java.lang.Object

org.openimaj.ml.clustering.assignment.hard.KDTreeLongEuclideanAssigner

All Implemented Interfaces:

Assigner<long[]>, HardAssigner<long[],double[],IntDoublePair>

public class KDTreeLongEuclideanAssigner
extends Object
implements HardAssigner<long[],double[],IntDoublePair>

A HardAssigner that uses a LongNearestNeighboursKDTree to generate approximately correct cluster assignments.

Author:

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

Field Summary

Fields

Modifier and Type	Field and Description
protected LongNearestNeighboursKDTree	nn

Constructor Summary

Constructors

Constructor and Description
KDTreeLongEuclideanAssigner(CentroidsProvider<long[]> provider) Construct the assigner using the given cluster data.
KDTreeLongEuclideanAssigner(long[][] data) Construct the assigner using the given cluster data.

Method Summary

Modifier and Type	Method and Description
int	assign(long[] data) Assign a single point to a cluster.
int[]	assign(long[][] data) Assign data to a cluster.
IntDoublePair	assignDistance(long[] data) Assign a single point to a cluster.
void	assignDistance(long[][] data, int[] indices, double[] distances) Assign data to clusters.
int	numDimensions() Get the number of dimensions of the input vectors.
int	size() The number of centroids or unique ids that can be generated.

Methods inherited from class java.lang.Object

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

Field Detail

nn

protected LongNearestNeighboursKDTree nn

Constructor Detail

KDTreeLongEuclideanAssigner

public KDTreeLongEuclideanAssigner(CentroidsProvider<long[]> provider)

Construct the assigner using the given cluster data.

Parameters:

provider - the cluster data provider

KDTreeLongEuclideanAssigner

public KDTreeLongEuclideanAssigner(long[][] data)

Construct the assigner using the given cluster data.

Parameters:

data - the cluster data

Method Detail

assign

public int[] assign(long[][] data)

Description copied from interface: HardAssigner

Assign data to a cluster.

Specified by:

assign in interface HardAssigner<long[],double[],IntDoublePair>

Parameters:

data - the data.

Returns:

The cluster indices which the data was assigned to.

assign

public int assign(long[] data)

Description copied from interface: HardAssigner

Assign a single point to a cluster.

Specified by:

assign in interface HardAssigner<long[],double[],IntDoublePair>

Parameters:

data - datum to assign.

Returns:

the cluster index.

assignDistance

public void assignDistance(long[][] data,
                           int[] indices,
                           double[] distances)

Description copied from interface: HardAssigner

Assign data to clusters. The results are returned in the indices and distances arrays. The return arrays must have the same length as the data array.

Specified by:

assignDistance in interface HardAssigner<long[],double[],IntDoublePair>

Parameters:

data - the data.

indices - the cluster index for each data point.

distances - the distance to the closest cluster for each data point.

assignDistance

public IntDoublePair assignDistance(long[] data)

Description copied from interface: HardAssigner

Assign a single point to a cluster.

Specified by:

assignDistance in interface HardAssigner<long[],double[],IntDoublePair>

Parameters:

data - point to assign.

Returns:

the cluster index and distance.

size

public int size()

Description copied from interface: HardAssigner

The number of centroids or unique ids that can be generated.

Specified by:

size in interface HardAssigner<long[],double[],IntDoublePair>

Returns:

The number of centroids or unique ids that can be generated.

numDimensions

public int numDimensions()

Description copied from interface: Assigner

Get the number of dimensions of the input vectors.

Specified by:

numDimensions in interface Assigner<long[]>

Returns:

the number of dimensions