org.openimaj.ml.clustering.assignment.hard

Class HierarchicalDoubleHardAssigner

java.lang.Object

org.openimaj.ml.clustering.assignment.hard.HierarchicalDoubleHardAssigner

All Implemented Interfaces:

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

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

The HierarchicalDoubleHardAssigner is a HardAssigner for HierarchicalDoubleKMeansResult instances. The assigner produces the index of the assigned leaf node as if the clusters were actually flat.

Author:

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

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HierarchicalDoubleHardAssigner.ScoringScheme The HierarchicalDoubleHardAssigner.ScoringScheme determines how the distance to a cluster is estimated from the hierarchy of k-means generated clusters.

Field Summary

Fields

Modifier and Type	Field and Description
protected HierarchicalDoublePathAssigner	path
protected HierarchicalDoubleKMeansResult	result
protected HierarchicalDoubleHardAssigner.ScoringScheme	scorer

Constructor Summary

Constructors

Constructor and Description
HierarchicalDoubleHardAssigner(HierarchicalDoubleKMeansResult result) Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.
HierarchicalDoubleHardAssigner(HierarchicalDoubleKMeansResult result, HierarchicalDoubleHardAssigner.ScoringScheme scorer) Construct with the given hierarchical KMeans clusterer and scoring scheme.

Method Summary

Modifier and Type	Method and Description
int	assign(double[] data) Assign a single point to a cluster.
int[]	assign(double[][] data) Assign data to a cluster.
IntDoublePair	assignDistance(double[] data) Assign a single point to a cluster.
void	assignDistance(double[][] 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

result

protected HierarchicalDoubleKMeansResult result

path

protected HierarchicalDoublePathAssigner path

scorer

protected HierarchicalDoubleHardAssigner.ScoringScheme scorer

Constructor Detail

HierarchicalDoubleHardAssigner

public HierarchicalDoubleHardAssigner(HierarchicalDoubleKMeansResult result,
                                      HierarchicalDoubleHardAssigner.ScoringScheme scorer)

Construct with the given hierarchical KMeans clusterer and scoring scheme.

Parameters:

result - the hierarchical KMeans clusterer

scorer - the scoring scheme

HierarchicalDoubleHardAssigner

public HierarchicalDoubleHardAssigner(HierarchicalDoubleKMeansResult result)

Construct with the given Hierarchical KMeans clusterer and the SUM scoring scheme.

Parameters:

result - the hierarchical KMeans clusterer

Method Detail

assign

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

Description copied from interface: HardAssigner

Assign data to a cluster.

Specified by:

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

Parameters:

data - the data.

Returns:

The cluster indices which the data was assigned to.

assign

public int assign(double[] data)

Description copied from interface: HardAssigner

Assign a single point to a cluster.

Specified by:

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

Parameters:

data - datum to assign.

Returns:

the cluster index.

assignDistance

public void assignDistance(double[][] 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<double[],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(double[] data)

Description copied from interface: HardAssigner

Assign a single point to a cluster.

Specified by:

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

Returns:

the number of dimensions