gov.sandia.cognition.learning.algorithm.clustering.initializer

Class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer<DataType,DataType>

gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer<ClusterType,DataType>

Type Parameters:

ClusterType - Type of Cluster<DataType> used in theaceous learn() method.

DataType - The algorithm operates on a Collection<DataType>, so DataType will be something like Vector or String.

All Implemented Interfaces:

FixedClusterInitializer<ClusterType,DataType>, DivergenceFunctionContainer<DataType,DataType>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

DistanceSamplingClusterInitializer, GreedyClusterInitializer

public abstract class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
extends DefaultDivergenceFunctionContainer<DataType,DataType>
implements FixedClusterInitializer<ClusterType,DataType>, Randomized

Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.

Since:

3.1

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected ClusterCreator<ClusterType,DataType>	creator The ClusterCreator to create the initial clusters from.
protected java.util.Random	random The random number generator to use.

Fields inherited from class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer

divergenceFunction

Constructor Summary

Constructors

Constructor and Description
AbstractMinDistanceFixedClusterInitializer() Creates a new, empty instance of AbstractMinDistanceFixedClusterInitializer.
AbstractMinDistanceFixedClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, java.util.Random random) Creates a new instance of AbstractMinDistanceFixedClusterInitializer.

Method Summary

Modifier and Type	Method and Description
AbstractMinDistanceFixedClusterInitializer<ClusterType,DataType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
ClusterCreator<ClusterType,DataType>	getCreator() Gets the cluster creator used to create the initial clusters.
java.util.Random	getRandom() Gets the random number generator used by this object.
java.util.ArrayList<ClusterType>	initializeClusters(int numClusters, java.util.Collection<? extends DataType> elements) Initializes a given number of clusters from the given elements using the greedy initialization algorithm.
protected abstract int	selectNextClusterIndex(double[] minDistances, boolean[] selected) Select the index for the next cluster based on the given minimum distances and array indicating which clusters have already been selected.
void	setCreator(ClusterCreator<ClusterType,DataType> creator) Sets the cluster creator used to create the initial clusters.
void	setRandom(java.util.Random random) Sets the random number generator used by this object.

Methods inherited from class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer

getDivergenceFunction, setDivergenceFunction

Methods inherited from class java.lang.Object

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

Field Detail

creator

protected ClusterCreator<ClusterType extends Cluster<DataType>,DataType> creator

The ClusterCreator to create the initial clusters from.

random

protected java.util.Random random

The random number generator to use.

Constructor Detail

AbstractMinDistanceFixedClusterInitializer

public AbstractMinDistanceFixedClusterInitializer()

Creates a new, empty instance of AbstractMinDistanceFixedClusterInitializer.

AbstractMinDistanceFixedClusterInitializer

public AbstractMinDistanceFixedClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction,
                                                  ClusterCreator<ClusterType,DataType> creator,
                                                  java.util.Random random)

Creates a new instance of AbstractMinDistanceFixedClusterInitializer.

Parameters:

divergenceFunction - The divergence function to use.

creator - The cluster creator to use.

random - The random number generator to use.

Method Detail

clone

public AbstractMinDistanceFixedClusterInitializer<ClusterType,DataType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class DefaultDivergenceFunctionContainer<DataType,DataType>

Returns:

A clone of this object.

initializeClusters

public java.util.ArrayList<ClusterType> initializeClusters(int numClusters,
                                                           java.util.Collection<? extends DataType> elements)

Initializes a given number of clusters from the given elements using the greedy initialization algorithm.

Specified by:

initializeClusters in interface FixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>

Parameters:

numClusters - The number of clusters to create.

elements - The elements to create the clusters from.

Returns:

The initial clusters to use.

Throws:

java.lang.IllegalArgumentException - If numClusters is less than 0.

java.lang.NullPointerException - If elements is null.

selectNextClusterIndex

protected abstract int selectNextClusterIndex(double[] minDistances,
                                              boolean[] selected)

Select the index for the next cluster based on the given minimum distances and array indicating which clusters have already been selected.

Parameters:

minDistances - The array of minimum distances.

selected - The array corresponding to whether or not an item has already been selected.

Returns:

The index of the next cluster to include. -1 means that there is nothing left to include.

getCreator

public ClusterCreator<ClusterType,DataType> getCreator()

Gets the cluster creator used to create the initial clusters.

Returns:

The cluster creator.

setCreator

public void setCreator(ClusterCreator<ClusterType,DataType> creator)

Sets the cluster creator used to create the initial clusters.

Parameters:

creator - The new cluster creator.

getRandom

public java.util.Random getRandom()

Description copied from interface: Randomized

Gets the random number generator used by this object.

Specified by:

getRandom in interface Randomized

Returns:

The random number generator used by this object.

setRandom

public void setRandom(java.util.Random random)

Description copied from interface: Randomized

Sets the random number generator used by this object.

Specified by:

setRandom in interface Randomized

Parameters:

random - The random number generator for this object to use.