jsat.clustering

Class EMGaussianMixture

java.lang.Object

jsat.clustering.ClustererBase

jsat.clustering.KClustererBase

jsat.clustering.EMGaussianMixture

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, KClusterer, MultivariateDistribution

public class EMGaussianMixture
extends KClustererBase
implements MultivariateDistribution

An implementation of Gaussian Mixture models that learns the specified number of Gaussians using Expectation Maximization algorithm.

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected int	MaxIterLimit Control the maximum number of iterations to perform.

Constructor Summary

Constructors

Constructor and Description
EMGaussianMixture()
EMGaussianMixture(EMGaussianMixture gm) Copy constructor.
EMGaussianMixture(SeedSelectionMethods.SeedSelection seedSelection)

Method Summary

Modifier and Type	Method and Description
EMGaussianMixture	clone()
int[]	cluster(DataSet dataSet, ExecutorService threadpool, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int[] designations) Performs clustering on the given data set.
int[]	cluster(DataSet dataSet, int clusters, ExecutorService threadpool, int[] designations)
int[]	cluster(DataSet dataSet, int clusters, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, ExecutorService threadpool, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)
protected double	cluster(DataSet dataSet, List<Double> accelCache, int K, List<Vec> means, int[] assignment, boolean exactTotal, ExecutorService threadpool, boolean returnError)
protected double	clusterCompute(int K, DataSet dataSet, int[] assignment, List<Vec> means, List<Matrix> covs, ExecutorService execServ)
int	getIterationLimit() Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.
SeedSelectionMethods.SeedSelection	getSeedSelection()
double	logPdf(double... x) Computes the log of the probability density function.
double	logPdf(Vec x) Computes the log of the probability density function.
double	pdf(double... x) Returns the probability of a given vector from this distribution.
double	pdf(Vec x) Returns the probability of a given vector from this distribution.
List<Vec>	sample(int count, Random rand) Performs sampling on the current distribution.
void	setIterationLimit(int iterLimit) Sets the maximum number of iterations allowed
void	setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection) Sets the method of seed selection to use for this algorithm.
boolean	setUsingData(DataSet dataSet) Sets the parameters of the distribution to attempt to fit the given list of data points.
boolean	setUsingData(DataSet dataSet, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of data points.
<V extends Vec> boolean	setUsingData(List<V> dataSet) Sets the parameters of the distribution to attempt to fit the given list of vectors.
<V extends Vec> boolean	setUsingData(List<V> dataSet, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of vectors.
boolean	setUsingDataList(List<DataPoint> dataPoint) Sets the parameters of the distribution to attempt to fit the given list of data points.
boolean	setUsingDataList(List<DataPoint> dataPoints, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of data points.

Methods inherited from class jsat.clustering.KClustererBase

cluster, cluster, cluster, cluster

Methods inherited from class jsat.clustering.ClustererBase

cluster, cluster, createClusterListFromAssignmentArray, getDatapointsFromCluster, supportsWeightedData

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, supportsWeightedData

Field Detail

MaxIterLimit

protected int MaxIterLimit

Control the maximum number of iterations to perform.

Constructor Detail

EMGaussianMixture

public EMGaussianMixture(SeedSelectionMethods.SeedSelection seedSelection)

EMGaussianMixture

public EMGaussianMixture()

EMGaussianMixture

public EMGaussianMixture(EMGaussianMixture gm)

Copy constructor. The new Gaussian Mixture can be altered without effecting gm

Parameters:

gm - the Guassian Mixture to duplicate

Method Detail

setSeedSelection

public void setSeedSelection(SeedSelectionMethods.SeedSelection seedSelection)

Sets the method of seed selection to use for this algorithm. SeedSelectionMethods.SeedSelection.KPP is recommended for this algorithm in particular.

Parameters:

seedSelection - the method of seed selection to use

getSeedSelection

public SeedSelectionMethods.SeedSelection getSeedSelection()

Returns:

the method of seed selection used

setIterationLimit

public void setIterationLimit(int iterLimit)

Sets the maximum number of iterations allowed

Parameters:

iterLimit - the maximum number of iterations of the ElkanKMeans algorithm

getIterationLimit

public int getIterationLimit()

Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.

Returns:

the maximum number of iterations of the ElkanKMeans algorithm that will be performed.

cluster

protected double cluster(DataSet dataSet,
                         List<Double> accelCache,
                         int K,
                         List<Vec> means,
                         int[] assignment,
                         boolean exactTotal,
                         ExecutorService threadpool,
                         boolean returnError)

clusterCompute

protected double clusterCompute(int K,
                                DataSet dataSet,
                                int[] assignment,
                                List<Vec> means,
                                List<Matrix> covs,
                                ExecutorService execServ)

logPdf

public double logPdf(double... x)

Description copied from interface: MultivariateDistribution

Computes the log of the probability density function. If the probability of the input is zero, the log of zero would be Double.NEGATIVE_INFINITY. Instead, -Double.MAX_VALUE is returned.

Specified by:

logPdf in interface MultivariateDistribution

Parameters:

x - the array for the vector the get the log probability of

Returns:

the log of the probability.

logPdf

public double logPdf(Vec x)

Description copied from interface: MultivariateDistribution

Computes the log of the probability density function. If the probability of the input is zero, the log of zero would be Double.NEGATIVE_INFINITY. Instead, -Double.MAX_VALUE is returned.

Specified by:

logPdf in interface MultivariateDistribution

Parameters:

x - the vector the get the log probability of

Returns:

the log of the probability.

pdf

public double pdf(double... x)

Description copied from interface: MultivariateDistribution

Returns the probability of a given vector from this distribution. By definition, the probability will always be in the range [0, 1].

Specified by:

pdf in interface MultivariateDistribution

Parameters:

x - the array of the vector the get the log probability of

Returns:

the probability

pdf

public double pdf(Vec x)

Description copied from interface: MultivariateDistribution

Returns the probability of a given vector from this distribution. By definition, the probability will always be in the range [0, 1].

Specified by:

pdf in interface MultivariateDistribution

Parameters:

x - the vector the get the log probability of

Returns:

the probability

setUsingData

public <V extends Vec> boolean setUsingData(List<V> dataSet)

Description copied from interface: MultivariateDistribution

Sets the parameters of the distribution to attempt to fit the given list of vectors. All vectors are assumed to have the same weight.

Specified by:

setUsingData in interface MultivariateDistribution

Type Parameters:

V - the vector type

Parameters:

dataSet - the list of data points

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

setUsingDataList

public boolean setUsingDataList(List<DataPoint> dataPoint)

Description copied from interface: MultivariateDistribution

Sets the parameters of the distribution to attempt to fit the given list of data points. The weights of the data points will be used.

Specified by:

setUsingDataList in interface MultivariateDistribution

Parameters:

dataPoint - the list of data points to use

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

setUsingData

public boolean setUsingData(DataSet dataSet)

Description copied from interface: MultivariateDistribution

Sets the parameters of the distribution to attempt to fit the given list of data points. The weights of the data points will be used.

Specified by:

setUsingData in interface MultivariateDistribution

Parameters:

dataSet - the data set to use

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

setUsingData

public boolean setUsingData(DataSet dataSet,
                            ExecutorService threadpool)

Description copied from interface: MultivariateDistribution

Sets the parameters of the distribution to attempt to fit the given list of data points. The weights of the data points will be used.

Specified by:

setUsingData in interface MultivariateDistribution

Parameters:

dataSet - the data set to use

threadpool - the source of threads for computation

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

setUsingData

public <V extends Vec> boolean setUsingData(List<V> dataSet,
                                            ExecutorService threadpool)

Description copied from interface: MultivariateDistribution

Sets the parameters of the distribution to attempt to fit the given list of vectors. All vectors are assumed to have the same weight.

Specified by:

setUsingData in interface MultivariateDistribution

Type Parameters:

V - the vector type

Parameters:

dataSet - the list of data points

threadpool - the source of threads for computation

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

setUsingDataList

public boolean setUsingDataList(List<DataPoint> dataPoints,
                                ExecutorService threadpool)

Description copied from interface: MultivariateDistribution

Sets the parameters of the distribution to attempt to fit the given list of data points. The weights of the data points will be used.

Specified by:

setUsingDataList in interface MultivariateDistribution

Parameters:

dataPoints - the list of data points to use

threadpool - the source of threads for computation

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

clone

public EMGaussianMixture clone()

Specified by:

clone in interface Clusterer

Specified by:

clone in interface KClusterer

Specified by:

clone in interface MultivariateDistribution

Specified by:

clone in class KClustererBase

sample

public List<Vec> sample(int count,
                        Random rand)

Description copied from interface: MultivariateDistribution

Performs sampling on the current distribution.

Specified by:

sample in interface MultivariateDistribution

Parameters:

count - the number of iid samples to draw

rand - the source of randomness

Returns:

a list of sample vectors from this distribution

cluster

public int[] cluster(DataSet dataSet,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Specified by:

cluster in interface Clusterer

Parameters:

dataSet - the data set to perform clustering on

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

cluster

public int[] cluster(DataSet dataSet,
                     ExecutorService threadpool,
                     int[] designations)

Description copied from interface: Clusterer

Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.

Specified by:

cluster in interface Clusterer

Parameters:

dataSet - the data set to perform clustering on

threadpool - a source of threads to run tasks

designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.

Returns:

an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null

cluster

public int[] cluster(DataSet dataSet,
                     int clusters,
                     ExecutorService threadpool,
                     int[] designations)

Specified by:

cluster in interface KClusterer

cluster

public int[] cluster(DataSet dataSet,
                     int clusters,
                     int[] designations)

Specified by:

cluster in interface KClusterer

cluster

public int[] cluster(DataSet dataSet,
                     int lowK,
                     int highK,
                     ExecutorService threadpool,
                     int[] designations)

Specified by:

cluster in interface KClusterer

cluster

public int[] cluster(DataSet dataSet,
                     int lowK,
                     int highK,
                     int[] designations)

Specified by:

cluster in interface KClusterer