jsat.clustering

Class EMGaussianMixture

java.lang.Object

jsat.clustering.EMGaussianMixture

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Clusterer, KClusterer, MultivariateDistribution

public class EMGaussianMixture
extends java.lang.Object
implements KClusterer, MultivariateDistribution

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

See Also:

Serialized Form

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, boolean parallel, 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, boolean parallel, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, boolean parallel, int[] designations)
int[]	cluster(DataSet dataSet, int lowK, int highK, int[] designations)
int	getIterationLimit() Returns the maximum number of iterations of the ElkanKMeans algorithm that will be performed.
SeedSelectionMethods.SeedSelection	getSeedSelection()
double	logPdf(Vec x) Computes the log of the probability density function.
double	pdf(Vec x) Returns the probability of a given vector from this distribution.
java.util.List<Vec>	sample(int count, java.util.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, boolean parallel) Sets the parameters of the distribution to attempt to fit the given list of data points.
<V extends Vec> boolean	setUsingData(java.util.List<V> dataSet, boolean parallel) Sets the parameters of the distribution to attempt to fit the given list of vectors.

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.clustering.KClusterer

cluster, cluster, cluster, cluster, cluster

Methods inherited from interface jsat.clustering.Clusterer

cluster, cluster, supportsWeightedData

Methods inherited from interface jsat.distributions.multivariate.MultivariateDistribution

logPdf, pdf, setUsingData, setUsingData, setUsingDataList

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.

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(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(java.util.List<V> dataSet,
                                            boolean parallel)

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

parallel - true if the training should be done using multiple-cores, false for single threaded.

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,
                            boolean parallel)

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

parallel - 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

Overrides:

clone in class java.lang.Object

sample

public java.util.List<Vec> sample(int count,
                                  java.util.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,
                     boolean parallel,
                     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

parallel - true if multiple threads should be used to perform clustering. false if it should be done in a single threaded manner.

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:

the int[]

cluster

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

Specified by:

cluster in interface KClusterer

cluster

public int[] cluster(DataSet dataSet,
                     int lowK,
                     int highK,
                     boolean parallel,
                     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