jsat.distributions.multivariate

Class NormalMR

java.lang.Object

jsat.distributions.multivariate.MultivariateDistributionSkeleton

jsat.distributions.multivariate.NormalM

jsat.distributions.multivariate.NormalMR

All Implemented Interfaces:

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

public class NormalMR
extends NormalM

This class implements the Multivariate Normal Distribution, but augments it so that fitting the distribution uses a robust estimate of the distribution parameters. This comes at increased cost that is cubic with respect to the number of variables.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NormalMR()

Method Summary

Modifier and Type	Method and Description
<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 jsat.distributions.multivariate.NormalM

clone, logPdf, pdf, sample, setCovariance, setMeanCovariance

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.distributions.multivariate.MultivariateDistribution

logPdf, pdf, setUsingData, setUsingData, setUsingData, setUsingDataList

Constructor Detail

NormalMR

public NormalMR()

Method Detail

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

Overrides:

setUsingData in class NormalM

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.