jsat.distributions.multivariate

Class NormalM

java.lang.Object

jsat.distributions.multivariate.MultivariateDistributionSkeleton

jsat.distributions.multivariate.NormalM

All Implemented Interfaces:

Serializable, Cloneable, MultivariateDistribution

public class NormalM
extends MultivariateDistributionSkeleton

Class for the multivariate Normal distribution. It is often called the Multivariate Gaussian distribution.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NormalM()
NormalM(Vec mean, Matrix covariance)

Method Summary

Modifier and Type	Method and Description
NormalM	clone()
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.
List<Vec>	sample(int count, Random rand) Performs sampling on the current distribution.
void	setCovariance(Matrix covMatrix) Sets the covariance matrix for this matrix.
void	setMeanCovariance(Vec mean, Matrix covariance) Sets the mean and covariance for this distribution.
<V extends Vec> boolean	setUsingData(List<V> dataSet) Sets the parameters of the distribution to attempt to fit the given list of vectors.
boolean	setUsingDataList(List<DataPoint> dataSet) Sets the parameters of the distribution to attempt to fit the given list of data points.

Methods inherited from class jsat.distributions.multivariate.MultivariateDistributionSkeleton

logPdf, pdf, setUsingData, setUsingData, setUsingData, setUsingDataList

Methods inherited from class java.lang.Object

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

Constructor Detail

NormalM

public NormalM(Vec mean,
               Matrix covariance)

NormalM

public NormalM()

Method Detail

setMeanCovariance

public void setMeanCovariance(Vec mean,
                              Matrix covariance)

Sets the mean and covariance for this distribution. For an n dimensional distribution, mean should be of length n and covariance should be an n by n matrix. It is also a requirement that the matrix be symmetric positive definite.

Parameters:

mean - the mean for the distribution. A copy will be used.

covariance - the covariance for this distribution. A copy will be used.

Throws:

ArithmeticException - if the mean and covariance do not agree, or the covariance is not positive definite. An exception may not be throw for all bad matrices.

setCovariance

public void setCovariance(Matrix covMatrix)

Sets the covariance matrix for this matrix.

Parameters:

covMatrix - set the covariance matrix used for this distribution

Throws:

ArithmeticException - if the covariance matrix is not square, does not agree with the mean, or is not positive definite. An exception may not be throw for all bad matrices.

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

Overrides:

logPdf in class MultivariateDistributionSkeleton

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].

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.

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> 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.

Parameters:

dataSet - 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.

clone

public NormalM clone()

Specified by:

clone in interface MultivariateDistribution

Specified by:

clone in class MultivariateDistributionSkeleton

sample

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

Description copied from interface: MultivariateDistribution

Performs sampling on the current distribution.

Parameters:

count - the number of iid samples to draw

rand - the source of randomness

Returns:

a list of sample vectors from this distribution