jsat.distributions.multivariate

Class SymmetricDirichlet

java.lang.Object

jsat.distributions.multivariate.MultivariateDistributionSkeleton

jsat.distributions.multivariate.SymmetricDirichlet

All Implemented Interfaces:

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

public class SymmetricDirichlet
extends MultivariateDistributionSkeleton

The Symmetric Dirichlet Distribution is a special case of the Dirichlet distribution, and occurs when all alphas have the same value.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SymmetricDirichlet(double alpha, int dim) Creates a new Symmetric Dirichlet distribution.

Method Summary

Modifier and Type	Method and Description
SymmetricDirichlet	clone()
double	getAlpha() Returns the alpha value used by this distribution
int	getDimension() Returns the dimension size of the current distribution
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	setAlpha(double alpha) Sets the alpha value used for the distribution
void	setDimension(int dim) Sets the dimension size of the distribution
<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.distributions.multivariate.MultivariateDistribution

logPdf, pdf, setUsingData, setUsingData, setUsingData, setUsingDataList

Constructor Detail

SymmetricDirichlet

public SymmetricDirichlet(double alpha,
                          int dim)

Creates a new Symmetric Dirichlet distribution.

Parameters:

alpha - the positive alpha value for the distribution

dim - the dimension of the distribution.

Throws:

java.lang.ArithmeticException - if a non positive alpha or dimension value is given

Method Detail

setDimension

public void setDimension(int dim)

Sets the dimension size of the distribution

Parameters:

dim - the new dimension size

getDimension

public int getDimension()

Returns the dimension size of the current distribution

Returns:

the number of dimensions in this distribution

setAlpha

public void setAlpha(double alpha)
              throws java.lang.ArithmeticException

Sets the alpha value used for the distribution

Parameters:

alpha - the positive value for the distribution

Throws:

java.lang.ArithmeticException - if the value given is not a positive value

getAlpha

public double getAlpha()

Returns the alpha value used by this distribution

Returns:

the alpha value used by this distribution

clone

public SymmetricDirichlet clone()

Specified by:

clone in interface MultivariateDistribution

Specified by:

clone in class MultivariateDistributionSkeleton

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(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.

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.

sample

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