jsat.distributions.multivariate

Class Dirichlet

java.lang.Object

jsat.distributions.multivariate.MultivariateDistributionSkeleton

jsat.distributions.multivariate.Dirichlet

All Implemented Interfaces:

Serializable, Cloneable, MultivariateDistribution

public class Dirichlet
extends MultivariateDistributionSkeleton

An implementation of the Dirichlet distribution. The Dirichlet distribution takes a vector of positive alphas as its argument, which also specifies the dimension of the distribution. The Dirichlet distribution has a non zero PDF only when the input vector sums to 1.0, and contains no negative or zero values.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Dirichlet(Vec alphas) Creates a new Dirichlet distribution.

Method Summary

Modifier and Type	Method and Description
Dirichlet	clone()
Vec	getAlphas() Returns the backing vector that contains the alphas specifying 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.
List<Vec>	sample(int count, Random rand) Performs sampling on the current distribution.
void	setAlphas(Vec alphas) Sets the alphas of the 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> dataPoint) 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

Dirichlet

public Dirichlet(Vec alphas)

Creates a new Dirichlet distribution.

Parameters:

alphas - the positive alpha values for the distribution. The length of the vector indicates the dimension

Throws:

ArithmeticException - if any of the alpha values are not positive

Method Detail

setAlphas

public void setAlphas(Vec alphas)
               throws ArithmeticException

Sets the alphas of the distribution. A copy is made, so altering the input does not effect the distribution.

Parameters:

alphas - the parameter values

Throws:

ArithmeticException - if any of the alphas are not positive numbers

getAlphas

public Vec getAlphas()

Returns the backing vector that contains the alphas specifying the current distribution. Mutable operations should not be applied.

Returns:

the alphas that make the current distribution.

clone

public Dirichlet 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(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> 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.

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.

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