@PublicationReference(author="Wikipedia", title="Dirichlet distribution", type=WebPage, year=2009, url="http://en.wikipedia.org/wiki/Dirichlet_distribution") public class DirichletDistribution extends AbstractDistribution<Vector> implements ClosedFormComputableDistribution<Vector>
| Modifier and Type | Class and Description |
|---|---|
static class |
DirichletDistribution.PDF
PDF of the Dirichlet distribution.
|
| Modifier and Type | Field and Description |
|---|---|
protected Vector |
parameters
Parameters of the Dirichlet distribution, must be at least 2-dimensional
and each element must be positive.
|
| Constructor and Description |
|---|
DirichletDistribution()
Creates a new instance of DirichletDistribution
|
DirichletDistribution(DirichletDistribution other)
Copy Constructor.
|
DirichletDistribution(int dimensionality)
Creates a new instance of DirichletDistribution
|
DirichletDistribution(Vector parameters)
Creates a new instance of DirichletDistribution
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| Modifier and Type | Method and Description |
|---|---|
DirichletDistribution |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
void |
convertFromVector(Vector parameters)
Converts the object from a Vector of parameters.
|
Vector |
convertToVector()
Converts the object to a vector.
|
Vector |
getMean()
Gets the arithmetic mean, or "first central moment" or "expectation",
of the distribution.
|
Vector |
getParameters()
Getter for parameters
|
DirichletDistribution.PDF |
getProbabilityFunction()
Gets the distribution function associated with this Distribution,
either the PDF or PMF.
|
Vector |
sample(java.util.Random random)
Draws a single random sample from the distribution.
|
void |
sampleInto(java.util.Random random,
int numSamples,
java.util.Collection<? super Vector> output)
Draws multiple random samples from the distribution and puts the result
into the given collection.
|
void |
setParameters(Vector parameters)
Setter for parameters
|
sampleequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitsampleprotected Vector parameters
public DirichletDistribution()
public DirichletDistribution(int dimensionality)
dimensionality - Dimensionality of the distributionpublic DirichletDistribution(Vector parameters)
parameters - Parameters of the Dirichlet distribution, must be at least 2-dimensional
and each element must be positive.public DirichletDistribution(DirichletDistribution other)
other - DirichletDistribution to copy.public DirichletDistribution clone()
AbstractCloneableSerializableObject class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone() to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone() as the basis for your implementation.clone in interface Vectorizableclone in interface CloneableSerializableclone in class AbstractCloneableSerializablepublic Vector getMean()
DistributionWithMeangetMean in interface DistributionWithMean<Vector>public Vector sample(java.util.Random random)
Distributionsample in interface Distribution<Vector>sample in class AbstractDistribution<Vector>random - Random-number generator to use in order to generate random numbers.public void sampleInto(java.util.Random random,
int numSamples,
java.util.Collection<? super Vector> output)
DistributionsampleInto in interface Distribution<Vector>random - Random number generator to use.numSamples - The number of samples to draw. Cannot be negative.output - The collection to add the samples into.public Vector convertToVector()
VectorizableconvertToVector in interface Vectorizablepublic void convertFromVector(Vector parameters)
VectorizableconvertFromVector in interface Vectorizableparameters - The parameters to incorporate.public Vector getParameters()
public void setParameters(Vector parameters)
parameters - Parameters of the Dirichlet distribution, must be at least 2-dimensional
and each element must be positive.public DirichletDistribution.PDF getProbabilityFunction()
ComputableDistributiongetProbabilityFunction in interface ComputableDistribution<Vector>