jsat.distributions.multivariate

Class MultivariateDistributionSkeleton

java.lang.Object

jsat.distributions.multivariate.MultivariateDistributionSkeleton

All Implemented Interfaces:

Serializable, Cloneable, MultivariateDistribution

Direct Known Subclasses:

Dirichlet, MultivariateKDE, NormalM, SymmetricDirichlet

public abstract class MultivariateDistributionSkeleton
extends Object
implements MultivariateDistribution

Common class for implementing a multivariate distribution. A number of methods are pre implemented, building off of the implementation of the remaining methods.
Note: the default implementation for the multithreaded methods calls the non threaded version of the method. The exception to this is the setUsingData(jsat.DataSet, java.util.concurrent.ExecutorService) method, which calls setUsingData(java.util.List, java.util.concurrent.ExecutorService)

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MultivariateDistributionSkeleton()

Method Summary

Modifier and Type	Method and Description
abstract MultivariateDistribution	clone()
double	logPdf(double... x) Computes the log of the probability density function.
double	logPdf(Vec x) Computes the log of the probability density function.
double	pdf(double... x) Returns the probability of a given vector from this distribution.
boolean	setUsingData(DataSet dataSet) Sets the parameters of the distribution to attempt to fit the given list of data points.
boolean	setUsingData(DataSet dataSet, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of data points.
<V extends Vec> boolean	setUsingData(List<V> dataSet, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of vectors.
boolean	setUsingDataList(List<DataPoint> dataPoints, ExecutorService threadpool) Sets the parameters of the distribution to attempt to fit the given list of data points.

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.distributions.multivariate.MultivariateDistribution

pdf, sample, setUsingData, setUsingDataList

Constructor Detail

MultivariateDistributionSkeleton

public MultivariateDistributionSkeleton()

Method Detail

logPdf

public double logPdf(double... 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

Parameters:

x - the array for the vector the get the log probability of

Returns:

the log of the probability.

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

Parameters:

x - the vector the get the log probability of

Returns:

the log of the probability.

pdf

public double pdf(double... 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].

Specified by:

pdf in interface MultivariateDistribution

Parameters:

x - the array of the vector the get the log probability of

Returns:

the probability

setUsingData

public boolean setUsingData(DataSet 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.

Specified by:

setUsingData in interface MultivariateDistribution

Parameters:

dataSet - the data set 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.

setUsingData

public boolean setUsingData(DataSet dataSet,
                            ExecutorService threadpool)

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.

Specified by:

setUsingData in interface MultivariateDistribution

Parameters:

dataSet - the data set to use

threadpool - the source of threads for computation

Returns:

true if the distribution was fit to the data, or false if the distribution could not be fit to the data set.

setUsingData

public <V extends Vec> boolean setUsingData(List<V> dataSet,
                                            ExecutorService threadpool)

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

Type Parameters:

V - the vector type

Parameters:

dataSet - the list of data points

threadpool - the source of threads for computation

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> dataPoints,
                                ExecutorService threadpool)

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.

Specified by:

setUsingDataList in interface MultivariateDistribution

Parameters:

dataPoints - the list of data points to use

threadpool - the source of threads for computation

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 abstract MultivariateDistribution clone()

Specified by:

clone in interface MultivariateDistribution

Overrides:

clone in class Object