gov.sandia.cognition.statistics.distribution
Class LaplaceDistribution

java.lang.Object
  gov.sandia.cognition.util.AbstractCloneableSerializable
      gov.sandia.cognition.statistics.AbstractDistribution<NumberType>
          gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<Double>
              gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
                  gov.sandia.cognition.statistics.distribution.LaplaceDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<Double>, ClosedFormDistribution<Double>, ClosedFormUnivariateDistribution<Double>, ComputableDistribution<Double>, Distribution<Double>, DistributionWithMean<Double>, EstimableDistribution<Double,LaplaceDistribution>, SmoothUnivariateDistribution, UnivariateDistribution<Double>, CloneableSerializable, Serializable, Cloneable

Direct Known Subclasses:

LaplaceDistribution.CDF, LaplaceDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Laplace distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Laplace_distribution")
public class LaplaceDistribution
extends AbstractClosedFormSmoothUnivariateDistribution
implements EstimableDistribution<Double,LaplaceDistribution>

A Laplace distribution, sometimes called a double exponential distribution. This distribution arrises when evaluating the difference between two iid exponential random variables, or when sampling Brownian motion at exponentially distributed time steps.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

static class	LaplaceDistribution.CDF CDF of the Laplace distribution.
static class	LaplaceDistribution.MaximumLikelihoodEstimator Estimates the ML parameters of a Laplace distribution from a Collection of Numbers.
static class	LaplaceDistribution.PDF The PDF of a Laplace Distribution.
static class	LaplaceDistribution.WeightedMaximumLikelihoodEstimator Creates a UnivariateGaussian from weighted data

Field Summary

static double	DEFAULT_MEAN Default mean, 0.0.
static double	DEFAULT_SCALE Default scale, 1.0.
protected double	mean Mean of the distribution
protected double	scale Scale factor of the distribution, must be greater than zero.

Constructor Summary

LaplaceDistribution()
Creates a new instance of LaplaceDistribution

LaplaceDistribution(double mean, double scale)
Creates a new instance of LaplaceDistribution

LaplaceDistribution(LaplaceDistribution other)
Copy Constructor

Method Summary

LaplaceDistribution	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.
LaplaceDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
LaplaceDistribution.MaximumLikelihoodEstimator	getEstimator() Gets an estimator associated with this distribution.
Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
Double	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
Double	getMinSupport() Gets the minimum support (domain or input) of the distribution.
LaplaceDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getScale() Getter for scale
double	getVariance() Gets the variance of the distribution.
ArrayList<Double>	sample(Random random, int numSamples) Draws multiple random samples from the distribution.
void	setMean(double mean) Setter for mean
void	setScale(double scale) Setter for scale
String	toString()

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample

Field Detail

DEFAULT_MEAN

public static final double DEFAULT_MEAN

Default mean, 0.0.

See Also:

Constant Field Values

DEFAULT_SCALE

public static final double DEFAULT_SCALE

Default scale, 1.0.

See Also:

Constant Field Values

mean

protected double mean

Mean of the distribution

scale

protected double scale

Scale factor of the distribution, must be greater than zero.

Constructor Detail

LaplaceDistribution

public LaplaceDistribution()

Creates a new instance of LaplaceDistribution

LaplaceDistribution

public LaplaceDistribution(double mean,
                           double scale)

Creates a new instance of LaplaceDistribution

Parameters:

mean - Mean of the distribution

scale - Scale factor of the distribution, must be greater than zero.

LaplaceDistribution

public LaplaceDistribution(LaplaceDistribution other)

Copy Constructor

Parameters:

other - LaplaceDistribution to copy

Method Detail

clone

public LaplaceDistribution clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object 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.

Specified by:

clone in interface Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractClosedFormUnivariateDistribution<Double>

Returns:

A clone of this object.

getMean

public Double getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<Double>

Specified by:

getMean in interface SmoothUnivariateDistribution

Returns:

Mean of the distribution.

setMean

public void setMean(double mean)

Setter for mean

Parameters:

mean - Mean of the distribution

getScale

public double getScale()

Getter for scale

Returns:

Scale factor of the distribution, must be greater than zero.

setScale

public void setScale(double scale)

Setter for scale

Parameters:

scale - Scale factor of the distribution, must be greater than zero.

sample

public ArrayList<Double> sample(Random random,
                                int numSamples)

Description copied from interface: Distribution

Draws multiple random samples from the distribution. It is generally more efficient to use this multiple-sample method than multiple calls of the single-sample method. (But not always.)

Specified by:

sample in interface Distribution<Double>

Parameters:

random - Random-number generator to use in order to generate random numbers.

numSamples - Number of samples to draw from the distribution.

Returns:

Samples drawn according to this distribution.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

getVariance

public double getVariance()

Description copied from interface: UnivariateDistribution

Gets the variance of the distribution. This is sometimes called the second central moment by more pedantic people, which is equivalent to the square of the standard deviation.

Specified by:

getVariance in interface UnivariateDistribution<Double>

Returns:

Variance of the distribution.

toString

public String toString()

Overrides:

toString in class Object

getCDF

public LaplaceDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<Double>

Specified by:

getCDF in interface SmoothUnivariateDistribution

Specified by:

getCDF in interface UnivariateDistribution<Double>

Returns:

CDF of the scalar distribution.

getProbabilityFunction

public LaplaceDistribution.PDF getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Returns:

Distribution function associated with this Distribution.

getMinSupport

public Double getMinSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMinSupport in interface UnivariateDistribution<Double>

Returns:

Minimum support.

getMaxSupport

public Double getMaxSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMaxSupport in interface UnivariateDistribution<Double>

Returns:

Minimum support.

getEstimator

public LaplaceDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<Double,LaplaceDistribution>

Returns:

A distribution estimator associated for this distribution.