gov.sandia.cognition.statistics.distribution

Class LogNormalDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<NumberType>

gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<java.lang.Double>

gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution

gov.sandia.cognition.statistics.distribution.LogNormalDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<java.lang.Double>, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, ComputableDistribution<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, EstimableDistribution<java.lang.Double,LogNormalDistribution>, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

LogNormalDistribution.CDF, LogNormalDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Log-normal distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Log-normal_distribution")
public class LogNormalDistribution
extends AbstractClosedFormSmoothUnivariateDistribution
implements EstimableDistribution<java.lang.Double,LogNormalDistribution>

Log-Normal distribution PDF and CDF implementations. The Log-Normal distribution is related to a UnivariateGaussian where the natural logarithm of the random variable is normally distributed. This turns up in application areas that are the product of some random variables, where each random variable is i.i.d. and normally distributed. Stock market returns are the classic example of a Log-Normal distribution.

Since:

2.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	LogNormalDistribution.CDF CDF of the Log-Normal Distribution
static class	LogNormalDistribution.MaximumLikelihoodEstimator Maximum Likelihood Estimator of a log-normal distribution.
static class	LogNormalDistribution.PDF PDF of a Log-normal distribution
static class	LogNormalDistribution.WeightedMaximumLikelihoodEstimator Maximum Likelihood Estimator from weighted data

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_LOG_NORMAL_MEAN Default log normal mean, 0.0.
static double	DEFAULT_LOG_NORMAL_VARIANCE Default log normal variance, 1.0.
static double	SQRT2PI Constant value of Math.sqrt(2*Math.PI)

Constructor Summary

Constructors

Constructor and Description
LogNormalDistribution() Default constructor.
LogNormalDistribution(double logNormalMean, double logNormalVariance) Creates a new instance of LogNormalDistribution
LogNormalDistribution(LogNormalDistribution other) Copy Constructor

Method Summary

Modifier and Type	Method and Description
void	convertFromVector(Vector parameters) Sets the parameters of the distribution from a 2-dimensional Vector with ( logNormalMean logNormalVariance )
Vector	convertToVector() Returns a 2-dimensional Vector with ( logNormalMean logNormalVariance )
LogNormalDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
LogNormalDistribution.MaximumLikelihoodEstimator	getEstimator() Gets an estimator associated with this distribution.
double	getLogNormalMean() Getter for logNormalMean
double	getLogNormalVariance() Getter for logNormalVariance
java.lang.Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
double	getMeanAsDouble() Gets the mean of the distribution as a double.
java.lang.Double	getMinSupport() Gets the minimum support (domain or input) of the distribution.
LogNormalDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getVariance() Gets the variance of the distribution.
double	sampleAsDouble(java.util.Random random) Samples a value from this distribution as a double.
void	sampleInto(java.util.Random random, double[] output, int start, int length) Samples values from this distribution as an array of doubles.
void	setLogNormalMean(double logNormalMean) Setter for logNormalMean
void	setLogNormalVariance(double logNormalVariance) Setter for logNormalVariance
java.lang.String	toString()

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

getMean, sampleAsDoubles, sampleInto

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

clone

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

sample, 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, sample, sampleInto

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.math.matrix.Vectorizable

clone

Field Detail

DEFAULT_LOG_NORMAL_MEAN

public static final double DEFAULT_LOG_NORMAL_MEAN

Default log normal mean, 0.0.

See Also:

Constant Field Values

DEFAULT_LOG_NORMAL_VARIANCE

public static final double DEFAULT_LOG_NORMAL_VARIANCE

Default log normal variance, 1.0.

See Also:

Constant Field Values

SQRT2PI

public static final double SQRT2PI

Constant value of Math.sqrt(2*Math.PI)

Constructor Detail

LogNormalDistribution

public LogNormalDistribution()

Default constructor.

LogNormalDistribution

public LogNormalDistribution(double logNormalMean,
                             double logNormalVariance)

Creates a new instance of LogNormalDistribution

Parameters:

logNormalMean - Mean of the underlying distribution, (-infinity,+infinity)

logNormalVariance - Variance of the underlying distribution, (0,infinity)

LogNormalDistribution

public LogNormalDistribution(LogNormalDistribution other)

Copy Constructor

Parameters:

other - LogNormalDistribution to copy

Method Detail

convertToVector

public Vector convertToVector()

Returns a 2-dimensional Vector with ( logNormalMean logNormalVariance )

Specified by:

convertToVector in interface Vectorizable

Returns:

2-dimensional Vector with ( logNormalMean logNormalVariance )

convertFromVector

public void convertFromVector(Vector parameters)

Sets the parameters of the distribution from a 2-dimensional Vector with ( logNormalMean logNormalVariance )

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - 2-dimensional Vector with ( logNormalMean logNormalVariance )

getLogNormalMean

public double getLogNormalMean()

Getter for logNormalMean

Returns:

Mean of the underlying distribution, (-infinity,+infinity)

setLogNormalMean

public void setLogNormalMean(double logNormalMean)

Setter for logNormalMean

Parameters:

logNormalMean - Mean of the underlying distribution, (-infinity,+infinity)

getLogNormalVariance

public double getLogNormalVariance()

Getter for logNormalVariance

Returns:

Variance of the underlying distribution, (0,infinity)

setLogNormalVariance

public void setLogNormalVariance(double logNormalVariance)

Setter for logNormalVariance

Parameters:

logNormalVariance - Variance of the underlying distribution, (0,infinity)

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a double.

Specified by:

getMeanAsDouble in interface UnivariateDistribution<java.lang.Double>

Returns:

The mean as a double.

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<java.lang.Double>

Returns:

Variance of the distribution.

sampleAsDouble

public double sampleAsDouble(java.util.Random random)

Description copied from interface: SmoothUnivariateDistribution

Samples a value from this distribution as a double. This is a convenience method to potentially avoid boxing.

Specified by:

sampleAsDouble in interface SmoothUnivariateDistribution

Overrides:

sampleAsDouble in class AbstractClosedFormSmoothUnivariateDistribution

Parameters:

random - Random number generator to use.

Returns:

A value sampled from this distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       double[] output,
                       int start,
                       int length)

Description copied from interface: SmoothUnivariateDistribution

Samples values from this distribution as an array of doubles. This is a convenience method to potentially avoid boxing.

Specified by:

sampleInto in interface SmoothUnivariateDistribution

Parameters:

random - Random number generator to use.

output - The array to write the result into. Cannot be null.

start - The offset in the array to start writing at. Cannot be negative.

length - The number of values to sample. Cannot be negative.

getCDF

public LogNormalDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<java.lang.Double>

Specified by:

getCDF in interface SmoothUnivariateDistribution

Specified by:

getCDF in interface UnivariateDistribution<java.lang.Double>

Returns:

CDF of the scalar distribution.

getProbabilityFunction

public LogNormalDistribution.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<java.lang.Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Returns:

Distribution function associated with this Distribution.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getMinSupport

public java.lang.Double getMinSupport()

Description copied from interface: UnivariateDistribution

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

Specified by:

getMinSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getMaxSupport

public java.lang.Double getMaxSupport()

Description copied from interface: UnivariateDistribution

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

Specified by:

getMaxSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getEstimator

public LogNormalDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<java.lang.Double,LogNormalDistribution>

Returns:

A distribution estimator associated for this distribution.