gov.sandia.cognition.statistics.distribution

Class LaplaceDistribution.CDF

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.LaplaceDistribution

gov.sandia.cognition.statistics.distribution.LaplaceDistribution.CDF

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, ClosedFormDifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>, DifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>, Vectorizable, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, ClosedFormComputableDistribution<java.lang.Double>, ClosedFormCumulativeDistributionFunction<java.lang.Double>, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, ComputableDistribution<java.lang.Double>, CumulativeDistributionFunction<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, EstimableDistribution<java.lang.Double,LaplaceDistribution>, InvertibleCumulativeDistributionFunction<java.lang.Double>, SmoothCumulativeDistributionFunction, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

LaplaceDistribution

public static class LaplaceDistribution.CDF
extends LaplaceDistribution
implements SmoothCumulativeDistributionFunction, InvertibleCumulativeDistributionFunction<java.lang.Double>

CDF of the Laplace distribution.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.distribution.LaplaceDistribution

LaplaceDistribution.CDF, LaplaceDistribution.MaximumLikelihoodEstimator, LaplaceDistribution.PDF, LaplaceDistribution.WeightedMaximumLikelihoodEstimator

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.distribution.LaplaceDistribution

DEFAULT_MEAN, DEFAULT_SCALE, mean, scale

Constructor Summary

Constructors

Constructor and Description
CDF() Creates a new instance of LaplaceDistribution.CDF
CDF(double mean, double scale) Creates a new instance of LaplaceDistribution.CDF
CDF(LaplaceDistribution other) Copy Constructor

Method Summary

Modifier and Type	Method and Description
java.lang.Double	differentiate(java.lang.Double input) Differentiates the output with respect to the input
double	evaluate(double input) Produces a double output for the given double input
java.lang.Double	evaluate(java.lang.Double input) Evaluates the function on the given input and returns the output.
double	evaluateAsDouble(java.lang.Double input) Evaluates the scalar function as a double.
LaplaceDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
LaplaceDistribution.PDF	getDerivative() Gets the closed-form derivative of the function.
java.lang.Double	inverse(double p) Computes the inverse of the CDF for the give probability.
static double	inverse(LaplaceDistribution laplace, double p) Computes the inverse of the CDF for the give probability.

Methods inherited from class gov.sandia.cognition.statistics.distribution.LaplaceDistribution

clone, convertFromVector, convertToVector, getEstimator, getMaxSupport, getMean, getMeanAsDouble, getMinSupport, getProbabilityFunction, getScale, getVariance, sampleAsDouble, sampleInto, setMean, setScale, toString

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

sampleAsDoubles, sampleInto

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.SmoothUnivariateDistribution

getMean, getProbabilityFunction, sampleAsDouble, sampleAsDoubles, sampleInto

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

getMaxSupport, getMeanAsDouble, getMinSupport, getVariance

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

sample, sample, sampleInto

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

clone, convertFromVector, convertToVector

Constructor Detail

CDF

public CDF()

Creates a new instance of LaplaceDistribution.CDF

CDF

public CDF(double mean,
           double scale)

Creates a new instance of LaplaceDistribution.CDF

Parameters:

mean - Mean of the distribution

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

CDF

public CDF(LaplaceDistribution other)

Copy Constructor

Parameters:

other - LaplaceDistribution to copy

Method Detail

evaluate

public java.lang.Double evaluate(java.lang.Double input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<java.lang.Double,java.lang.Double>

Specified by:

evaluate in interface ScalarFunction<java.lang.Double>

Specified by:

evaluate in interface UnivariateScalarFunction

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

evaluateAsDouble

public double evaluateAsDouble(java.lang.Double input)

Description copied from interface: ScalarFunction

Evaluates the scalar function as a double.

Specified by:

evaluateAsDouble in interface ScalarFunction<java.lang.Double>

Specified by:

evaluateAsDouble in interface UnivariateScalarFunction

Parameters:

input - The input value.

Returns:

The scalar output calculated from the given input.

evaluate

public double evaluate(double input)

Description copied from interface: UnivariateScalarFunction

Produces a double output for the given double input

Specified by:

evaluate in interface UnivariateScalarFunction

Parameters:

input - Input to the Evaluator

Returns:

output at the given input

inverse

public java.lang.Double inverse(double p)

Computes the inverse of the CDF for the give probability. That is, find x=CDF.inverse(p) so that CDF(x)=p.

Specified by:

inverse in interface InvertibleCumulativeDistributionFunction<java.lang.Double>

Parameters:

p - Probability to invert, must be [0,1].

Returns:

Finds the value of the CDF "x" so that CDF(x)=p.

inverse

public static double inverse(LaplaceDistribution laplace,
                             double p)

Computes the inverse of the CDF for the give probability. That is, find x=CDF.inverse(p) so that CDF(x)=p.

Parameters:

laplace - Laplace distribution to invert.

p - Probability to invert, must be [0,1].

Returns:

Finds the value of the CDF "x" so that CDF(x)=p.

getCDF

public LaplaceDistribution.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>

Overrides:

getCDF in class LaplaceDistribution

Returns:

CDF of the scalar distribution.

getDerivative

public LaplaceDistribution.PDF getDerivative()

Description copied from interface: ClosedFormDifferentiableEvaluator

Gets the closed-form derivative of the function.

Specified by:

getDerivative in interface ClosedFormDifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>

Specified by:

getDerivative in interface SmoothCumulativeDistributionFunction

Returns:

Closed-form derivative of the function.

differentiate

public java.lang.Double differentiate(java.lang.Double input)

Description copied from interface: DifferentiableEvaluator

Differentiates the output with respect to the input

Specified by:

differentiate in interface DifferentiableEvaluator<java.lang.Double,java.lang.Double,java.lang.Double>

Parameters:

input - Input about which to compute the derivative

Returns:

Derivative of the output with respect to the given input