Uses of Interface
org.apache.commons.math3.distribution.RealDistribution

Packages that use RealDistribution

Package	Description
org.apache.commons.math3.distribution	Implementations of common discrete and continuous distributions.
org.apache.commons.math3.ml.neuralnet	Neural networks.
org.apache.commons.math3.random	Random number and random data generators.
org.apache.commons.math3.stat.inference	Classes providing hypothesis testing.

Uses of RealDistribution in org.apache.commons.math3.distribution

Classes in org.apache.commons.math3.distribution that implement RealDistribution

Modifier and Type	Class	Description
class	AbstractRealDistribution	Base class for probability distributions on the reals.
class	BetaDistribution	Implements the Beta distribution.
class	CauchyDistribution	Implementation of the Cauchy distribution.
class	ChiSquaredDistribution	Implementation of the chi-squared distribution.
class	ConstantRealDistribution	Implementation of the constant real distribution.
class	EnumeratedRealDistribution	Implementation of a real-valued EnumeratedDistribution.
class	ExponentialDistribution	Implementation of the exponential distribution.
class	FDistribution	Implementation of the F-distribution.
class	GammaDistribution	Implementation of the Gamma distribution.
class	GumbelDistribution	This class implements the Gumbel distribution.
class	LaplaceDistribution	This class implements the Laplace distribution.
class	LevyDistribution	This class implements the Lévy distribution.
class	LogisticDistribution	This class implements the Logistic distribution.
class	LogNormalDistribution	Implementation of the log-normal (gaussian) distribution.
class	NakagamiDistribution	This class implements the Nakagami distribution.
class	NormalDistribution	Implementation of the normal (gaussian) distribution.
class	ParetoDistribution	Implementation of the Pareto distribution.
class	TDistribution	Implementation of Student's t-distribution.
class	TriangularDistribution	Implementation of the triangular real distribution.
class	UniformRealDistribution	Implementation of the uniform real distribution.
class	WeibullDistribution	Implementation of the Weibull distribution.

Uses of RealDistribution in org.apache.commons.math3.ml.neuralnet

Methods in org.apache.commons.math3.ml.neuralnet with parameters of type RealDistribution

Modifier and Type	Method	Description
static FeatureInitializer	FeatureInitializerFactory.randomize(RealDistribution random, FeatureInitializer orig)	Adds some amount of random data to the given initializer.

Uses of RealDistribution in org.apache.commons.math3.random

Classes in org.apache.commons.math3.random that implement RealDistribution

Modifier and Type	Class	Description
class	EmpiricalDistribution	Represents an empirical probability distribution -- a probability distribution derived from observed data without making any assumptions about the functional form of the population distribution that the data come from.

Methods in org.apache.commons.math3.random that return RealDistribution

Modifier and Type	Method	Description
protected RealDistribution	EmpiricalDistribution.getKernel(SummaryStatistics bStats)	The within-bin smoothing kernel.
private RealDistribution	EmpiricalDistribution.k(double x)	The within-bin kernel of the bin that x belongs to.

Methods in org.apache.commons.math3.random with parameters of type RealDistribution

Modifier and Type	Method	Description
double	RandomDataImpl.nextInversionDeviate(RealDistribution distribution)	Deprecated. use the distribution's sample() method

Uses of RealDistribution in org.apache.commons.math3.stat.inference

Methods in org.apache.commons.math3.stat.inference with parameters of type RealDistribution

Modifier and Type	Method	Description
private static void	KolmogorovSmirnovTest.jitter(double[] data, RealDistribution dist)	Adds random jitter to data using deviates sampled from dist.
double	KolmogorovSmirnovTest.kolmogorovSmirnovStatistic(RealDistribution distribution, double[] data)	Computes the one-sample Kolmogorov-Smirnov test statistic, \(D_n=\sup_x |F_n(x)-F(x)|\) where \(F\) is the distribution (cdf) function associated with distribution, \(n\) is the length of data and \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in data.
static double	TestUtils.kolmogorovSmirnovStatistic(RealDistribution dist, double[] data)
double	KolmogorovSmirnovTest.kolmogorovSmirnovTest(RealDistribution distribution, double[] data)	Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
double	KolmogorovSmirnovTest.kolmogorovSmirnovTest(RealDistribution distribution, double[] data, boolean exact)	Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
boolean	KolmogorovSmirnovTest.kolmogorovSmirnovTest(RealDistribution distribution, double[] data, double alpha)	Performs a Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
static double	TestUtils.kolmogorovSmirnovTest(RealDistribution dist, double[] data)
static double	TestUtils.kolmogorovSmirnovTest(RealDistribution dist, double[] data, boolean strict)
static boolean	TestUtils.kolmogorovSmirnovTest(RealDistribution dist, double[] data, double alpha)