public class NakagamiDistribution extends AbstractRealDistribution
| Modifier and Type | Field and Description | 
|---|---|
| static double | DEFAULT_INVERSE_ABSOLUTE_ACCURACYDefault inverse cumulative probability accuracy. | 
random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY| Constructor and Description | 
|---|
| NakagamiDistribution(double mu,
                    double omega)Build a new instance. | 
| NakagamiDistribution(double mu,
                    double omega,
                    double inverseAbsoluteAccuracy)Build a new instance. | 
| NakagamiDistribution(RandomGenerator rng,
                    double mu,
                    double omega,
                    double inverseAbsoluteAccuracy)Build a new instance. | 
| Modifier and Type | Method and Description | 
|---|---|
| double | cumulativeProbability(double x)For a random variable  Xwhose values are distributed according
 to this distribution, this method returnsP(X <= x). | 
| double | density(double x)Returns the probability density function (PDF) of this distribution
 evaluated at the specified point  x. | 
| double | getNumericalMean()Use this method to get the numerical value of the mean of this
 distribution. | 
| double | getNumericalVariance()Use this method to get the numerical value of the variance of this
 distribution. | 
| double | getScale()Access the scale parameter,  omega. | 
| double | getShape()Access the shape parameter,  mu. | 
| protected double | getSolverAbsoluteAccuracy()Returns the solver absolute accuracy for inverse cumulative computation. | 
| double | getSupportLowerBound()Access the lower bound of the support. | 
| double | getSupportUpperBound()Access the upper bound of the support. | 
| boolean | isSupportConnected()Use this method to get information about whether the support is connected,
 i.e. | 
| boolean | isSupportLowerBoundInclusive()Whether or not the lower bound of support is in the domain of the density
 function. | 
| boolean | isSupportUpperBoundInclusive()Whether or not the upper bound of support is in the domain of the density
 function. | 
cumulativeProbability, inverseCumulativeProbability, logDensity, probability, probability, reseedRandomGenerator, sample, samplepublic static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
public NakagamiDistribution(double mu,
                    double omega)
 Note: this constructor will implicitly create an instance of
 Well19937c as random generator to be used for sampling only (see
 AbstractRealDistribution.sample() and AbstractRealDistribution.sample(int)). In case no sampling is
 needed for the created distribution, it is advised to pass null
 as random generator via the appropriate constructors to avoid the
 additional initialisation overhead.
mu - shape parameteromega - scale parameter (must be positive)NumberIsTooSmallException - if mu < 0.5NotStrictlyPositiveException - if omega <= 0public NakagamiDistribution(double mu,
                    double omega,
                    double inverseAbsoluteAccuracy)
 Note: this constructor will implicitly create an instance of
 Well19937c as random generator to be used for sampling only (see
 AbstractRealDistribution.sample() and AbstractRealDistribution.sample(int)). In case no sampling is
 needed for the created distribution, it is advised to pass null
 as random generator via the appropriate constructors to avoid the
 additional initialisation overhead.
mu - shape parameteromega - scale parameter (must be positive)inverseAbsoluteAccuracy - the maximum absolute error in inverse
 cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY).NumberIsTooSmallException - if mu < 0.5NotStrictlyPositiveException - if omega <= 0public NakagamiDistribution(RandomGenerator rng, double mu, double omega, double inverseAbsoluteAccuracy)
rng - Random number generatormu - shape parameteromega - scale parameter (must be positive)inverseAbsoluteAccuracy - the maximum absolute error in inverse
 cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY).NumberIsTooSmallException - if mu < 0.5NotStrictlyPositiveException - if omega <= 0public double getShape()
mu.public double getScale()
omega.protected double getSolverAbsoluteAccuracy()
getSolverAbsoluteAccuracy in class AbstractRealDistributionpublic double density(double x)
x. In general, the PDF is
 the derivative of the CDF.
 If the derivative does not exist at x, then an appropriate
 replacement should be returned, e.g. Double.POSITIVE_INFINITY,
 Double.NaN, or  the limit inferior or limit superior of the
 difference quotient.x - the point at which the PDF is evaluatedxpublic double cumulativeProbability(double x)
X whose values are distributed according
 to this distribution, this method returns P(X <= x). In other
 words, this method represents the (cumulative) distribution function
 (CDF) for this distribution.x - the point at which the CDF is evaluatedxpublic double getNumericalMean()
Double.NaN if it is not definedpublic double getNumericalVariance()
Double.POSITIVE_INFINITY as
 for certain cases in TDistribution) or Double.NaN if it
 is not definedpublic double getSupportLowerBound()
inverseCumulativeProbability(0). In other words, this
 method must return
 inf {x in R | P(X <= x) > 0}.
Double.NEGATIVE_INFINITY)public double getSupportUpperBound()
inverseCumulativeProbability(1). In other words, this
 method must return
 inf {x in R | P(X <= x) = 1}.
Double.POSITIVE_INFINITY)public boolean isSupportLowerBoundInclusive()
getSupporLowerBound() is finite and
 density(getSupportLowerBound()) returns a non-NaN, non-infinite
 value.public boolean isSupportUpperBoundInclusive()
getSupportUpperBound() is finite and
 density(getSupportUpperBound()) returns a non-NaN, non-infinite
 value.public boolean isSupportConnected()
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