A Nakagami continuous random variable.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters :

x : array-like


q : array-like

lower or upper tail probability

nu : array-like

shape parameters

loc : array-like, optional

location parameter (default=0)

scale : array-like, optional

scale parameter (default=1)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : str, optional

composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)

Alternatively, the object may be called (as a function) to fix the shape, :

location, and scale parameters returning a “frozen” continuous RV object: :

rv = nakagami(nu, loc=0, scale=1) :

  • Frozen RV object with the same methods but holding the given shape, location, and scale fixed.


Nakagami distribution

nakagami.pdf(x,nu) = 2*nu**nu/gamma(nu) * x**(2*nu-1) * exp(-nu*x**2) for x > 0, nu > 0.


>>> import matplotlib.pyplot as plt
>>> numargs = nakagami.numargs
>>> [ nu ] = [0.9,] * numargs
>>> rv = nakagami(nu)

Display frozen pdf

>>> x = np.linspace(0, np.minimum(rv.dist.b, 3))
>>> h = plt.plot(x, rv.pdf(x))

Check accuracy of cdf and ppf

>>> prb = nakagami.cdf(x, nu)
>>> h = plt.semilogy(np.abs(x - nakagami.ppf(prb, nu)) + 1e-20)

Random number generation

>>> R = nakagami.rvs(nu, size=100)


rvs(nu, loc=0, scale=1, size=1) Random variates.
pdf(x, nu, loc=0, scale=1) Probability density function.
cdf(x, nu, loc=0, scale=1) Cumulative density function.
sf(x, nu, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate).
ppf(q, nu, loc=0, scale=1) Percent point function (inverse of cdf — percentiles).
isf(q, nu, loc=0, scale=1) Inverse survival function (inverse of sf).
stats(nu, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(nu, loc=0, scale=1) (Differential) entropy of the RV.
fit(data, nu, loc=0, scale=1) Parameter estimates for generic data.

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