scipy.stats.nakagami = <scipy.stats._continuous_distns.nakagami_gen object>[source]

A Nakagami continuous random variable.

As an instance of the rv_continuous class, nakagami object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.


The probability density function for nakagami is:

\[f(x, \nu) = \frac{2 \nu^\nu}{\Gamma(\nu)} x^{2\nu-1} \exp(-\nu x^2)\]

for \(x >= 0\), \(\nu > 0\).

nakagami takes nu as a shape parameter for \(\nu\).

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, nakagami.pdf(x, nu, loc, scale) is identically equivalent to nakagami.pdf(y, nu) / scale with y = (x - loc) / scale.


>>> from scipy.stats import nakagami
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> nu = 4.97
>>> mean, var, skew, kurt = nakagami.stats(nu, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(nakagami.ppf(0.01, nu),
...                 nakagami.ppf(0.99, nu), 100)
>>> ax.plot(x, nakagami.pdf(x, nu),
...        'r-', lw=5, alpha=0.6, label='nakagami pdf')

Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = nakagami(nu)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = nakagami.ppf([0.001, 0.5, 0.999], nu)
>>> np.allclose([0.001, 0.5, 0.999], nakagami.cdf(vals, nu))

Generate random numbers:

>>> r = nakagami.rvs(nu, size=1000)

And compare the histogram:

>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)


rvs(nu, loc=0, scale=1, size=1, random_state=None)

Random variates.

pdf(x, nu, loc=0, scale=1)

Probability density function.

logpdf(x, nu, loc=0, scale=1)

Log of the probability density function.

cdf(x, nu, loc=0, scale=1)

Cumulative distribution function.

logcdf(x, nu, loc=0, scale=1)

Log of the cumulative distribution function.

sf(x, nu, loc=0, scale=1)

Survival function (also defined as 1 - cdf, but sf is sometimes more accurate).

logsf(x, nu, loc=0, scale=1)

Log of the survival function.

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).

moment(n, nu, loc=0, scale=1)

Non-central moment of order n

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.

expect(func, args=(nu,), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)

Expected value of a function (of one argument) with respect to the distribution.

median(nu, loc=0, scale=1)

Median of the distribution.

mean(nu, loc=0, scale=1)

Mean of the distribution.

var(nu, loc=0, scale=1)

Variance of the distribution.

std(nu, loc=0, scale=1)

Standard deviation of the distribution.

interval(alpha, nu, loc=0, scale=1)

Endpoints of the range that contains alpha percent of the distribution

Previous topic


Next topic