scipy.stats.rv_discrete

class scipy.stats.rv_discrete(a=0, b=inf, name=None, badvalue=None, moment_tol=1e-08, values=None, inc=1, longname=None, shapes=None, extradoc=None)

A generic discrete random variable class meant for subclassing.

rv_discrete is a base class to construct specific distribution classes and instances from for discrete random variables. rv_discrete can be used to construct an arbitrary distribution with defined by a list of support points and the corresponding probabilities.

Parameters :

a : float, optional

Lower bound of the support of the distribution, default: 0

b : float, optional

Upper bound of the support of the distribution, default: plus infinity

moment_tol : float, optional

The tolerance for the generic calculation of moments

values : tuple of two array_like

(xk, pk) where xk are points (integers) with positive probability pk with sum(pk) = 1

inc : integer

increment for the support of the distribution, default: 1 other values have not been tested

badvalue : object, optional

The value in (masked) arrays that indicates a value that should be ignored.

name : str, optional

The name of the instance. This string is used to construct the default example for distributions.

longname : str, optional

This string is used as part of the first line of the docstring returned when a subclass has no docstring of its own. Note: longname exists for backwards compatibility, do not use for new subclasses.

shapes : str, optional

The shape of the distribution. For example "m, n" for a distribution that takes two integers as the first two arguments for all its methods.

extradoc : str, optional

This string is used as the last part of the docstring returned when a subclass has no docstring of its own. Note: extradoc exists for backwards compatibility, do not use for new subclasses.

Notes

Alternatively, the object may be called (as a function) to fix the shape and location parameters returning a “frozen” discrete RV object:

myrv = generic(<shape(s)>, loc=0)

• frozen RV object with the same methods but holding the given shape and location fixed.

You can construct an aribtrary discrete rv where P{X=xk} = pk by passing to the rv_discrete initialization method (through the values=keyword) a tuple of sequences (xk, pk) which describes only those values of X (xk) that occur with nonzero probability (pk).

To create a new discrete distribution, we would do the following:

class poisson_gen(rv_continuous):
    #"Poisson distribution"
    def _pmf(self, k, mu):
        ...

and create an instance

poisson = poisson_gen(name=”poisson”, shapes=”mu”, longname=’A Poisson’)

The docstring can be created from a template.

Examples

>>> import matplotlib.pyplot as plt
>>> numargs = generic.numargs
>>> [ <shape(s)> ] = ['Replace with resonable value', ]*numargs

Display frozen pmf:

>>> rv = generic(<shape(s)>)
>>> x = np.arange(0, np.min(rv.dist.b, 3)+1)
>>> h = plt.plot(x, rv.pmf(x))

Check accuracy of cdf and ppf:

>>> prb = generic.cdf(x, <shape(s)>)
>>> h = plt.semilogy(np.abs(x-generic.ppf(prb, <shape(s)>))+1e-20)

Random number generation:

>>> R = generic.rvs(<shape(s)>, size=100)

Custom made discrete distribution:

>>> vals = [arange(7), (0.1, 0.2, 0.3, 0.1, 0.1, 0.1, 0.1)]
>>> custm = rv_discrete(name='custm', values=vals)
>>> h = plt.plot(vals[0], custm.pmf(vals[0]))

(Source code)

Methods

generic.rvs(<shape(s)>, loc=0, size=1)	random variates
generic.pmf(x, <shape(s)>, loc=0)	probability mass function
generic.cdf(x, <shape(s)>, loc=0)	cumulative density function
generic.sf(x, <shape(s)>, loc=0)	survival function (1-cdf — sometimes more accurate)
generic.ppf(q, <shape(s)>, loc=0)	percent point function (inverse of cdf — percentiles)
generic.isf(q, <shape(s)>, loc=0)	inverse survival function (inverse of sf)
generic.stats(<shape(s)>, loc=0, moments=’mv’)	mean(‘m’, axis=0), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’)
generic.entropy(<shape(s)>, loc=0)	entropy of the RV
generic(<shape(s)>, loc=0)	calling a distribution instance returns a frozen distribution