Module « scipy.stats »

Classe « rv_discrete »

Informations générales

Héritage

builtins.object
    rv_generic
        rv_discrete

Définition

class rv_discrete(rv_generic):

Description _{[extrait de rv_discrete.__doc__]}

A generic discrete random variable class meant for subclassing.

    `rv_discrete` is a base class to construct specific distribution classes
    and instances for discrete random variables. It can also be used
    to construct an arbitrary distribution defined by a list of support
    points and 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, optional
        ``(xk, pk)`` where ``xk`` are integers and ``pk`` are the non-zero
        probabilities between 0 and 1 with ``sum(pk) = 1``. ``xk``
        and ``pk`` must have the same shape.
    inc : integer, optional
        Increment for the support of the distribution.
        Default is 1. (other values have not been tested)
    badvalue : float, optional
        The value in a result arrays that indicates a value that for which
        some argument restriction is violated, default is np.nan.
    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 two shape arguments for all its methods
        If not provided, shape parameters will be inferred from
        the signatures of the private methods, ``_pmf`` and ``_cdf`` of
        the instance.
    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.
    seed : {None, int, `numpy.random.Generator`,
            `numpy.random.RandomState`}, optional

        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
        singleton is used.
        If `seed` is an int, a new ``RandomState`` instance is used,
        seeded with `seed`.
        If `seed` is already a ``Generator`` or ``RandomState`` instance then
        that instance is used.

    Methods
    -------
    rvs
    pmf
    logpmf
    cdf
    logcdf
    sf
    logsf
    ppf
    isf
    moment
    stats
    entropy
    expect
    median
    mean
    std
    var
    interval
    __call__
    support

    Notes
    -----
    This class is similar to `rv_continuous`. Whether a shape parameter is
    valid is decided by an ``_argcheck`` method (which defaults to checking
    that its arguments are strictly positive.)
    The main differences are:

    - the support of the distribution is a set of integers
    - instead of the probability density function, ``pdf`` (and the
      corresponding private ``_pdf``), this class defines the
      *probability mass function*, `pmf` (and the corresponding
      private ``_pmf``.)
    - scale parameter is not defined.

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

    >>> from scipy.stats import rv_discrete
    >>> class poisson_gen(rv_discrete):
    ...     "Poisson distribution"
    ...     def _pmf(self, k, mu):
    ...         return exp(-mu) * mu**k / factorial(k)

    and create an instance::

    >>> poisson = poisson_gen(name="poisson")

    Note that above we defined the Poisson distribution in the standard form.
    Shifting the distribution can be done by providing the ``loc`` parameter
    to the methods of the instance. For example, ``poisson.pmf(x, mu, loc)``
    delegates the work to ``poisson._pmf(x-loc, mu)``.

    **Discrete distributions from a list of probabilities**

    Alternatively, you can construct an arbitrary discrete rv defined
    on a finite set of values ``xk`` with ``Prob{X=xk} = pk`` by using the
    ``values`` keyword argument to the `rv_discrete` constructor.

    Examples
    --------
    Custom made discrete distribution:

    >>> from scipy import stats
    >>> xk = np.arange(7)
    >>> pk = (0.1, 0.2, 0.3, 0.1, 0.1, 0.0, 0.2)
    >>> custm = stats.rv_discrete(name='custm', values=(xk, pk))
    >>>
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.plot(xk, custm.pmf(xk), 'ro', ms=12, mec='r')
    >>> ax.vlines(xk, 0, custm.pmf(xk), colors='r', lw=4)
    >>> plt.show()

    Random number generation:

    >>> R = custm.rvs(size=100)

    

Constructeur(s)

Signature du constructeur	Description
__new__(cls, a=0, b=inf, name=None, badvalue=None, moment_tol=1e-08, values=None, inc=1, longname=None, shapes=None, extradoc=None, seed=None)
__init__(self, a=0, b=inf, name=None, badvalue=None, moment_tol=1e-08, values=None, inc=1, longname=None, shapes=None, extradoc=None, seed=None)

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Méthodes héritées de la classe rv_generic

__call__, __init_subclass__, __setstate__, __subclasshook__, entropy, freeze, interval, mean, median, moment, stats, std, support, var

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__