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Module « scipy.stats »

Fonction gausshyper - module scipy.stats

Signature de la fonction gausshyper

def gausshyper(*args, **kwds) 

Description

help(scipy.stats.gausshyper)

A Gauss hypergeometric continuous random variable.

As an instance of the `rv_continuous` class, `gausshyper` 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.

Methods
-------
rvs(a, b, c, z, loc=0, scale=1, size=1, random_state=None)
    Random variates.
pdf(x, a, b, c, z, loc=0, scale=1)
    Probability density function.
logpdf(x, a, b, c, z, loc=0, scale=1)
    Log of the probability density function.
cdf(x, a, b, c, z, loc=0, scale=1)
    Cumulative distribution function.
logcdf(x, a, b, c, z, loc=0, scale=1)
    Log of the cumulative distribution function.
sf(x, a, b, c, z, loc=0, scale=1)
    Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
logsf(x, a, b, c, z, loc=0, scale=1)
    Log of the survival function.
ppf(q, a, b, c, z, loc=0, scale=1)
    Percent point function (inverse of ``cdf`` --- percentiles).
isf(q, a, b, c, z, loc=0, scale=1)
    Inverse survival function (inverse of ``sf``).
moment(order, a, b, c, z, loc=0, scale=1)
    Non-central moment of the specified order.
stats(a, b, c, z, loc=0, scale=1, moments='mv')
    Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
entropy(a, b, c, z, loc=0, scale=1)
    (Differential) entropy of the RV.
fit(data)
    Parameter estimates for generic data.
    See `scipy.stats.rv_continuous.fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html#scipy.stats.rv_continuous.fit>`__ for detailed documentation of the
    keyword arguments.
expect(func, args=(a, b, c, z), 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(a, b, c, z, loc=0, scale=1)
    Median of the distribution.
mean(a, b, c, z, loc=0, scale=1)
    Mean of the distribution.
var(a, b, c, z, loc=0, scale=1)
    Variance of the distribution.
std(a, b, c, z, loc=0, scale=1)
    Standard deviation of the distribution.
interval(confidence, a, b, c, z, loc=0, scale=1)
    Confidence interval with equal areas around the median.

Notes
-----
The probability density function for `gausshyper` is:

.. math::

    f(x, a, b, c, z) = C x^{a-1} (1-x)^{b-1} (1+zx)^{-c}

for :math:`0 \le x \le 1`, :math:`a,b > 0`, :math:`c` a real number,
:math:`z > -1`, and :math:`C = \frac{1}{B(a, b) F[2, 1](c, a; a+b; -z)}`.
:math:`F[2, 1]` is the Gauss hypergeometric function
`scipy.special.hyp2f1`.

`gausshyper` takes :math:`a`, :math:`b`, :math:`c` and :math:`z` as shape
parameters.

The probability density above is defined in the "standardized" form. To shift
and/or scale the distribution use the ``loc`` and ``scale`` parameters.
Specifically, ``gausshyper.pdf(x, a, b, c, z, loc, scale)`` is identically
equivalent to ``gausshyper.pdf(y, a, b, c, z) / scale`` with
``y = (x - loc) / scale``. Note that shifting the location of a distribution
does not make it a "noncentral" distribution; noncentral generalizations of
some distributions are available in separate classes.

References
----------
.. [1] Armero, C., and M. J. Bayarri. "Prior Assessments for Prediction in
       Queues." *Journal of the Royal Statistical Society*. Series D (The
       Statistician) 43, no. 1 (1994): 139-53. doi:10.2307/2348939

Examples
--------
>>> import numpy as np
>>> from scipy.stats import gausshyper
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate the first four moments:

>>> a, b, c, z = 13.8, 3.12, 2.51, 5.18
>>> mean, var, skew, kurt = gausshyper.stats(a, b, c, z, moments='mvsk')

Display the probability density function (``pdf``):

>>> x = np.linspace(gausshyper.ppf(0.01, a, b, c, z),
...                 gausshyper.ppf(0.99, a, b, c, z), 100)
>>> ax.plot(x, gausshyper.pdf(x, a, b, c, z),
...        'r-', lw=5, alpha=0.6, label='gausshyper 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 = gausshyper(a, b, c, z)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of ``cdf`` and ``ppf``:

>>> vals = gausshyper.ppf([0.001, 0.5, 0.999], a, b, c, z)
>>> np.allclose([0.001, 0.5, 0.999], gausshyper.cdf(vals, a, b, c, z))
True

Generate random numbers:

>>> r = gausshyper.rvs(a, b, c, z, size=1000)

And compare the histogram:

>>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
>>> ax.set_xlim([x[0], x[-1]])
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

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