Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé

Module « scipy.stats »

Fonction chi - module scipy.stats

Signature de la fonction chi

def chi(*args, **kwds) 

Description

help(scipy.stats.chi)

A chi continuous random variable.

As an instance of the `rv_continuous` class, `chi` 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(df, loc=0, scale=1, size=1, random_state=None)
    Random variates.
pdf(x, df, loc=0, scale=1)
    Probability density function.
logpdf(x, df, loc=0, scale=1)
    Log of the probability density function.
cdf(x, df, loc=0, scale=1)
    Cumulative distribution function.
logcdf(x, df, loc=0, scale=1)
    Log of the cumulative distribution function.
sf(x, df, loc=0, scale=1)
    Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
logsf(x, df, loc=0, scale=1)
    Log of the survival function.
ppf(q, df, loc=0, scale=1)
    Percent point function (inverse of ``cdf`` --- percentiles).
isf(q, df, loc=0, scale=1)
    Inverse survival function (inverse of ``sf``).
moment(order, df, loc=0, scale=1)
    Non-central moment of the specified order.
stats(df, loc=0, scale=1, moments='mv')
    Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
entropy(df, 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=(df,), 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(df, loc=0, scale=1)
    Median of the distribution.
mean(df, loc=0, scale=1)
    Mean of the distribution.
var(df, loc=0, scale=1)
    Variance of the distribution.
std(df, loc=0, scale=1)
    Standard deviation of the distribution.
interval(confidence, df, loc=0, scale=1)
    Confidence interval with equal areas around the median.

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

.. math::

    f(x, k) = \frac{1}{2^{k/2-1} \Gamma \left( k/2 \right)}
               x^{k-1} \exp \left( -x^2/2 \right)

for :math:`x >= 0` and :math:`k > 0` (degrees of freedom, denoted ``df``
in the implementation). :math:`\Gamma` is the gamma function
(`scipy.special.gamma`).

Special cases of `chi` are:

    - ``chi(1, loc, scale)`` is equivalent to `halfnorm`
    - ``chi(2, 0, scale)`` is equivalent to `rayleigh`
    - ``chi(3, 0, scale)`` is equivalent to `maxwell`

`chi` takes ``df`` as a shape parameter.

The probability density above is defined in the "standardized" form. To shift
and/or scale the distribution use the ``loc`` and ``scale`` parameters.
Specifically, ``chi.pdf(x, df, loc, scale)`` is identically
equivalent to ``chi.pdf(y, df) / 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.

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

Calculate the first four moments:

>>> df = 78
>>> mean, var, skew, kurt = chi.stats(df, moments='mvsk')

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

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

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

>>> vals = chi.ppf([0.001, 0.5, 0.999], df)
>>> np.allclose([0.001, 0.5, 0.999], chi.cdf(vals, df))
True

Generate random numbers:

>>> r = chi.rvs(df, 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()

Vous êtes un professionnel et vous avez besoin d'une formation ? Sensibilisation à
l'Intelligence Artificielle Voir le programme détaillé