Vous êtes un professionnel et vous avez besoin d'une formation ? Mise en oeuvre d'IHM
avec Qt et PySide6 Voir le programme détaillé

Module « scipy.special »

Fonction smirnovi - module scipy.special

Signature de la fonction smirnovi

def smirnovi(*args, **kwargs) 

Description

help(scipy.special.smirnovi)

smirnovi(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])

smirnovi(n, p, out=None)

Inverse to `smirnov`

Returns `d` such that ``smirnov(n, d) == p``, the critical value
corresponding to `p`.

Parameters
----------
n : int
  Number of samples
p : float array_like
    Probability
out : ndarray, optional
    Optional output array for the function results

Returns
-------
scalar or ndarray
    The value(s) of smirnovi(n, p), the critical values.

See Also
--------
smirnov : The Survival Function (SF) for the distribution
scipy.stats.ksone : Provides the functionality as a continuous distribution
kolmogorov, kolmogi : Functions for the two-sided distribution
scipy.stats.kstwobign : Two-sided Kolmogorov-Smirnov distribution, large n

Notes
-----
`smirnov` is used by `stats.kstest` in the application of the
Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this
function is exposed in `scpy.special`, but the recommended way to achieve
the most accurate CDF/SF/PDF/PPF/ISF computations is to use the
`stats.ksone` distribution.

Examples
--------
>>> from scipy.special import smirnovi, smirnov

>>> n = 24
>>> deviations = [0.1, 0.2, 0.3]

Use `smirnov` to compute the complementary CDF of the Smirnov
distribution for the given number of samples and deviations.

>>> p = smirnov(n, deviations)
>>> p
array([0.58105083, 0.12826832, 0.01032231])

The inverse function ``smirnovi(n, p)`` returns ``deviations``.

>>> smirnovi(n, p)
array([0.1, 0.2, 0.3])

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