Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé

Module « scipy.stats »

Fonction gzscore - module scipy.stats

Signature de la fonction gzscore

def gzscore(a, *, axis=0, ddof=0, nan_policy='propagate') 

Description

help(scipy.stats.gzscore)

Compute the geometric standard score.

Compute the geometric z score of each strictly positive value in the
sample, relative to the geometric mean and standard deviation.
Mathematically the geometric z score can be evaluated as::

    gzscore = log(a/gmu) / log(gsigma)

where ``gmu`` (resp. ``gsigma``) is the geometric mean (resp. standard
deviation).

Parameters
----------
a : array_like
    Sample data.
axis : int or None, optional
    Axis along which to operate. Default is 0. If None, compute over
    the whole array `a`.
ddof : int, optional
    Degrees of freedom correction in the calculation of the
    standard deviation. Default is 0.
nan_policy : {'propagate', 'raise', 'omit'}, optional
    Defines how to handle when input contains nan. 'propagate' returns nan,
    'raise' throws an error, 'omit' performs the calculations ignoring nan
    values. Default is 'propagate'.  Note that when the value is 'omit',
    nans in the input also propagate to the output, but they do not affect
    the geometric z scores computed for the non-nan values.

Returns
-------
gzscore : array_like
    The geometric z scores, standardized by geometric mean and geometric
    standard deviation of input array `a`.

See Also
--------
gmean : Geometric mean
gstd : Geometric standard deviation
zscore : Standard score

Notes
-----
This function preserves ndarray subclasses, and works also with
matrices and masked arrays (it uses ``asanyarray`` instead of
``asarray`` for parameters).

.. versionadded:: 1.8

References
----------
.. [1] "Geometric standard score", *Wikipedia*,
       https://en.wikipedia.org/wiki/Geometric_standard_deviation#Geometric_standard_score.

Examples
--------
Draw samples from a log-normal distribution:

>>> import numpy as np
>>> from scipy.stats import zscore, gzscore
>>> import matplotlib.pyplot as plt

>>> rng = np.random.default_rng()
>>> mu, sigma = 3., 1.  # mean and standard deviation
>>> x = rng.lognormal(mu, sigma, size=500)

Display the histogram of the samples:

>>> fig, ax = plt.subplots()
>>> ax.hist(x, 50)
>>> plt.show()

Display the histogram of the samples standardized by the classical zscore.
Distribution is rescaled but its shape is unchanged.

>>> fig, ax = plt.subplots()
>>> ax.hist(zscore(x), 50)
>>> plt.show()

Demonstrate that the distribution of geometric zscores is rescaled and
quasinormal:

>>> fig, ax = plt.subplots()
>>> ax.hist(gzscore(x), 50)
>>> plt.show()

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