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Module « scipy.special »

Fonction log_expit - module scipy.special

Signature de la fonction log_expit

def log_expit(*args, **kwargs) 

Description

help(scipy.special.log_expit)

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


    log_expit(x, out=None)

    Logarithm of the logistic sigmoid function.

    The SciPy implementation of the logistic sigmoid function is
    `scipy.special.expit`, so this function is called ``log_expit``.

    The function is mathematically equivalent to ``log(expit(x))``, but
    is formulated to avoid loss of precision for inputs with large
    (positive or negative) magnitude.

    Parameters
    ----------
    x : array_like
        The values to apply ``log_expit`` to element-wise.
    out : ndarray, optional
        Optional output array for the function results

    Returns
    -------
    out : scalar or ndarray
        The computed values, an ndarray of the same shape as ``x``.

    See Also
    --------
    expit

    Notes
    -----
    As a ufunc, ``log_expit`` takes a number of optional keyword arguments.
    For more information see
    `ufuncs <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_

    .. versionadded:: 1.8.0

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.special import log_expit, expit

    >>> log_expit([-3.0, 0.25, 2.5, 5.0])
    array([-3.04858735, -0.57593942, -0.07888973, -0.00671535])

    Large negative values:

    >>> log_expit([-100, -500, -1000])
    array([ -100.,  -500., -1000.])

    Note that ``expit(-1000)`` returns 0, so the naive implementation
    ``log(expit(-1000))`` return ``-inf``.

    Large positive values:

    >>> log_expit([29, 120, 400])
    array([-2.54366565e-013, -7.66764807e-053, -1.91516960e-174])

    Compare that to the naive implementation:

    >>> np.log(expit([29, 120, 400]))
    array([-2.54463117e-13,  0.00000000e+00,  0.00000000e+00])

    The first value is accurate to only 3 digits, and the larger inputs
    lose all precision and return 0.
    

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