scipy.special.log_expit#
- scipy.special.log_expit(x, out=None) = <ufunc 'log_expit'>#
- 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:
- xarray_like
- The values to apply - log_expitto element-wise.
- outndarray, optional
- Optional output array for the function results 
 
- Returns:
- outscalar or ndarray
- The computed values, an ndarray of the same shape as - x.
 
 - See also - Notes - As a ufunc, - log_expittakes a number of optional keyword arguments. For more information see ufuncs- Added in version 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.