How to calculate a logistic sigmoid function in Python?

unwind 2015-04-27 15:43

This should do it:

import math

def sigmoid(x):
  return 1 / (1 + math.exp(-x))

And now you can test it by calling:

>>> sigmoid(0.458)
0.61253961344091512

Update: Note that the above was mainly intended as a straight one-to-one translation of the given expression into Python code. It is not tested or known to be a numerically sound implementation. If you know you need a very robust implementation, I'm sure there are others where people have actually given this problem some thought.

Martin Thoma 2014-08-01 02:41:42

Just because I need it so often to try little things: sigmoid = lambda x: 1 / (1 + math.exp(-x))

Neil G 2015-04-25 18:22:52

This does not work for extreme negative values of x. I was using this unfortunate implementation until I noticed it was creating NaNs.

Richard Rast 2016-07-21 01:18:29

If you replace math.exp with np.exp you won't get NaNs, although you will get runtime warnings.

ViniciusArruda 2017-11-23 21:41:25

Using math.exp with numpy array can yield some errors, like: TypeError: only length-1 arrays can be converted to Python scalars. To avoid it you should use numpy.exp.

Elias Hasle 2019-02-06 18:03:22

Can numerical instability be mitigated simply by adding x = max(-709,x) before the expression?

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