''' '''

import numpy

numpy.seterr(all='ignore')

def sigmoid(x):

return 1. / (1 + numpy.exp(-x))

def softmax(x):

e = numpy.exp(x - numpy.max(x)) # prevent overflow

if e.ndim == 1:

return e / numpy.sum(e, axis=0)

else:

return e / numpy.array([numpy.sum(e, axis=1)]).T # ndim = 2

# # probability density for the Gaussian dist

# def gaussian(x, mean=0.0, scale=1.0):

# s = 2 * numpy.power(scale, 2)

# e = numpy.exp( - numpy.power((x - mean), 2) / s )

# return e / numpy.square(numpy.pi * s)