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import numpy

numpy.seterr(all='ignore')

def sigmoid(x):

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

def dsigmoid(x):

return x * (1. - x)

def tanh(x):

return numpy.tanh(x)

def dtanh(x):

return 1. - x * 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

def ReLU(x):

return x * (x > 0)

def dReLU(x):

return 1. * (x > 0)

# # 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)