Ok, I'm working on a predator/prey simulation, which evolve using genetic
algorithms. At the moment, they use a quite simple feed-forward neural network,
which can change size over time. Each brain "tick" is performed by the
following function (inside the Brain class):
def tick(self):
input_num = self.input_num
hidden_num = self.hidden_num
output_num = self.output_num
hidden = [0]*hidden_num
output = [0]*output_num
inputs = self.input
h_weight = self.h_weight
o_weight = self.o_weight
e = math.e
count = -1
for x in range(hidden_num):
temp = 0
for y in range(input_num):
count += 1
temp += inputs[y] * h_weight[count]
hidden[x] = 1/(1+e**(-temp))
count = -1
for x in range(output_num):
temp = 0
for y in range(hidden_num):
count += 1
temp += hidden[y] * o_weight[count]
output[x] = 1/(1+e**(-temp))
self.output = output
The function is actually quite fast (~0.040 seconds per 200 calls, using 10
input, 20 hidden and 3 output neurons), and used to be much slower untill I
fiddled about with it a bit to make it faster. However, it is still somewhat
slow for what I need it.
My question to you is if you an see any obvious (or not so obvious) way of
making this faster. I've heard about numpy and have been reading about it, but
I really can't see how it could be implemented here.
Cheers!
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