How can I make this piece of code even faster?

[Fabio Zadrozny]

On Sat, Jul 20, 2013 at 5:22 PM, <pablobarhamalzas at gmail.com> wrote:

> 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!
> --
> http://mail.python.org/mailman/listinfo/python-list
>



Low level optimizations:

If you're in Python 2.x (and not 3), you should use xrange() instead of
range(), or maybe even create a local variable and increment it and check
its value within a while (that way you can save a few instructions on
method invocations from xrange/range).

Anyways, if that's not fast enough, just port it to c/c++ (or one of the
alternatives to speed it up while still in python: numba, cython,
shedskin). Or (if you can), try to use PyPy and see if you get more speed
without doing anything.

Cheers,

Fabio
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