On Jul 25, 10:57 am, Suresh Pillai <[EMAIL PROTECTED]> wrote:
> I am performing simulations on networks (graphs). I have a question on
> speed of execution (assuming very ample memory for now). I simplify the
> details of my simulation below, as the question I ask applies more
> generally than my specific case. I would greatly appreciate general
> feedback in terms of computing and of course considerations specific to
> implementation in Python.
>
> The nodes in my network may be ON or OFF. The network starts off with
> all nodes in the OFF state. I loop through the nodes. For each node
> that is OFF, I consider some probability of it turning ON based on the
> states of its neighbours. I MUST GO THROUGH ALL NODES BEFORE DECIDING
> WHICH ONES TO TURN ON.
>
> So my question is whether it is faster to
>
> 1. loop through a list of ALL nodes and check for OFF nodes using ifs
>
> or to
>
> 2. loop through a container of OFF nodes and remove from this when they
> turn ON
or 3. build a new list every iteration intead of deleting from the old
one:
while processing:
new_off_list = []
for x in off_list:
if goes_on(x):
on_list.append(x)
else:
new_off_list.append(x)
off_list = new_off_list
generation += 1
Iain
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