I'm interested in prototyping a Monte Carlo type simulation algorithm in
Python. The background is that a friend has written a similar program in C++,
and I'm interested in seeing if I can achieve something comparable in a much
better language :-)
The basic job of the program will be to simulate games of chance - so we'll
have random inputs (die rolls, card draws, etc) and repeatedly simulate
calculating a "result". Based on the results of the simulation, the idea is to
estimate the probability of a given result.
So, to give a very specific example:
import random
def die(n, sides=6):
total = sum(random.randint(1, sides) for i in range(n))
return total
def simulate(n, test):
"Run the simulation N times, returning the probability that TEST is true"
successes = 0
for i in range(n):
if test():
successes = successes + 1
return successes/n
def check_3d6_gt_15():
return die(3) > 15
if __name__ == '__main__':
print(simulate(100000, check_3d6_gt_15))
Obviously, this is going to run ridiculously slowly as the number of
simulations or the complexity of the calculation increases, but this gives the
idea.
My immediate instinct is that somewhere in the scipy stack, there will be a
module that does this sort of thing efficiently, but I don't really know where
to look - my understanding of the maths involved is very much at the naive
level above, so I'm not sure what terms I should be searching for, or how to
frame a query.
Can anyone give me some pointers as to where I should go to find out more about
this sort of task? Either more general theory (that would help me ask the right
questions!) or specific packages or techniques I should be using in
Python/numpy would be fantastic.
Any help would be gratefully accepted - surely nobody wants to see Python
beaten by a C++ program??? :-)
Thanks,
Paul
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