On 20Jun2010 12:44, Stefan Behnel <stefan...@behnel.de> wrote:
| southof40, 20.06.2010 12:19:
| >I have list of of N Vehicle objects - the only possible vehicles are
| >cars, bikes, trucks.
| >
| >I want to select an object from the list with a probability of : cars
| >0.7, bikes 0.3, trucks 0.1.
| >
| >I've currently implemented this by creating another list in which each
| >car object from the original list appears 7 times, each bike 3 times
| >and each truck once. I then pick at random from that list.
| >
| >This works but seems very clunky to me.
| 
| Why? It's a very simple, generic, easy to understand and fast
| solution to the problem.

Only 3 out of 4, if you want to be precise in your selections.
Supposing he wants probabilities 0.7432, 0.3765, 0.1087654 ?
The required list needs to be Very Long to achieve an accurate
representation, and thus Very Slow to construct/populate.

A faster approach is to make a list represention the sum of the
proportions as one counts along the choices, thus 0.7, 1.0, 1.1 in the
example given (0.7, 0.7+0.3, 0.7+0.3+0.1). Then choose a value in the
range 0.0 to the total (1.1) using the pseudo-random function of your
choice, such as that in the random module. Then binary search the list
for the matching item.

The list scales linearly as the number of choices, not exponentially
with the precision of the proportions. The search is logarithmic with
the number of choices. Beyond a very small number of choices the former
will dominate.

Cheers,
-- 
Cameron Simpson <c...@zip.com.au> DoD#743
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