Probability selection algorithm
Mel Wilson
mwilson at the-wire.com
Mon Feb 3 22:12:10 CET 2003
In article <7xhebnscrj.fsf at ruckus.brouhaha.com>,
Paul Rubin <phr-n2003b at NOSPAMnightsong.com> wrote:
>Paul Rubin <phr-n2003b at NOSPAMnightsong.com> writes:
>> [ ... ]
>> where urand returns a random number between 0.0 and 1.0 and
>> problist is your list of probabilities. "selected" is set to
>> the appropriate randomly chosen index.
>>
>> Do I have that right?
>
>Whoops, no I didn't have it right. Try this instead:
>
> def sprob(problist):
> remaining = 1.0
> for i in range(len(problist)):
> p = problist[i]
> if urand()*remaining < p:
> return i
> remaining -= p
> raise "shouldn't get here"
>
>Again, this depends on your probability list adding up to 1.
>Otherwise, add up the probabilities and scale everything appropriately.
There's a variant that works nicely with unnormalized
weightings:
selected = None # in case the set of pairs is empty
cumulative = 0
for weighting, payload in set_of_weighting_payload_pairs:
cumulative += weighting
if urand() * cumulative < weighting:
selected = payload
`weighting` is the histogram value correlating with the
probablilty of selecting its corresponding payload.
Weightings don't have to sum to 1.0 .
Good for random Markov text generators etc. It's a
generalization of the algorighthm that chooses a fortune
cookie from an n-cookie file giving each a probability of
1/n for being chosen.
Regards. Mel.
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