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Fast on-demand sampling from categorical distributions

Project description

Categorical Sampler

Install from pip: `pip install categorical-sampler`

Let’s generate a probability distribution to get us started. First, sample a bunch of random numbers to determine probability “scores”.

>>> from random import random
>>> k = 10**6
>>> scores = [random() for i in range(k)]
>>> total = sum(scores)
>>> probabilities = [s / total for s in scores]

We've normalized the scores to sum to 1, i.e. make
them into proper probabilities, but actually the categorical sampler will do that for us, so it’s not necessary:

>>> from categorical import Categorical as C
>>> my_sampler = C(scores)
>>> print my_sampler.sample()

Comparing to numpy, assuming we draw 1000 individual samples *individually*:

>>> from numpy.random import choice
>>> import time
>>> def time_numpy():
>>> start = time.time()
>>> for i in range(1000):
>>> choice(k, p=probabilities)
>>> print time.time() - start
>>> def time_my_alias():
>>> start = time.time()
>>> for i in range(1000):
>>> my_sampler.sample()
>>> print time.time() - start
>>> time_numpy()
>>> time_my_alias()

Get the actual probability of a given outcome:

>>> my_sampler.get_probability(487702)

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