Random Drawing Simulation -- performance issue
David J. Braden
dbraden at invalid.add
Wed Sep 13 18:12:13 CEST 2006
Travis E. Oliphant wrote:
> Brendon Towle wrote:
>> I need to simulate scenarios like the following: "You have a deck of
>> 3 orange cards, 5 yellow cards, and 2 blue cards. You draw a card,
>> replace it, and repeat N times."
> Thinking about the problem as drawing sample froms a discrete
> distribution defined by the population might help.
> For example, in SciPy you can define your own discrete random variable:
> var = scipy.stats.rv_discrete(name='sample',
> would quickly return an array of "draws"
> If you didn't want to install SciPy, but were willing to install NumPy,
> then the crux of the algorithm is to generate an entire array of uniform
> random variables: numpy.random.rand(count) and then map them through
> the inverse cumulative distribution function to generate your samples.
> The inverse cumulative distribution function is just a series of steps
> whose width depends on the probablity distribution.
> Thus, the population defines the draw boundaries. To make it easy, just
> multiply the uniform random number by the total number of cards and then
> the boundaries are on the integers of the number of each kind of card.
> Here is an implementation. I find this version to be 2x - 5x faster
> depending on how many draws are used.
> import numpy as N
> def randomDrawing_N(count, population):
> probs = [x for x in population]
> res = [[0, item] for item in population]
> nums = N.random.rand(count)
> cprobs = N.cumsum(+probs)
> nums *= cprobs[-1]
> for k, val in enumerate(probs):
> res[k] += ((nums > cprobs[k]) & (nums < cprobs[k+1])).sum()
> return res
> -Travis Oliphant
In response to what Travis and Simon wrote -
(1) Where the heck can I find a description of scipy's stat functions?
Documentation on these seems sparse.
(2) How does one set up a good timer for Python as implemented for
Windows? (i.e., how can I make API calls to Windows from Python?)
Please bear with me, or not; I am /just/ starting off with Python.
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