On Wed, Aug 31, 2011 at 2:07 PM, Olivier Delalleau <sh...@keba.be> wrote: > You can use: > 1 + numpy.argmax(numpy.random.multinomial(1, [0.1, 0.2, 0.7])) > > For your "real" application you'll probably want to use a value >1 for the > first parameter (equal to your sample size), instead of calling it multiple > times. > > -=- Olivier
Thanks. Warren (Weckesser) mentioned this possibility to me yesterday and I forgot to put it in my post. I assume you mean something like x = np.arange(3) y = np.random.multinomial(30, [0.1,0.2,0.7]) z = np.repeat(x, y) np.random.shuffle(z) That look right? -Chris JS > > 2011/8/31 Christopher Jordan-Squire <cjord...@uw.edu> >> >> In numpy, is there a way of generating a random integer in a specified >> range where the integers in that range have given probabilities? So, >> for example, generating a random integer between 1 and 3 with >> probabilities [0.1, 0.2, 0.7] for the three integers? >> >> I'd like to know how to do this without replacement, as well. If the >> probabilities are uniform, there are a number of ways, including just >> shuffling the data and taking the first however-many elements of the >> shuffle. But this doesn't apply with non-uniform probabilities. >> Similarly, one could try arbitrary-sampling-method X (such as >> inverse-cdf sampling) and then rejecting repeats. But that is clearly >> sub-optimal if the number of samples desired is near the same order of >> magnitude as the total population, or if the probabilities are very >> skewed. (E.g. a weighted sample of size 2 without replacement from >> [0,1,2] with probabilities [0.999,.00005, 0.00005] will take a long >> time if you just sample repeatedly until you have two distinct >> samples.) >> >> I know parts of what I want can be done in scipy.statistics using a >> discrete_rv or with the python standard library's random package. I >> would much prefer to do it only using numpy because the eventual >> application shouldn't have a scipy dependency and should use the same >> random seed as numpy.random. >> >> (For more background, what I want is to create a function like sample >> in R, where I can give it an array-like of doo-hickeys and another >> array-like of probabilities associated with each doo-hickey, and then >> generate a random sample of doo-hickeys with those probabilities. One >> step for that is generating ints, to use as indices, with the same >> probabilities. I'd like a version of this to be in numpy/scipy, but it >> doesn't really belong in scipy since it doesn't >> >> -Chris JS >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
