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 [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
