Madison May added the comment: > What do R, SciPy, Fortran, Matlab or other statistical packages already do?
Numpy avoids recalculating the cumulative distribution by introducing a 'size' argument to numpy.random.choice(). The cumulative distribution is calculated once, then 'size' random choices are generated and returned. Their overall implementation is quite similar to the method suggested in the python docs. >>> choices, weights = zip(*weighted_choices) >>> cumdist = list(itertools.accumulate(weights)) >>> x = random.random() * cumdist[-1] >>> choices[bisect.bisect(cumdist, x)] The addition of a 'size' argument to random.choice() has already been discussed (and rejected) in Issue18414, but this was on the grounds that the standard idiom for generating a list of random choices ([random.choice(seq) for i in range(k)]) is obvious and efficient. ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <http://bugs.python.org/issue18844> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: http://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
