yes, and its probably slower to boot. A quick benchmark on my computer shows that:
a = np.zeros([4000,4000],'f4')+500 np.mean(a) takes 0.02 secs np.mean(a,dtype=np.float64) takes 0.1 secs np.mean(a.astype(np.float64)) takes 0.06 secs so casting the whole array is almost 40% faster than setting the accumulator type!, I would imagine having the type-casting being done on the fly during the mean computation would be even faster. On Jun 1, 2011, at 9:11 AM, Bruce Southey wrote: > On 06/01/2011 11:01 AM, Robert Kern wrote: >> On Wed, Jun 1, 2011 at 10:44, Craig Yoshioka<[email protected]> wrote: >>> would anyone object to fixing the numpy mean and stdv functions, so that >>> they always used a 64-bit value to track sums, or so that they used a >>> running calculation. That way >>> >>> np.mean(np.zeros([4000,4000],'f4')+500) >>> >>> would not equal 511.493408? >> Yes, I object. You can set the accumulator dtype explicitly if you >> need it: np.mean(arr, dtype=np.float64) >> > Sure but fails to address that the output dtype of mean in this case is > np.float64 which one would naively assume is also np.float64: >>>> np.mean(np.zeros([4000,4000],'f4')+500).dtype > dtype('float64') > > Thus, we have: > Tickets 465 and 518 > http://projects.scipy.org/numpy/ticket/465 > http://projects.scipy.org/numpy/ticket/518 > > Various threads as well such as: > http://mail.scipy.org/pipermail/numpy-discussion/2010-December/054253.html > > Bruce > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion
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