Hi!
The following is a known "bug" since at least 2010 [1]:
import numpy as np
X = np.ones((50000, 1024), np.float32)
print X.mean()
>>> 0.32768
I ran into this for the first time today as part of a larger program. I
was very surprised by this, and spent over an hour looking for bugs in
my code before noticing that the culprit was `mean` being broken for
large float32 arrays. I realize that this behavior is actually
documented, but it is absolutely non-intuitive. I assume most users
expect `mean` to just work.
This has been discussed once two years ago [2], but nothing came of
that. This could be easily fixed by making `np.float64` the default
dtype (as it already is for integer types), or by at least checking
inside mean if the passed array was a large np.float32 array and switch
the dtype to np.float64 in that case. Is there a reason why this has not
been done?
Cheers
Thomas
[1]
http://mail.scipy.org/pipermail/numpy-discussion/2010-November/053697.html
[2]
http://numpy-discussion.10968.n7.nabble.com/Bug-in-numpy-mean-revisited-td1293.html
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