[Numpy-discussion] New functions.
Craig Yoshioka
craigyk@me....
Wed Jun 1 11:29:50 CDT 2011
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<craigyk@me.com> 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
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20110601/2d15e269/attachment-0001.html
More information about the NumPy-Discussion
mailing list