Travis Oliphant wrote:
> David Cournapeau wrote:
>
>> Hi,
>>
>> The email from Albert made me look again on some surprising results I
>> got a few months ago when starting my first "serious" numpy project. I
>> noticed that when computing multivariate gaussian densities, centering
>> the data was more expensive than everything else, including
>> exponentiation. Now that I have some experience with numpy, and
>> following the previous discussion, I tried the following script:
>>
>>
>
> Try it again with the new code in SVN.
>
It looks like your modification solved this particular issue !:
10 0.645 0.065 0.645 0.065
storage_email.py:8(center_broadcast)
10 0.625 0.062 0.625 0.062
storage_email.py:26(center_manual)
1 0.333 0.333 0.333 0.333
/usr/lib/python2.4/site-packages/numpy/lib/shape_base.py:530(repmat)
Now, using broadcast is as fast as doing the substraction itself (which
does not include the necessary repmat). I tried it on my laptop, where I
can safely use beta code (python 2.4 + SVN numpy), which explains the
timing differences compared to my previous email; as the memory is
limited on the laptop, I only benchmarked the brodcasting. For smaller
arrays, I couldn't see major differences in relative timing for the
other implementations.
Thank you very much !
David
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