[Numpy-discussion] Memory hungry reduce ops in Numpy
Andreas Müller
amueller@ais.uni-bonn...
Tue Nov 15 10:46:52 CST 2011
On 11/15/2011 04:28 PM, Bruce Southey wrote:
> On 11/14/2011 10:05 AM, Andreas Müller wrote:
>> On 11/14/2011 04:23 PM, David Cournapeau wrote:
>>> On Mon, Nov 14, 2011 at 12:46 PM, Andreas Müller
>>> <amueller@ais.uni-bonn.de> wrote:
>>>> Hi everybody.
>>>> When I did some normalization using numpy, I noticed that numpy.std uses
>>>> more ram than I was expecting.
>>>> A quick google search gave me this:
>>>> http://luispedro.org/software/ncreduce
>>>> The site claims that std and other reduce operations are implemented
>>>> naively with many temporaries.
>>>> Is that true? And if so, is there a particular reason for that?
>>>> This issues seems quite easy to fix.
>>>> In particular the link I gave above provides code.
>>> The code provided only implements a few special cases: being more
>>> efficient in those cases only is indeed easy.
>> I am particularly interested in the std function.
>> Is this implemented as a separate function or an instantiation
>> of a general reduce operations?
>>
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> The'On-line algorithm'
> (http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm)
> <http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm>
> could save you storage. I would presume if you know cython that you
> can probably make it quick as well (to address the loop over the data).
>
My question was more along the lines of "why doesn't numpy do the online
algorithm".
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