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On 11/15/2011 04:28 PM, Bruce Southey wrote:
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On 11/14/2011 10:05 AM, Andreas Müller wrote:
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<pre wrap="">On 11/14/2011 04:23 PM, David Cournapeau wrote:
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<pre wrap="">On Mon, Nov 14, 2011 at 12:46 PM, Andreas Müller
<a moz-do-not-send="true" class="moz-txt-link-rfc2396E" href="mailto:amueller@ais.uni-bonn.de"><amueller@ais.uni-bonn.de></a> wrote:
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<pre wrap="">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:
<a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://luispedro.org/software/ncreduce">http://luispedro.org/software/ncreduce</a>
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.
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<pre wrap="">The code provided only implements a few special cases: being more
efficient in those cases only is indeed easy.
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<pre wrap="">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<span class="mw-headline" id="On-line_algorithm"> 'On-line
algorithm</span>' (<a moz-do-not-send="true"
href="http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm">http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm)</a>
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).<br>
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<br>
My question was more along the lines of "why doesn't numpy do the
online algorithm".<br>
<br>
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