I don't know if it's relevant. But you should really try the newest version,
which is 0.9.
On Fri, Oct 7, 2011 at 10:52 AM, Ian Goodfellow <[email protected]>wrote:
> I understand that LinearSVC is implemented using liblinear, which I thought
> should work well with large datasets. However, when I pass LinearSVC.fit a
> design matrix of size 40,000 x 14,400 (in float32 format, so 2.3 gigabytes)
> it ends up using at least 8 additional gigabytes of RAM!
> I know that the numpy array passed to scikits needs to be C contiguous to
> avoid it being copied internally. I've checked and mine is, so that's not
> the issue.
> Is it normal for LinearSVC.fit to use so much memory? And if so, is this
> due to some intrinsic requirement of the algorithm, the implementation of
> liblinear, or the implementation of LinearSVC?
> I'm using scikits.learn version 0.4 installed using apt-get in ubuntu
> 11.04, if that's relevant.
>
> Thanks in advance,
> Ian
>
>
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> sense of it. IT sense. And common sense.
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--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
------------------------------------------------------------------------------
All of the data generated in your IT infrastructure is seriously valuable.
Why? It contains a definitive record of application performance, security
threats, fraudulent activity, and more. Splunk takes this data and makes
sense of it. IT sense. And common sense.
http://p.sf.net/sfu/splunk-d2dcopy2
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