On 11/10/2011 12:27 PM, Mathieu Blondel wrote:
>
> How many training instances do you have?
>
In the particular example I was thinking of. I had <3k for training, I
think.
> On Nov 10, 2011 7:21 PM, "Andreas Müller" <[email protected]
> <mailto:[email protected]>> wrote:
>
> On 11/10/2011 12:18 AM, Gael Varoquaux wrote:
> > On Wed, Nov 09, 2011 at 11:00:34PM +0100, Andreas Mueller wrote:
> >> As in the other thread, usually one has to scan for parameters
> any way.
> >> Computing every value just once and then storing it seems ok to
> me. For
> >> example, for the chi2 kernel, there is very efficient code
> available by
> >> Christoph Lampert using SSE2 instructions. I used precomputed
> kernel
> >> matrices for multi instance kernels. I could easily implement
> them on
> >> the GPU using batches and then store them one and for all. If I
> had to
> >> do memory transfers for every single example that I need the kernel
> >> for, it would be very slow.
> >> Maybe these are special use cases but I think they are valid ones.
> > They are, but the question is: can they be answered in a toolkit
> meant to
> > be used from Python, where there is a large function-call
> overhead? I
> > don't know the answer to this question, to be fair, I am just
> raising it.
> Maybe I wasn't clear in making my point: I was trying to say
> that computing the whole gram matrix worked just fine for me.
>
> I think the large function call overhead makes other solutions
> impractical.
>
------------------------------------------------------------------------------
RSA(R) Conference 2012
Save $700 by Nov 18
Register now
http://p.sf.net/sfu/rsa-sfdev2dev1
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
