what is implemented in the scikit matches the algorithms implemented in SPAMS.
SPAMS implements more like positivity constraints for example.
A real comparison in terms of speed and results has however not been done.
Note that for the positivity constraint we just need to modify
LassoLARS and Lass
2012/1/9 Mathias Verbeke :
> Dear all,
>
> In the documentation of the SVM module, I saw that it was possible to pass
> your own Gram matrix to the kernel. I was wondering if it was also possible
> to do the reverse, i.e. to export the calculated Gram matrix (that gives the
> similarity between the
Dear all,
In the documentation of the SVM module, I saw that it was possible to pass
your own Gram matrix to the kernel. I was wondering if it was also possible
to do the reverse, i.e. to export the calculated Gram matrix (that gives
the similarity between the train and test instances)?
Best and
Short answer, no.
sparse_encode is just a wrapper for funcionality that existed in the
scikit already (lasso, omp), with support for parallelization. We
couldn't embed SPAMS anyway, because of the license IIRC.
A benchmark would be interesting indeed.
Vlad
On 09.01.2012, at 18:02, Ian Goodfell
Is decomposition.sparse_encode implemented using SPAMS (
http://www.di.ens.fr/~mairal/software_eng.php ) ? If not, does anyone
know how the two implementations compare?
Thanks,
Ian
--
Ridiculously easy VDI. With Citrix VDI
2012/1/9 Olivier Grisel :
> That would work. Alternatively we could have:
>
> algorithm="csvm" or algorithm="nusvm" as already used elsewhere
> (e.g. neighbors, pls, dict_learning and manifold).
+1.
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
---
On 01/09/2012 12:19 PM, Mathieu Blondel wrote:
> On Mon, Jan 9, 2012 at 7:40 PM, Gael Varoquaux
> wrote:
>
>
>> Can it be that if nu is None C is used, or do you thihnk that this is
>> confusing?
>>
> That wouldn't work for NuSVR, which uses both C and nu (I know, it's
> confusing).
>
On Mon, Jan 09, 2012 at 08:19:33PM +0900, Mathieu Blondel wrote:
> > Can it be that if nu is None C is used, or do you thihnk that this is
> > confusing?
> That wouldn't work for NuSVR, which uses both C and nu (I know, it's
> confusing).
That's exactly what I had in mind: I was proposing nu to b
On Mon, Jan 9, 2012 at 7:40 PM, Gael Varoquaux
wrote:
> Can it be that if nu is None C is used, or do you thihnk that this is
> confusing?
That wouldn't work for NuSVR, which uses both C and nu (I know, it's confusing).
If we merge C-SVM and nu-SVM classes, users may think that they can
obtain
2012/1/9 Fabian Pedregosa :
> On Mon, Jan 9, 2012 at 2:00 AM, Olivier Grisel
> wrote:
>> Hi all,
>>
>> As discussed earlier, here is a new tool to publish the doc on github
>> rather than sourceforge.
>>
>> The result is available here:
>>
>> https://github.com/scikit-learn/scikit-learn.org (the
2012/1/9 Gael Varoquaux :
> On Mon, Jan 09, 2012 at 11:11:29AM +0100, Lars Buitinck wrote:
>> 2012/1/9 Fabian Pedregosa :
>> > I'm OK with the idea of having one class for classification and one
>> > for regression. It's conceptually easier and simplifies the docs. +1
>
>> What should the parameter
On Mon, Jan 09, 2012 at 11:11:29AM +0100, Lars Buitinck wrote:
> 2012/1/9 Fabian Pedregosa :
> > I'm OK with the idea of having one class for classification and one
> > for regression. It's conceptually easier and simplifies the docs. +1
> What should the parameter be called that chooses nu vs. C?
2012/1/9 Fabian Pedregosa :
> I'm OK with the idea of having one class for classification and one
> for regression. It's conceptually easier and simplifies the docs. +1
What should the parameter be called that chooses nu vs. C?
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
On Mon, Jan 9, 2012 at 4:08 PM, Peter Prettenhofer
wrote:
> ``ravel`` is only used in the binary case so it is not responsible for
> the copy.
I was referring to ravel() in
https://github.com/scipy/scipy/blob/master/scipy/sparse/compressed.py#L264,
not in scikit-learn.
Mathieu
On Mon, Jan 9, 2012 at 10:56 AM, Olivier Grisel
wrote:
> Thanks :)
>
> Can someone switch the DNS?
I think you'll have to ask Stefan van der Walt
--
Ridiculously easy VDI. With Citrix VDI-in-a-Box, you don't need a compl
Thanks :)
Can someone switch the DNS?
--
Olivier
--
Ridiculously easy VDI. With Citrix VDI-in-a-Box, you don't need a complex
infrastructure or vast IT resources to deliver seamless, secure access to
virtual desktops. W
On Sun, Jan 8, 2012 at 11:06 PM, Andreas wrote:
> Hey everybody.
> @larsmans (my personal hero for the day) started refactoring the SVM
> class structure here:
> https://github.com/larsmans/scikit-learn/commits/refactor-svm
> after some discussion here:
> https://github.com/scikit-learn/scikit-lea
On Mon, Jan 9, 2012 at 2:00 AM, Olivier Grisel wrote:
> Hi all,
>
> As discussed earlier, here is a new tool to publish the doc on github
> rather than sourceforge.
>
> The result is available here:
>
> https://github.com/scikit-learn/scikit-learn.org (the repo for the
> tool, basically a README.
I've just sent a PR which implements Oliviers solution - The overhead
(converting to f-style) only applies to multi-class classification so
I think its ok to do it this way. SGDClassifier currently does not
support `partial_fit` only via the `init_coef` and `init_intercept`
arguments, they are auto
19 matches
Mail list logo
