I do not know what the problem is because I cannot even reproduce it. I
will fix it as soon as I can figure it out. In the meantime, I've
submitted this pull request to skip the test, so builds do not fail:
[1]https://github.com/scikit-learn/scikit-learn/pull/2415
On Mon, Sep 2, 2013, at 08
Hi all,
Thanks, Nicolas, for starting the introductions. I would also like to
introduce my GSOC project. I'll be adding biclustering capabilities to
scikit-learn: some well-known algorithms such as Spectral Biclustering,
Cheng and Church, and BiMax, as well as scoring metrics and utilities
for g
ple biclusters simultaneously, it does not mask the data with
random noise, and it handles missing values better.
Best,
Kemal
On Mon, Apr 29, 2013 at 4:12 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On Mon, Apr 29, 2013 at 09:20:24AM +0200, Kemal Eren wrote:
> > The Spe
Hi all,
Thanks for your comments. I have made the suggested revisions to my
proposal. A few comments and questions:
Since nsNMF is out, there is still some time available. Any other
algorithms that you would be interested in?
The Spectral coclustering algorithm from 2001 with 888 citations is a
s and examples should be written at the same time as you implement
> the algorithms (not at the end of the GSOC).
>
> Mathieu
>
> On Sun, Apr 28, 2013 at 10:40 PM, Kemal Eren wrote:
>
>> Hi scikit-learn team,
>>
>> As discussed in another thread, I have put tog
regards,
Kemal Eren
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Okay, then I'll put together a biclustering proposal tomorrow after work.
It will be a difficult task to come up with a good set of core algorithms,
because the field is so varied. There are over a hundred published methods,
each of which formulates the biclustering problem differently. Any
particu
Hi Mathieu and team,
If you are looking for biclustering algorithms I could certainly do that. I
did my Master's thesis on it and wrote this software:
http://bmi.osu.edu/hpc/software/bibench/. Its biclustering algorithms are
wrappers to existing tools. It would be really nice to have Python/Cython
done, including adding other stacking
methods such as Feature-Weighted Linear Stacking, supporting various voting
schemes, etc.
This could be a very useful addition to the scikit-learn toolbox. Is there
anyone interested in men
