To be pragmatic I would: go for MDP.
Gael
- Original message -
> >
> > I don't have the Bishop, and I must confess that I am still confused by
> > the Wikipedia. That said, it doesn't really matter. As long as people
> > feel confident that it is well defined and useful, it belongs to th
2012/1/19 Joris A. :
>
> In the meantime, any good libs to recommend? MDP?
Yes have a look at MDP and maybe also scikits.statsmodels which is
focused more classical statistics for finance and economics than
scikit-learn.
--
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel
>
> I don't have the Bishop, and I must confess that I am still confused by
> the Wikipedia. That said, it doesn't really matter. As long as people
> feel confident that it is well defined and useful, it belongs to the
> scikit, and I am all for it :).
>
> Gael
>
>
>
Thank you all!
Let's hope it w
2012/1/19 Alexandre Gramfort :
> +1 for FA. It's standard and indeed very similar to ProbabilisticPCA
> that we have.
>
> Now we need a volunteer :)
Is there a way to implement it in a scalable way (w.r.t n_samples and
n_features and n_factors / n_components)?
Because if it fallbacks to the defau
+1 for FA. It's standard and indeed very similar to ProbabilisticPCA
that we have.
Now we need a volunteer :)
Alex
On Thu, Jan 19, 2012 at 7:31 AM, Gael Varoquaux
wrote:
> On Wed, Jan 18, 2012 at 11:53:01PM +0100, Andreas wrote:
>> > Factor analysis is a decomposition with a particular
>> > ass
On Wed, Jan 18, 2012 at 11:53:01PM +0100, Andreas wrote:
> > Factor analysis is a decomposition with a particular
> > assumption about the noise.
> > See Bishop page 583
> For a bit more detail:
> The idea is that the data is a linear transform of some
> underlying, lower dimensional data plus so
FA gets used a lot in finance for getting tractable factorizations of large
covariance matrices. Definitely would be very useful to have in sklearn.
On Wed, Jan 18, 2012 at 5:56 PM, David Warde-Farley <
[email protected]> wrote:
> On Wed, Jan 18, 2012 at 11:49:07PM +0100, Andreas wrote:
On Wed, Jan 18, 2012 at 11:49:07PM +0100, Andreas wrote:
> > Actually, I am not sure what FA means. For me ICA, PCA, or any
> > decomposition model is an FA. Joris, what do you have in mind in
> > particular?
> >
> >
> Factor analysis is a decomposition with a particular
> assumption about the
On 01/18/2012 11:49 PM, Andreas wrote:
> On 01/18/2012 11:45 PM, Gael Varoquaux wrote:
>
>> On Wed, Jan 18, 2012 at 11:41:24PM +0100, Andreas wrote:
>>
>>
>>> As far as I can tell, FA is not implemented yet.
>>>
>>>
>> Actually, I am not sure what FA means. For me ICA, PCA, o
On 01/18/2012 11:45 PM, Gael Varoquaux wrote:
> On Wed, Jan 18, 2012 at 11:41:24PM +0100, Andreas wrote:
>
>> As far as I can tell, FA is not implemented yet.
>>
> Actually, I am not sure what FA means. For me ICA, PCA, or any
> decomposition model is an FA. Joris, what do you have in
On Jan 19, 2012, at 00:45 , Gael Varoquaux wrote:
> On Wed, Jan 18, 2012 at 11:41:24PM +0100, Andreas wrote:
>> As far as I can tell, FA is not implemented yet.
>
> Actually, I am not sure what FA means. For me ICA, PCA, or any
> decomposition model is an FA. Joris, what do you have in mind in
On Wed, Jan 18, 2012 at 11:41:24PM +0100, Andreas wrote:
>As far as I can tell, FA is not implemented yet.
Actually, I am not sure what FA means. For me ICA, PCA, or any
decomposition model is an FA. Joris, what do you have in mind in
particular?
Gael
Hi Joris.
As far as I can tell, FA is not implemented yet.
Thanks for pointing that out. It should definitely be included.
Cheers,
Andy
On 01/18/2012 02:51 PM, Joris A. wrote:
Hello All,
Sorry if it's stupid but it's not so obvious to me.
Is it possible to perform a factorial analysis with sk
Hello All,
Sorry if it's stupid but it's not so obvious to me.
Is it possible to perform a factorial analysis with sklearn or do I have to
use other libraries?
Thanks and regards,
Joris
--
Keep Your Developer Skills Curre
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