On 14 March 2012 05:01, Olivier Grisel wrote:
> Le 13 mars 2012 01:44, Emanuele Olivetti a
> écrit :
> > Hi,
> >
> > I guess the correct link of the video is now:
> >
> http://pyvideo.org/video/622/introduction-to-interactive-predictive-analytics
> > or
> > https://www.youtube.com/watch?v=Zd5dfo
On 03/17/2012 01:55 AM, Lars Buitinck wrote:
> Op 17 maart 2012 01:30 heeft Andreas het
> volgende geschreven:
>> If we change the API, I would go for alpha as the current
>> "scale_C=True" but optionally provide the "C", which behaves
>> like the LibSVM parameter.
> You mean we'd have two regular
Op 17 maart 2012 01:30 heeft Andreas het
volgende geschreven:
> If we change the API, I would go for alpha as the current
> "scale_C=True" but optionally provide the "C", which behaves
> like the LibSVM parameter.
You mean we'd have two regularization parameters? I'd find that confusing.
--
Lar
On 03/17/2012 12:15 AM, Olivier Grisel wrote:
> Le 15 mars 2012 23:43, Nelle Varoquaux a écrit :
>
>> Hi Olivier,
>>
>> On 16 March 2012 02:50, Olivier Grisel wrote:
>>
>>> Here is a WIP PR:
>>>
>>> https://github.com/scikit-learn/scikit-learn/pull/702
>>>
>>> Any comments? Volunteers
On 03/17/2012 12:40 AM, Olivier Grisel wrote:
> Le 16 mars 2012 15:29, Andreas Müller a écrit :
>
>> On 03/16/2012 11:12 PM, James Bergstra wrote:
>>
>>> I was also recently bit by this scale_C business. It looks like the
>>> decision to make scale_C=True the un-changeable default has al
Le 16 mars 2012 15:29, Andreas Müller a écrit :
> On 03/16/2012 11:12 PM, James Bergstra wrote:
>> I was also recently bit by this scale_C business. It looks like the
>> decision to make scale_C=True the un-changeable default has already
>> been made, but when this is done *PLEASE* make this abund
Le 15 mars 2012 23:43, Nelle Varoquaux a écrit :
> Hi Olivier,
>
> On 16 March 2012 02:50, Olivier Grisel wrote:
>>
>> Here is a WIP PR:
>>
>> https://github.com/scikit-learn/scikit-learn/pull/702
>>
>> Any comments? Volunteers to help me fix the last broken stuff?
>
>
> I volunteer ! If it is n
On 03/16/2012 11:12 PM, James Bergstra wrote:
> I was also recently bit by this scale_C business. It looks like the
> decision to make scale_C=True the un-changeable default has already
> been made, but when this is done *PLEASE* make this abundantly clear
> in the documentation... my understanding
I was also recently bit by this scale_C business. It looks like the
decision to make scale_C=True the un-changeable default has already
been made, but when this is done *PLEASE* make this abundantly clear
in the documentation... my understanding was that the C in the SVM
equation means the thing th
Hi Olivier,
The code looks very well written. I think it would fit well in
scikit-learn. The API would have to be modified to fit the scikit-learn
format. You can read more about that at the developers' page:
http://scikit-learn.org/stable/developers/index.html
It will also require some un
On 03/15/2012 06:41 PM, Olivier Grisel wrote:
Le 13 mars 2012 02:32, Olivier Mangin a écrit :
Hello,
Since I am currently using NMF for my robotics research, I have an
implementation of some algorithms from [1]_ that could extend
scikit-learn current implementation.
More precisely the current
Op 16 maart 2012 12:34 heeft Conrad Lee het
volgende geschreven:
> If so, then a couple more questions remain. Does scikit-learn support
> structured arrays, or do those need to be converted to 2-d arrays? Is it
> important for some of the models that the booleans be represented a as
> floats ra
>
> I just added a snippet that works with structured arrays to
> http://scipy-central.org/:
>
>
> http://scipy-central.org/item/35/1/convert-categorical-data-in-a-structure-numpy-array-to-boolean-fields
>
Great Warren, that's exactly what I was looking for. Now there's the
question: should somet
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