On 6 April 2012 20:05, Joonas Sillanpää wrote:
> Hi!
>
> I just tried to install from source using instructions
> http://scikit-learn.org/stable/install.html I installed all packages and
> did "sudo pip install -U scikit-learn".
> After installing did:
>
> python -c "import sklearn; sklearn.t
On Sat, Apr 7, 2012 at 1:18 AM, Vlad Niculae wrote:
> Hi Shankar
>
> I am also following the PGM class and I would like to stress out that the
> way they implement all the factor operations feels to me to be by no means
> efficient, way too much random memory indexing. However the class seems
> v
There was some discussion along these lines last year, but I don't think
anyone has worked on it yet. Scikit-learn doesn't currently have the
ability to do manifold learning from a precomputed distance matrix, but
it could be extended to that pretty easily.
What it would take would be to modif
On Fri, Apr 06, 2012 at 08:18:45PM +0300, Vlad Niculae wrote:
> Hi Shankar
>
> I am also following the PGM class and I would like to stress out that the way
> they implement all the factor operations feels to me to be by no means
> efficient, way too much random memory indexing. However the clas
I'd like to use isomap (and other manifold learning techniques) with
abstract metric spaces (and perhaps more generally similarity and
dissimilarity matricies - but we can put that aside for the moment).
It looks to me like isomap assumes points are described by points in
R^N or some data structure
Hi Shankar
I am also following the PGM class and I would like to stress out that the way
they implement all the factor operations feels to me to be by no means
efficient, way too much random memory indexing. However the class seems very
insightful, maybe after it ends we will be illuminated as
Hey Shankar,
I respect your decision: it is better for everyone to have less
applications, but well-though out. What you are learning as you go could
help setting up a serious application for next year, hopefully.
Thanks for keeping us updated.
Gael
On Fri, Apr 06, 2012 at 09:41:40PM +0800, Sha
> No LARS is another way to solve the LASSO regression problem that is
> distinct from the Coordinate Descent method (and from the Stochastic
> Gradient Descent method too).
Thanks, I was trying to make the connection but only found a Cholesky solver. :)
---
why not use pymc or r:
- http://code.google.com/p/pymc/
- http://cran.r-project.org/web/views/Bayesian.html
On Fri, Apr 6, 2012 at 9:41 AM, Shankar Satish wrote:
> Hello everyone,
>
> I was supposed to prepare a proposal for bayesian networks in sklearn.
> However as i researched the details f
Le 6 avril 2012 16:32, Immanuel B a écrit :
> Hey Alex,
>> a bonus you could add is logistic regression using L1 + L2. as well as
>> the support of ElasticNet (also L1 + L2) using the Lars algorithm.
> I'm somewhat lost, can you be more specific? Are you referring to strong rule
> support?
No LAR
Hey Alex,
> a bonus you could add is logistic regression using L1 + L2. as well as
> the support of ElasticNet (also L1 + L2) using the Lars algorithm.
I'm somewhat lost, can you be more specific? Are you referring to strong rule
support?
best,
Immanuel
> The benefit you could explicit is that a
Hello everyone,
I was supposed to prepare a proposal for bayesian networks in sklearn.
However as i researched the details further, i realized out that doing a
python implementation will be harder than i thought, primarily due to the
need of many customized data structures.
I have also been follo
Le 6 avril 2012 13:37, João André a écrit :
> Dear All,
>
> Hello. My name is João André and I'm a Portuguese phd student at Oxford
> Brookes University. My subject is risk management of bridges during their
> construction phase.
> I've developed a structural robustness index (which basically weig
Dear All,
Hello. My name is João André and I'm a Portuguese phd student at Oxford
Brookes University. My subject is risk management of bridges during their
construction phase.
I've developed a structural robustness index (which basically weights the
damage accumulation within the structure) which
Hi!
I just tried to install from source using instructions
http://scikit-learn.org/stable/install.html I installed all packages and
did "sudo pip install -U scikit-learn".
After installing did:
python -c "import sklearn; sklearn.test()"
Ran 993 tests in 35.552s
FAILED (SKIP=6, e
LinearSVC doesn't provide it as far as I can tell, however sklearn.svm.SVC,
which does allow you to
set 'probability=True' - which enables probability estimates
This must be done before calling predict_proba.
http://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
J
2
'SVMs do not directly provide probability estimates, these are calculated
using five-fold cross-validation, and thus performance can suffer'
http://scikit-learn.org/dev/modules/svm.html#svm
2012/4/6 Lars Buitinck
> Op 5 april 2012 11:49 heeft het volgende
> geschreven:
> > I was wondering wh
Op 5 april 2012 11:49 heeft het volgende geschreven:
> I was wondering whether it is possible to output probability estimates in
> sklearn.svm.LinearSVC.
>
> The underlying liblinear has the following option:
> -b probability_estimates: whether to output probability estimates, 0 or 1
> (default
Hello all,
I started to use scikit-learn which I find great !
I was wondering whether it is possible to output probability estimates in
sklearn.svm.LinearSVC.
The underlying liblinear has the following option:
-b probability_estimates: whether to output probability estimates, 0 or 1
(default 0
On Apr 6, 2012, at 10:19 , Andreas Mueller wrote:
> On 04/06/2012 08:04 AM, xinfan meng wrote:
>>
>>
>> On Fri, Apr 6, 2012 at 1:57 PM, David Warde-Farley
>> wrote:
>> On 2012-04-05, at 5:17 PM, Vlad Niculae wrote:
>>
>> >
>> > http://ufldl.stanford.edu/wiki/images/8/84/SelfTaughtFeatures.p
On 04/06/2012 08:04 AM, xinfan meng wrote:
On Fri, Apr 6, 2012 at 1:57 PM, David Warde-Farley
mailto:[email protected]>> wrote:
On 2012-04-05, at 5:17 PM, Vlad Niculae mailto:[email protected]>> wrote:
>
> http://ufldl.stanford.edu/wiki/images/8/84/SelfTaughtFeatures.pn
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