Thanks! And does it make sense to use L1 regularisation here (irrespective
of the graph structure)?
Best,
Mathias
On Mon, Feb 20, 2012 at 11:06 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On Mon, Feb 20, 2012 at 10:35:51AM +0100, Mathias Verbeke wrote:
> > I wo
Hi Olivier,
Thanks for the fast reply!
> I have a high-dimensional feature set, where the features originate from
> > graphs. I was wondering if the use of GraphLasso applies and would be a
> good
> > idea in this case? And if it would be, can I then just apply it on the
> > feature vectors or do
Hi all,
I have a high-dimensional feature set, where the features originate from
graphs. I was wondering if the use of GraphLasso applies and would be a
good idea in this case? And if it would be, can I then just apply it on the
feature vectors or do I need to input the originating graph structure
Hi Andreas,
You would have to add it to the "fit" method of SVC, not GridSearchCV.
>
How can this be done in the digits example, since there's only one fit
there, namely the one of GridSearch?
> > Does this mean class weighting isn't possible at all with GridSearch?
> At the moment, yes.
>
> If
ght should be moved to
> the initialization of SVC.
> I am (somewhat) working on this.
>
> Cheers,
> Andy
>
>
>
> On 02/03/2012 01:23 PM, Mathias Verbeke wrote:
>
> Hi Olivier,
>
> That's something I tried already, but then I get:
>
> AssertionError: I
Hi Olivier,
That's something I tried already, but then I get:
AssertionError: Invalid parameter class_weight for estimator SVC
Any idea what can be wrong?
Thanks,
Mathias
On Fri, Feb 3, 2012 at 12:19 PM, Olivier Grisel wrote:
> 2012/2/3 Mathias Verbeke :
> > Hi Adreas,
> &
stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV>)
> that decides exactly that.
> It is "True" by default.
>
> Cheers,
> Andy
>
>
> On 02/03/2012 10:54 AM, Mathias Verbeke wrote:
>
> Hi all,
>
> I'm currentl
Hi all,
I'm currently looking at the GridSearch example (
http://scikit-learn.org/0.9/auto_examples/grid_search_digits.html), and I
don't completely get the point of using cross-validation twice. Why aren't
the parameters and the classifier selected in on cross-validations step?
Furthermore, I wa
hon2.6/dist-packages/scipy/sparse/csr.py", line 299, in
_get_row_slice
raise IndexError('index (%d) out of range' % i )
IndexError: index (1) out of range
Could this have something to do with the sparse vectors?
Thanks,
Mathias
On Sat, Jan 28, 2012 at 9:12 PM, Olivier Gris
Hi Oliver,
Thanks, that works! Sorry for the dummy questions.
Cheers and thanks again,
Mathias
On Sat, Jan 28, 2012 at 12:38 PM, Olivier Grisel
wrote:
> 2012/1/28 Mathias Verbeke :
> > Hi Gael,
> >
> > Thanks for your quick answer. Your solution solved the error, but
ne 230,
in asarray
return array(a, dtype, copy=False, order=order)
ValueError: setting an array element with a sequence.
Would you have any idea how this is caused?
Thanks,
Mathias
On Sat, Jan 28, 2012 at 9:49 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On S
Ok, thanks a lot!
Cheers,
Mathias
On Fri, Jan 27, 2012 at 2:45 PM, Andreas wrote:
> **
> On 01/27/2012 02:34 PM, Mathias Verbeke wrote:
>
> Dear Andy,
>
> I'm currently using version 0.9.
>
> Then you should upgrade ;)
> It is available in Version .10
> Andy
>
>
> On 01/27/2012 02:29 PM, Mathias Verbeke wrote:
>
> Dear all,
>
> When I want to set the cache_size of an SVM, using
>
> clf = svm.SVC(cache_size=200.0)
>
> I get: TypeError: __init__() got an unexpected keyword argument
> 'cache_size',
Dear all,
When I want to set the cache_size of an SVM, using
clf = svm.SVC(cache_size=200.0)
I get: TypeError: __init__() got an unexpected keyword argument
'cache_size', althought this should be possible according to the
documentation.
What am I doing wrong here? Or isn't this possible anymore
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
Hi,
First, thanks for all the answers! Waauw, really interesting discussion. I
have only basic Python skills, and never programmed in Cython (together
with a lot of time constraints, as most of you probably), but I would like
to give it a try to add new distance metrics to the brute force method.
Dear all,
I just started working with Scikit Learn and I'm currently using the
Nearest Neighbors module. In the documentation is stated that it currently
only supports the Euclidean distance metric, and I was wondering if it
would be easy to extend it with other distance metrics? Since it uses the
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