Olivier: RidgeCV is based on an eigenvalue decomposition (kernel case) and
an SVD (linear case) so I think it's independent.
Lars: That's a good idea. So we want to minimize \sum_i mu_i (w^T x_i -
y_i)^2 where mu_i is the sample weight. This should be equivalent to \sum_i
(sqrt(mu_i) w^T x_i - sqr
Folks,
what changed in MiniBatchKMeans in .12 ?
Running it on datasets.load_digits() gave 10 classes in .11
but now only 8 in .12 ?
test-mbkmeans.py and logs attached.
(Sure the size is too small for MiniBatch
and for that matter kmeans is I think generally weak, low priority.)
Bytheway datase
Hi Yaroslav.
Thanks for the report.
I didn't know about the deprecation warnings.
For the other warnings: I think using the sklearn.test()
is a bad idea and using ``nosetests sklearn --exe``
should work better.
Also thanks for all the porting, it is really appreciated :)
Cheers,
Andy
On 09/08/2
Hi Andreas,
Exactly! already did that but still have some strange overfitting behaviour
for the intersection kernel that I have to further investigate.
Thanks
On Fri, Sep 7, 2012 at 11:43 AM, Andreas Müller wrote:
> Hi Abdalrahman.
> I am not sure I know what you mean.
> Are you referring to th
On Sun, Sep 9, 2012 at 4:31 PM, Mathieu Blondel wrote:
> I've just tried scipy.sparse.linalg.lsqr [*] on the full news20 dataset.
> On my box it takes 8 seconds to run with tol=1e-3 and 5 seconds with
> tol=1e-2 without any accuracy loss. It also solves the memory problem
> mentioned by Lars, as i
2012/9/9 Mathieu Blondel :
> I've just tried scipy.sparse.linalg.lsqr [*] on the full news20 dataset. On
> my box it takes 8 seconds to run with tol=1e-3 and 5 seconds with tol=1e-2
> without any accuracy loss. It also solves the memory problem mentioned by
> Lars, as it works directly with X and y
2012/9/9 Mathieu Blondel :
> I've just tried scipy.sparse.linalg.lsqr [*] on the full news20 dataset. On
> my box it takes 8 seconds to run with tol=1e-3 and 5 seconds with tol=1e-2
> without any accuracy loss. It also solves the memory problem mentioned by
> Lars, as it works directly with X and y
I've just tried scipy.sparse.linalg.lsqr [*] on the full news20 dataset. On
my box it takes 8 seconds to run with tol=1e-3 and 5 seconds with tol=1e-2
without any accuracy loss. It also solves the memory problem mentioned by
Lars, as it works directly with X and y.
Unlike scipy.linalg.lsqr, scipy.
Thanks Olivier that helped to show me the output, but for the same code as
given before i am not getting proper clusters as shown in the plot below
there are no clearly disparate clusters , the points seems to overlap. But
using heirarchical clustering on same dataset i did find about 7 disparate
