documentation is less
ambiguous.
If you still see issues room for improvement don't hesitate to share your
insights.
Cheers,
Denis
On Tue, Sep 24, 2013 at 3:56 PM, Shishir Pandey wrote:
> On 24-09-2013 18:03, Denis-Alexander Engemann wrote:
>
> Hi Shishir,
>
>
it as you would regular PCA + additional arguments +
extended application.
HTH,
Denis
Also see:
Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal
component analysis." Journal of the Royal Statistical Society: Series B
(Statistical Methodology) 61.3 (1999
your custom criterion.
HTH,
Denis
Thanks
On Sun, Sep 8, 2013 at 2:18 PM, Gael Varoquaux
mailto:gael.varoqu...@normalesup.org>> wrote:
On Sun, Sep 08, 2013 at 01:40:49AM +0500, Safi Ullah Marwat wrote:
> My question, Is there any way to find mixing matrix for the new data using
> exi
.8 this even speeds up the test-suite, if only about a few seconds.
Before we can feel comfortable to merge this new functionality additional
benchmarks and reviews would be highly appreciated.
I'm currently at the EuroSciPy -- don't hesitate to address me in case you have
any que
Hi, I'd like to improve ICA as discussed in #2113.
Also I'd like to inquire memory behavior of the decomposition classes to better
support their combined application on bog data sets with more than 100k samples.
Also I'd like to take a look at API inconsistencies between those classes, e.g
with r
(If you have even a few labeled data points, try SVM ?
SGDClassifier is really fast.)
cheers
-- denis
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cheers
-- denis
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msec sparse-sparse binary search
6.87 msec sparse-sparse hash map
4.19 msec sparse-sparse incremental
1.67 msec scipy
(My "speedup" is wy slower except for dot( very sparse, very sparse ) --
testbench 1, my intuition 0.)
ch
w fast this is will depend so strongly on cache / VM
that it'll be hard to compare.
No I haven't tried different sizes / sparsities much --
where can we collect numbers from *real* data ?
cheers
-- denis
-
Olivier Grisel writes:
>
> 2012/12/27 denis :
> > Folks,
> > does any module in scikit-learn do dot( sparse vec, sparse vec ) a lot ?
> > I wanted to try out a fast dot_sparse_vec (time ~ nnz, space ~ n)
> > but so far I see only safe_sparse_dot( big sparse arr
Folks,
does any module in scikit-learn do dot( sparse vec, sparse vec ) a lot ?
I wanted to try out a fast dot_sparse_vec (time ~ nnz, space ~ n)
but so far I see only safe_sparse_dot( big sparse array, numpy array )
e.g. for RandomPCA.
Thanks,
cheers / sante / Prost Neujahr
-- denis
more
interpretable when you display it compared to if you use random order of
variables.
Thanks and regards,
Denis.
Sent from Samsung tablet--
Monitor your physical, virtual and cloud infrastructure from a single
web
return f_f64( A, B )
...
(This may be more of a cython question, but you sklearn people must
do this often ?)
thanks,
cheers
-- denis
--
Monitor your physical, vi
0 .8 ...
Toy 2D yet informative datasets ?
Maybe fun but I think a chimera, and 2D is not nD.
A couple more *real* datasets should be trivial ?
cheers
-- denis
--
Monitor your physical, virtual and cloud
f the values are all positive you can even beat that,
running sum >= nearest so far -> quit early, return Toofar.
(Algorithms are more fun, inner loops important.)
For clustering (don't know your application)
you might look at Markov clustering, http://www.micans.org/mcl .
cheers
--
735 |res| av 2.64 max 13.8 coef None ExtraTreesRegressor
R2 .716 |res| av 2.65 max 15.6 coef av -.391 max 2.35 SGDRegressor
...
test-regress.{py,log,sum} are in github.com/denis-bz/sklearn-regress .
Perhaps this could be on the way to an examples/try-regress.py
to help people underst
Hehe..
Just for logging purposes:
I saw theses old SDKs under /Developer which were conflicting with the recent
10.8 SDK from /Applications
So
sudo /Developer/Library/uninstall-devtools --mode=all
did the trick.
Denis
Ok... I think I got it. Xcode path is heavily messed up...
Thanks though (sometimes just posting it helps to get it ;-).
D
sense to me.
Any pointers would be appreciated.
Thanks,
Denis
On Sat, Nov 10, 2012 at 12:23 PM, Gael Varoquaux
mailto:gael.varoqu...@normalesup.org>> wrote:
Hi Freddie,
On Thu, Nov 08, 2012 at 08:13:25PM -0800, F
181 177 174 170 150 147 130]
seed 2: clusters [288 220 201 183 179 165 158 149 136 118]
seed 3: clusters [342 229 204 178 176 168 153 148 108 91]
seed 4: clusters [398 197 187 178 178 171 165 125 107 91]
shows how poor kmeans is here; is it good anywhere ?
cheers
-- denis
(Dumb question, w
KMeans is exactly the same in .11 and .12
cheers
-- denis
On 10/09/2012 10:58, Andreas Müller wrote:
> Hi Denis.
> That is weird. Unfortunately I don't have time to investigate ATM.
> Maybe someone else does?
> Also, I thought we would reinitializ
generally weak, low priority.)
Bytheway datasets.load_digits() is only the 1797 uciml/optdigits test,
not the 5620 train+test in mldata uci-20070111-optdigits .
(Sorry if this shows up twice, going in circles
"Post by non-member to a members-only list".)
cheers,
bon weekend
188 176 110]
cluster sizes MiniBatchKmeans: [378 313 246 199 187 179 175 120]
cheers
-- denis
On 10/09/2012 10:37, Andreas Mueller wrote:
> On 09/10/2012 09:31 AM, Nelle Varoquaux wrote:
--
Live Security Virtual Conf
datasets.load_digits() is only the 1797 uciml/optdigits test,
not the 5620 train+test in mldata uci-20070111-optdigits .
cheers,
bon weekend
-- denis
""" test MiniBatchKMeans on digits """
# http://scikit-learn.org/stable/auto_examples/document_clustering.html
#
vm ...]
at the top of each q ? or do you have other ways of tracking everything ?
cheers
-- denis
I've always had a bad memory, as far back as I can remember.
-- Lewis Thomas
--
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usually-not-good-for-sparse-data
But I think scaling should be left to users --
even 3 or 4 sizes cannot fit all data.
Florian, is your data images,
could you say "scale to [0:1] when ..." ?
Thanks, cheers
-- denis
-
Asked here because wasn't able to find such a comparison anywhere else.
Denis.
On 04.12.2011 17:50, David Warde-Farley wrote:
> When I last tried out Orange, it was very much a C++ library trying and
> failing to masquerade as a Python library. The API was complicated and
> prosa
x27;t license my
software, probably it's not important if embedded lib is BSD or GPL
(just a guess).
Denis.
--
All the data continuously generated in your IT infrastructure
contains a definitive record of cust
Hi Brian,
Thanks, all points are quite important for me (for most users, I think).
Performance problems are surprising, considering Orange is mainly C++.
Denis.
On 04.12.2011 16:57, bdho...@gmail.com wrote:
> Hi Denis,
>
> My main motivation is mostly usability. In terms of developme
long term.
Denis.
> Hi Denis,
>
> I my opinion here are the main reasons why scikit-learn cannot reuse orange:
>
> - scikit-learn is a scikit (scientific python toolkit): it is meant to
> be used by he scipy community and to play by its tacit rules: the
> primary data st
ry much for any comments.
Denis.
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All the data continuously generated in your IT infrastructure
contains a definitive record of customers, application performance,
security threats, fraudulent activity, and more. Sp
gs ) [0]
return d
cheers
-- denis
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All the data continuously generated in your IT infrastructure
contains a definitive record of customers, application performance,
security threats, fraudulent activity, and
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