On Thu, Dec 20, 2012 at 4:45 PM, Alejandro Weinstein
wrote:
> On Thu, Dec 20, 2012 at 3:01 PM, Alejandro Weinstein
> wrote:
>> When clicking in the `plot_lda_vs_qda` link
>> http://scikit-learn.org/stable/auto_examples/plot_lda_vs_qda.html (one
>> of the examples at the
On Thu, Dec 20, 2012 at 3:01 PM, Alejandro Weinstein
wrote:
> When clicking in the `plot_lda_vs_qda` link
> http://scikit-learn.org/stable/auto_examples/plot_lda_vs_qda.html (one
> of the examples at the top of scikit-learn), I get a 404 error (see
> below). The other examples are O
Hi:
When clicking in the `plot_lda_vs_qda` link
http://scikit-learn.org/stable/auto_examples/plot_lda_vs_qda.html (one
of the examples at the top of scikit-learn), I get a 404 error (see
below). The other examples are OK.
An error has been encountered in accessing this page.
1. Server: scikit-l
On Thu, Oct 4, 2012 at 8:40 AM, Gael Varoquaux
wrote:
> On Thu, Oct 04, 2012 at 06:39:00AM -0600, Alejandro Weinstein wrote:
>> Any reason for this behavior?
>
> It's probably because the lars algorithm is unstable with correlated
> regressors.
I tried with a home-made &q
On Mon, Sep 17, 2012 at 11:37 PM, Gael Varoquaux
wrote:
> Indeed, you are right. I had a look at the code, and the reason why this
> happens is to support multiple output (i.e. y being a 2D matrix).
> The relevent code is around line 508 in least_angle.py
>
> That said, it is clearly suboptimal in
Hi:
It seems that the shape of the coef_path_ attribute returned by Lars
is larger than it should.
For instance:
##
import numpy as np
from sklearn import linear_model
from sklearn import datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabete
Hi:
I just notice that the default values of the `normalize` argument for
Lars and Lasso are different. For Lars it is set to True, while for
Lasso is set to False. lasso_path also set the default value of
`normalize` to False.
Since Lars and Lasso are "close cousins", I would expect them to have
On Mon, Sep 17, 2012 at 1:19 PM, Alexandre Gramfort
wrote:
> Option one is to duplicate the doc of Lasso.fit or option 2 remove the
> work "ElasticNet" from the docstring. I am +1 for 2.
Option 2 sounds good to me.
Thanks,
Alejandro
--
Hi:
The docstring of the fit method of the Lasso class says it's fitting
an Elastic Net Model:
###
from sklearn.linear_model import Lasso
rgr = Lasso()
help(rgr.fit)
>>> Help on method fit in module sklearn.linear_model.coordinate_descent:
fit(self, X, y, Xy=Non
On Tue, Jun 5, 2012 at 4:36 PM, Jacob VanderPlas
wrote:
> Hi all,
> Is there any interest to do a scikit-learn sprint at Scipy in Austin
> next month? I will be there, and I have a few ideas brewing that I'd
> love to work on...
> I'd be happy to be the contact person for the conference organizer
On Tue, Apr 3, 2012 at 9:11 PM, Satrajit Ghosh wrote:
> http://julialang.org/
This recent post by John Cook seems relevant:
"Math languages vs. application languages"
http://www.johndcook.com/blog/2012/04/02/why-scipy/
Alejandro.
On Tue, Mar 13, 2012 at 6:37 AM, Shankar Satish wrote:
> Do you think my proposal about implementing reinforcement-learning
> algorithms (subject line: "GSOC project idea: online learning algorithms")
> is something that is well suited for integration into scikit-learn? Do you
> think it makes mor
Hi:
When I run manifold.LocallyLinearEmbedding (using sklearn 0.10), as in
the following code,
###
from sklearn import manifold, datasets
n_points = 1000
n_neighbors = 10
out_dim = 2
X, _ = datasets.samples_generator.ma
Hi:
I am trying to install the latest version of scikit-learn (59db66...).
I cloned the repository, and typed 'make'. One of the unit tests is
failing:
==
FAIL: sklearn.tests.test_multiclass.test_ovr_fit_predict
-
Hi:
There is a new "Sparse- and low-rank approximation wiki", created by
Stephen Becker:
http://ugcs.caltech.edu/~srbecker/wiki/Main_Page
I added a link to the scikit-learn OMP solver:
http://ugcs.caltech.edu/~srbecker/wiki/Category:Greedy_Solvers
And created a scikit-learn page:
http://ugcs.
Hi:
The outcome of some searches in the website points to old versions of
scikit-learn. For instance, if I search for "expectation maximization"
I get
http://scikit-learn.org/0.5/modules/gmm.html
http://scikit-learn.org/0.5/modules/generated/scikits.learn.gmm.GMM.html
Note that it points to vers
On Mon, Nov 7, 2011 at 12:32 PM, Jacob VanderPlas
wrote:
> I think, based on this, that KernelPCA is correct as written, except
> that the arpack method should use which='LA' rather than which='LM'
> (thus ignoring any negative eigenvalues). This would fix Alejandro's
> problem. I'll make the ch
On Wed, Nov 9, 2011 at 5:02 PM, Alejandro Weinstein
wrote:
> Is there a lower bound for n_neighbors when using the the Local
> Tangent Space Alignment method with LLE?
I can solve the problem by using eigen_solver='dense'. Seems to be a
manifestation of the same problem describ
On Wed, Nov 9, 2011 at 4:49 PM, Gael Varoquaux
wrote:
> Is what we have on the front page:
> http://scikit-learn.org/stable/
> not useful? I am thinking in particular of the shorten table of content
> that is visible if you scroll down. I don't really see how it is
> different from the pages that
Hi:
Is there a lower bound for n_neighbors when using the the Local
Tangent Space Alignment method with LLE?
I get this warning
/usr/local/lib/python2.6/dist-packages/scipy/linalg/decomp_lu.py:59:
RuntimeWarning: Diagonal number 198 is exactly zero. Singular matrix.
RuntimeWarning)
when I ru
On Wed, Nov 9, 2011 at 4:30 PM, Gael Varoquaux
wrote:
> Yes, we are not linking back from the docstrings to the narrative
> documentation. On the other hand, the docstrings are meant to be seen in
> an interpretor. Do you expect to find links from the docstrings of the
> Python standard library t
On Wed, Nov 9, 2011 at 3:51 PM, Gael Varoquaux
wrote:
> You should use the table of contents to find what you are looking for:
> http://scikit-learn.org/stable/user_guide.html
> using a search in your browser, you'll find the manifold entry.
But in this page it is not easy to find the detailed do
Hi:
If i go to the Python Module Index
http://scikit-learn.org/stable/py-modindex.html
I cannot find the sklearn.manifold module. Is there a reason for this
to be missing? Where can I find the documentation for this module?
Alejandro
Hi:
I am observing an unexpected behavior of Isomap, related to the
dimensions of the transformed data. If I generate random data, say
1000 points each with dimension 10, and fit a transform using as a
parameter out_dim=3, the fitted data has dimension (1000, 3), as
expected. However, when I repea
On Tue, Oct 18, 2011 at 7:49 PM, Alexandre Gramfort
wrote:
> could you open an issue with a small test script with one X and y that
> produce a different result using both implementations?
Here it is:
https://github.com/scikit-learn/scikit-learn/issues/403
I notice that when it fails, the suppo
On Tue, Oct 18, 2011 at 1:15 AM, Vlad Niculae wrote:
> At the moment I have no idea what the cause is. Does it behave
> in the same way if you use the gram solver instead?
Yes. It behaves in the same way. This is the result of the same
experiment with the addition of p_gram, the probability of re
Hi:
I am observing a behavior of the scikit.learn implementation of OMP
(sklearn.linear_model.orthogonal_mp) that I don't understand. I am
performing the following experiment:
- Generate a dictionary D (input data) with i.i.d. gaussian entries
(with the column norm normalized to one) with dimensi
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