Hi James,
I had a similar problem - my approach was to wrap the sparse matrix into
another sparse matrix interface that applies the centering on the spot
when computing dot products. It builds on the same rationale as the
scipy.linalg.LinearOperator that's often used in optimization. I used it
for
On 14 October 2013 20:48, Robert McGibbon wrote:
[...]
>
> p.s. core devs: pretty please don't remove the HMM code from the scikit :)
>
+1E6
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Hey,
I presume this is too specialized for inclusion in the scikit, but I've
written a von Mises HMM, suitable for processes on angles/directions where
you have periodic boundary conditions.
The code is on github, BSD-licensed, etc. I'd be happy to make a PR if
there's interest, but it's not nece
John, you're right about the difference in nomenclature. I've been using
scikit-learn's names for the parameters, so the alpha I've referred to
is the regularization strength and corresponds to lambda in glmnet. The
mixing parameter, referred to in glmnet as alpha, is the L1-ratio in
scikit-lea
I meant "l1_ratio" instead of "l1_ration".
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alpha is the strength of the regularizer and l1_ration is the mixing
weight. "lambda" is a reserved keyword in python, hence the use of
alpha instead. But this is very confusing and I which we had used a
common English name like "penalty_strength" instead.
http://scikit-learn.org/stable/modules/ge
Hi James,
In R speak:
The reason you see the advice to choose a higher alpha if nobs < nvars and
a lower alpha if the comparison is that alpha is the mixing weight between
L1 and L2 penalties (whereas lambda is the regularization level) and
because the L1 penalty tends to set more coefficients to
Please have a look at the contributors guide:
http://scikit-learn.org/stable/developers/#contributing-code
In particular this doc mentions [Easy] tagged issues:
https://github.com/scikit-learn/scikit-learn/issues?labels=Easy
But in general the best way to contribute is to actually use the
libra
Hi,
I am Ankit Agrawal, a 4th year undergrad majoring in EE with
specialization in Communications and Signal Processing at IIT Bombay. I
completed my GSoC with scikit-image this year and have a good grasp with
Python(and a little bit with Cython). I have completed a course in ML, and
have tak
Hi, James:
If by 'alpha' you mean what the lasso literature refers to as 'lambda', my
recollection is that the maximum lambda is determined simply by the L1 norm
of the coefficients of the ordinary least squares solution, because any
value greater than that provides no constraint for the lasso sol
2013/10/14 James Jensen :
> I've noticed people use the term "normalize" in different ways. In the case
> of the `normalize=True` flag of the linear models, does it mean both scaling
> samples to have unit norm and centering them to have mean zero? If so, this
> is inconsistent with the usage in, s
Thanks, Alex. That is helpful. Looks like the glmnet documentation says
that this is how they do it as well. What they don't explain is how to
find alpha_max in the first place. The only thing I've thought of is
doing something like a binary search until you find the smallest alpha
yielding the
Thank you, Olivier.
Just to clarify: you say
You can control the centering with `normalize=True` flag of the
ElasticNet class (or any other linear regression model).
I've noticed people use the term "normalize" in different ways. In the
case of the `normalize=True` flag of the linear mod
Actually the mrec implementation is not the original SLIM algorithm
but a variant demonstrated by the lib author here:
http://slideshare.net/MarkLevy/efficient-slides
Thanks @larsmans for the tweet :)
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Olivier
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Oct
2013/10/14 Nick Pentreath :
> Mendeley have also recently open-sourced their recommender framework, which
> relies on SGD to train models using scikit-learn, and seems to try to fit
> into the sklearn API.
>
> https://github.com/Mendeley/mrec/
Thanks for the hint. Has anybody some feedback to shar
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