fixed in
https://github.com/scikit-learn/scikit-learn/commit/24e962cfe1c348d0c1de95f546b2091fe75a2c06
Alex
On Mon, Jul 27, 2015 at 7:00 PM, panfei wrote:
> the site is:
> http://scikit-learn.org/stable/auto_examples/plot_cv_predict.html
>
>
> The last line of the example code will cause the gen
RBMs are used for density estimation still - it is just that they are
limited in what they can model in my experience. You should look into the
VAE thing (linked following the texture modeling stuff I sent above) if you
are interested in density modeling - they are pretty cool and seem to do
densit
Thanks, this is helpful.
I have seen RBMs only in pretraining for supervised predictions and was
wondering why they are not used in density estimation. Especially, that
there is an algorithm (CD) to train them in a reasonable amount of time.
Hierarchical bayes might be a better choice for a larger
the site is:
http://scikit-learn.org/stable/auto_examples/plot_cv_predict.html
The last line of the example code will cause the generated plot don't
response (cannot close the plot, cannot zoom in/out it, all I can do is to
restart the kernel of ipython notebook), in my case:
Fedora 22 + Python2
Hi,
i am new to scikit-learn. I am intersted to contribute either by
introducing some new algorithms or optimizing existing algorithms. I have
gone through the issue-tracker and found few interesting topics to work on.
I have posted my question as what is the status of the work and what are
the
RBMs are a factorization of a generally intractable problem - as you
mention it is still O(n**2) but much better than the combinatorial brute
force thing that the RBM factorization replaces. There might be faster RBM
algorithms around but I don't know of any faster implementations that don't
use GP
i am using scikit learn's RBM implementation. There are two problems:
1.
The running time is O(d^2) where d is the number of features. This
becomes a problem in using high dimensionality sparse features. Consider
features that come from feature hashing for instance.
2.
It only
