Re: [Scikit-learn-general] Scikit learn's multiprocessing

2014-06-25 4:50 GMT+02:00 Sturla Molden sturla.mol...@gmail.com: In general, only POSIX APIs are safe to use on both sides of a fork Actually, only a short list of async-signal-safe library routines [1, 2]. Practically all of POSIX is off-limits after fork in a multithreaded program. [1]

[Scikit-learn-general] Sparse NMF

Hello, I have worked a bit on the sparse NMF model proposed by Hoyer [1]. The paper is mentioned in the Scikits NMF module but AFAIK the model is currently not implemented. Recently, we proposed an efficient algorithm based on block coordinate descent [2]. A reference python implementation is

Re: [Scikit-learn-general] Sparse NMF

Yes, thats a different sparse model than the one in Hoyer, 2004 (most likely the one by Kim and Park). Maybe, Vlad or someone else can comment on that. ~Vamsi. On Wed, Jun 25, 2014 at 10:15 AM, Alexandre Gramfort alexandre.gramf...@telecom-paristech.fr wrote: hi, have you played with the

Re: [Scikit-learn-general] Sparse NMF

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/nmf.py#L346 References -- This implements C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779.

Re: [Scikit-learn-general] Sparse NMF

Hi, Allow me to clarify. We don't implement Hoyer's sparse update rule indeed (it shouldn't say this implements, I initially cited Hoyer for motivating sparseness constraints in NMF). Instead, we implement a version of sparse NMF with a clear (but not particularly elegant) objective function,

Re: [Scikit-learn-general] Sparse NMF

i will never post a docstring again :) sorry for the noise michael On Wednesday, June 25, 2014, Vlad Niculae zephy...@gmail.com wrote: Hi, Allow me to clarify. We don't implement Hoyer's sparse update rule indeed (it shouldn't say this implements, I initially cited Hoyer for motivating

Re: [Scikit-learn-general] Sparse NMF

Vamsi, do you have any benchmarks for your implementation? The biggest reason why we didn't change the current implementation yet is that it was hard to find something else that is consistently faster better. Here are some benchmarks I did last time I looked at this, when I wanted to replace the

Re: [Scikit-learn-general] Sparse NMF

Vlad, if you are going to cite the Mazack paper, you should also look into the original paper by Kim and Park (2006) which proposes the sparse NMF model ( the one which is currently implemented by Scikit-learn). For the sparse NMF model by Hoyer (2004), some benchmarks can be found in my ICLR

Re: [Scikit-learn-general] Help for learning/contributing

Hello, Today I dropped by the freenode channel, but no one was around at the time, so I'm relaying my message here, thanks for reading :) I’m a math undergraduate who works as a python developer. I’m currently between jobs, so I have one month worth of spare time and i was thinking about

Re: [Scikit-learn-general] Help for learning/contributing

Hi Ignacio, A good starting place is often working on the documentation. For example, https://github.com/scikit-learn/scikit-learn/pull/3084 is an attempt at filling in a gap in the documentation, but it doesn't look like Raul is going to complete the work any time soon. If you want to pull his

[Scikit-learn-general] question on img_to_graph

Hi, Would you know if img_to_graph is using 4-neighbourhoods in 2D, and 8-neigbourhoods in 3D, or 8 and 26? http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.img_to_graph.html#sklearn.feature_extraction.image.img_to_graph Thank you for your help, Kind regards,

Re: [Scikit-learn-general] Help for learning/contributing

Hi Joel, Is that along the lines of the sort of things you would like to start on? Yes, I think these look good. I'll start looking into them and pop around here or github if anything comes up. Thanks Ignacio 2014-06-25 16:54 GMT-03:00 Joel Nothman joel.noth...@gmail.com: Hi Ignacio, A

Re: [Scikit-learn-general] question on img_to_graph

On 25/06/2014 22:00, Kevin Keraudren wrote: Hi, Would you know if img_to_graph is using 4-neighbourhoods in 2D, and 8-neigbourhoods in 3D, or 8 and 26?

[Scikit-learn-general] SVC.predict_proba result inconsistent with SVC.predict result

Hello all, I have the following code: . . . . # 'train' is a (M,N) numpy array (input) and 'traint' is a (M,) numpy array (target/label) clf = SVC(kernel=rbf, C=1.74, gamma=0.0023, probability=True) clf.fit(train, traint) print clf.classes_# Ensure our classes are [0,1] t1 =

Re: [Scikit-learn-general] question on img_to_graph

Thanks! Le 25/06/2014 22:36, bthirion a écrit : On 25/06/2014 22:00, Kevin Keraudren wrote: Hi, Would you know if img_to_graph is using 4-neighbourhoods in 2D, and 8-neigbourhoods in 3D, or 8 and 26?