Thanks for putting together this clean implementation! My concern is that Polya-Gamma is not popular enough to warrant inclusion in NumPy, which tries very hard to limit scope these days. For example, Polya-Gamma isn’t implemented in scioy.stats and doesn’t have a Wikipedia page, both of which are generally much more inclusive than NumPy.
On Sun, Dec 27, 2020 at 3:29 AM Tom Swirly <t...@swirly.com> wrote: > I'm just a lurker, but I spent a minute or two to look at that commit, > which looks to be high quality. While I personally have not used this > distribution, people I know use it all the time (for ML). > > > A quibble: > > #define NPY_PI 3.141592653589793238462643383279502884 /* pi */ > > and the following defines which appear > in numpy/random/src/distributions/random_polyagamma.c are already defined > in numpy/core/include/numpy/npy_math.h > > Probably it would be better to include that file instead, if it isn't > already included. > > > DISCLAIMER: I checked none of the math other than passing my eyes over it. > > > > On Sun, Dec 27, 2020 at 12:05 PM Zolisa Bleki <blkzol...@myuct.ac.za> > wrote: > >> Hi All, >> >> I would like to know if Numpy accepts addition of new distributions since >> the implementation of the Generator interface. If so, what is the criteria >> for a particular distribution to be accepted? The reason why i'm asking is >> because I would like to propose adding the Polya-gamma distribution to >> numpy, for the following reasons: >> >> 1) Polya-gamma random variables are commonly used as auxiliary variables >> during data augmentation in Bayesian sampling algorithms, which have >> wide-spread usage in Statistics and recently, Machine learning. >> 2) Since this distribution is mostly useful for random sampling, it since >> appropriate to have it in numpy and not projects like scipy [1]. >> 3) The only python/C++ implementation of the sampler available is >> licensed under GPLv3 which I believe limits copying into packages that >> choose to use a different license [2]. >> 4) Numpy's random API makes adding the distribution painless. >> >> I have done preliminary work on this by implementing the distribution >> sampler as decribed in [3]; see: >> https://github.com/numpy/numpy/compare/master...zoj613:polyagamma . >> There is a more efficient sampling algorithm described in a later paper >> [4], but I chose not to start with that one unless I know it is worth >> investing time in. >> >> I would appreciate your thoughts on this proposal. >> >> Regards, >> Zolisa >> >> >> Refs: >> [1] https://github.com/scipy/scipy/issues/11009 >> [2] https://github.com/slinderman/pypolyagamma >> [3] https://arxiv.org/pdf/1205.0310v1.pdf >> [4] https://arxiv.org/pdf/1405.0506.pdf >> >> >> >> Disclaimer - University of Cape Town This email is subject to UCT >> policies and email disclaimer published on our website at >> http://www.uct.ac.za/main/email-disclaimer or obtainable from +27 21 650 >> 9111. If this email is not related to the business of UCT, it is sent by >> the sender in an individual capacity. Please report security incidents or >> abuse via https://csirt.uct.ac.za/page/report-an-incident.php. >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > > > -- > /t > > PGP Key: https://flowcrypt.com/pub/tom.ritchf...@gmail.com > *https://tom.ritchford.com <https://tom.ritchford.com>* > *https://tom.swirly.com <https://tom.swirly.com>* > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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