Hi John, I believe general inference methods are out of scope for scikit-learn. Even general structured learning algorithms are not in scope at the moment, as it's hard to fit problems in numpy arrays. For learning, you might want to check out pystruct [1].
If you just want inference, opengm has a good Python wrapper [2]. It supports many inference methods, some with their own implementations and some by linking to other libraries. Many of these libraries (e.g. the ones that pystruct interfaces with) have their own wrappers too. Hope this helps, Vlad [1] http://pystruct.github.io/ [2] http://hci.iwr.uni-heidelberg.de/opengm2/ On Wed Mar 19 15:50:44 2014, Josh Wasserstein wrote: > Hi, > > I am a big fan of scikit-learn. I have been using it for two years now > in my Ph.D. and later in industry, and I wanted to say thank the > community that makes it possible. > > I recently started to play with PyMC, which is great. However, I > noticed what seems to be a gap in machine learning methods for Python. > > I have been unable to find any variational methods for inference in > Python. For example, belief propagation, loopy belief propagation, > TRW, and the many, many variants of these methods. > > Are there any plans in Scikit-Learn to include support for some of > these algorithms? If not, does anyone have any pointers on where I can > find Python-friendly packages for this type of inference? > > Generally speaking, assuming that you have a e.g. Bayesian network, > and you want to compute the posterior over some variables using > variational methods, how would you proceed with the existing > landscape in Python? > > Thanks a a lot, > > Josh > > > ------------------------------------------------------------------------------ > Learn Graph Databases - Download FREE O'Reilly Book > "Graph Databases" is the definitive new guide to graph databases and their > applications. Written by three acclaimed leaders in the field, > this first edition is now available. Download your free book today! > http://p.sf.net/sfu/13534_NeoTech > > > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Learn Graph Databases - Download FREE O'Reilly Book "Graph Databases" is the definitive new guide to graph databases and their applications. Written by three acclaimed leaders in the field, this first edition is now available. Download your free book today! http://p.sf.net/sfu/13534_NeoTech _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
