An acceptably pythonic bayesian natural language processing / discrete machine learning library.

Key goals are simplicity, speed, elegance, and conciseness of user code. This project is mainly intended as a research platform - something really simple to use to build a quick model of language while the math is being derived. Since so much time is spent in this discovery phase, the runtime hit of python (obviated a bit by transparent optimizations) shouldn't be too much of a burden.

A naive bayes, a maximum entropy classifier, and an exact (as-in, viterbi-decoded) hidden markov model are implemented. All rely on a mixture of C and python, with rough cython alternatives.

Core code is mostly restricted to python, although inner loops are optimized in C (see maxent.c for an example). Fallbacks to python are possible although they currently do not happen gracefully. If you don't feel like compiling the c code (simple as running setup.py build!), you can edit the first line of counter.py to change to the python counter type. This doesn't work for the maximum entropy model, nor any other model that accesses an optimized C API the c counters export.

My current focus is on fleshing out chain models and generalizing some of the shared code (higher-order class code could be designed to be generic, as would the viterbi decoder and any future approximate inference schemes). Following that, I'm hoping to start tackling some real datasets and see if I can dig up anything cool

I'm also looking at porting the c layer to cython, which appears, so far, to lead to a 2-3x slowdown (still hundreds of times faster than python) with very little total effort (compared to the moderately optimized, from a python module standpoint, C). Porting to cython would also buy better 3.0 compatibility (I think), but I haven't looked at this one bit.

Parallel computation, through map-reduce paradigm or the multi-processing module would be interesting (and very nice for slow-as-hell POS tagger testing), but I haven't really seen a huge enough reason for it to write it. If I ever get a machine with more than 2 cores this will be priority #1.

The code is licensed under the GNU GPL v2. If you're interested in using the code under a different license please send a message my github account and we'll work something out (I'm very friendly to this - I'm licensing under GPL only because it's the one I know best, and I'm not some BSD hippy).

(cue the licensing)

Copyright (C) 2009 Matt Jones

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.