https://github.com/davidar/polya

Hierarchical Bayesian Nonparametrics (Python/C++)
https://github.com/davidar/polya

Last synced: 11 months ago
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Hierarchical Bayesian Nonparametrics (Python/C++)

• Host: GitHub
• URL: https://github.com/davidar/polya
• Owner: davidar
• License: mit
• Created: 2013-04-17T08:17:39.000Z (about 13 years ago)
• Default Branch: master
• Last Pushed: 2021-05-30T03:21:42.000Z (about 5 years ago)
• Last Synced: 2025-06-25T00:03:25.452Z (12 months ago)
• Language: C++
• Homepage:
• Size: 35.5 MB
• Stars: 7
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
![](pyphmm.png)

**Polya** is a Python/C++ library for performing inference with [Pólya urn models](https://en.wikipedia.org/wiki/Polya_urn_model) (including [Chinese restaurant processes](https://en.wikipedia.org/wiki/Chinese_restaurant_process)) with an emphasis on (hierarchical) [Pitman-Yor processes](https://en.wikipedia.org/wiki/Pitman-Yor_process), although [Dirichlet processes](https://en.wikipedia.org/wiki/Dirichlet_process) and [Dirichlet-multinomial](https://en.wikipedia.org/wiki/Dirichlet-multinomial_distribution) (and [Beta-binomial](https://en.wikipedia.org/wiki/Beta-binomial_distribution)) models could be handled as special cases with minor modifications. Nonparametric mixture modelling is also partially supported (only when the number of components is bounded, for now).

Example implementations of the [hierarchical Pitman-Yor language model (HPYLM)](http://dx.doi.org/10.3115/1220175.1220299), the [doubly-HPYLM (DHPYLM)](http://jmlr.org/proceedings/papers/v5/wood09a.html), and the [PYP-HMM](http://aclweb.org/anthology/P11-1087) are also provided:

    $ cd data && python brown.py && python sou.py && cd ..

    $ ./hpylm.py < data/brown.reduc.txt > hpylm.log

    Perplexity: 231.818141085

    $ cat data/{sou.norm.train,brown.norm,sou.norm.test}.txt | \

        ./hpylm.py 3 `wc -w < data/sou.norm.test.txt` > union.log

    Perplexity: 169.793801556

    $ ./dhpylm.py 3 data/{sou.norm.test,sou.norm.train,brown.norm}.txt > dhpylm.log

    Perplexity: 159.532152794

    $ ./pyphmm.py < data/brown.tagged.simp.txt > pyphmm.log

    M-1: 81.1001970389