learning-hmm: Yet another library for hidden Markov models
[ algorithms, library, machine-learning, mit, statistics ]
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This library provides functions for the maximum likelihood estimation of discrete hidden Markov models. At present, only Baum-Welch and Viterbi algorithms are implemented for the plain HMM and the input-output HMM.
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- learning-hmm-0.3.2.2.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
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| Versions [RSS] | 0.1.0.0, 0.1.1.0, 0.1.1.1, 0.2.0.0, 0.2.1.0, 0.3.0.0, 0.3.0.1, 0.3.1.0, 0.3.1.1, 0.3.1.2, 0.3.1.3, 0.3.2.0, 0.3.2.1, 0.3.2.2 |
|---|---|
| Change log | CHANGES.md |
| Dependencies | base (>=4.7 && <5), containers, deepseq, hmatrix (>=0.16), random-fu, random-source, vector [details] |
| License | MIT |
| Copyright | Copyright (c) 2014-2015 Mitsuhiro Nakamura |
| Author | Mitsuhiro Nakamura |
| Maintainer | Mitsuhiro Nakamura <m.nacamura@gmail.com> |
| Category | Algorithms, Machine Learning, Statistics |
| Home page | https://github.com/mnacamura/learning-hmm |
| Source repo | head: git clone https://github.com/mnacamura/learning-hmm.git |
| Uploaded | by mnacamura at 2015-04-05T13:13:18Z |
| Distributions | |
| Reverse Dependencies | 1 direct, 0 indirect [details] |
| Downloads | 8900 total (3 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
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| Status | Docs available [build log] Last success reported on 2015-04-05 [all 1 reports] |