learning-hmm: Yet another library for hidden Markov models

[ algorithms, library, machine-learning, mit, statistics ] [ Propose Tags ]

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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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 && <4.8), containers, deepseq, hmatrix (<0.16.1), 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 2015-03-13T07:55:01Z
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Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 8681 total (36 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2015-03-13 [all 1 reports]