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https://github.com/maxmouchet/HMMBase.jl
Hidden Markov Models for Julia.
https://github.com/maxmouchet/HMMBase.jl
hidden-markov-models hmm julia statistics
Last synced: 2 months ago
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Hidden Markov Models for Julia.
- Host: GitHub
- URL: https://github.com/maxmouchet/HMMBase.jl
- Owner: maxmouchet
- License: mit
- Archived: true
- Created: 2019-01-07T12:10:13.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-14T17:34:49.000Z (about 1 year ago)
- Last Synced: 2024-03-26T02:04:05.316Z (3 months ago)
- Topics: hidden-markov-models, hmm, julia, statistics
- Language: Julia
- Homepage:
- Size: 5.38 MB
- Stars: 91
- Watchers: 8
- Forks: 12
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- awesome-julia - Hidden Markov Models for Julia
README
Hidden Markov Models for Julia.
## Status
HMMBase is not maintained anymore. It will keep being available as a Julia package but we encourage existing and new users to migrate to [HiddenMarkovModels.jl](https://github.com/gdalle/HiddenMarkovModels.jl) which offers a similar interface. For more information see [HiddenMarkovModels.jl: when did HMMs get so fast?](https://discourse.julialang.org/t/ann-hiddenmarkovmodels-jl-when-did-hmms-get-so-fast/100191).
## Introduction
HMMBase provides a lightweight and efficient abstraction for hidden Markov models in Julia. Most HMMs libraries only support discrete (e.g. categorical) or Normal distributions. In contrast HMMBase builds upon [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) to support arbitrary univariate and multivariate distributions.
See [HMMBase.jl - A lightweight and efficient Hidden Markov Model abstraction](https://discourse.julialang.org/t/ann-hmmbase-jl-a-lightweight-and-efficient-hidden-markov-model-abstraction/21604) for more details on the motivation behind this package.
Benchmark of HMMBase against hmmlearn and pyhsmm.
**Features:**
- Supports any observation distributions conforming to the [Distribution](https://juliastats.org/Distributions.jl/latest/types/) interface.
- Fast and stable implementations of the forward/backward, EM (Baum-Welch) and Viterbi algorithms.
**Non-features:**
- Multi-sequences HMMs, see [MS_HMMBase](https://github.com/mmattocks/MS_HMMBase.jl)
- Bayesian models, probabilistic programming, see [Turing](https://github.com/TuringLang/Turing.jl)
- Nonparametric models (HDP-H(S)MM, ...)
## Installation
The package can be installed with the Julia package manager.
From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
```
pkg> add HMMBase
```
## Documentation
- [**STABLE**][docs-stable-url] — **documentation of the most recently tagged version.**
- [**DEVEL**][docs-dev-url] — *documentation of the in-development version.*
## Project Status
The package is tested against Julia 1.0 and the latest Julia 1.x.
Starting with v1.0, we follow [semantic versioning]():
> Given a version number MAJOR.MINOR.PATCH, increment the:
> 1. MAJOR version when you make incompatible API changes,
> 2. MINOR version when you add functionality in a backwards compatible manner, and
> 3. PATCH version when you make backwards compatible bug fixes.
## Questions and Contributions
Contributions are very welcome, as are feature requests and suggestions. Please read the [CONTRIBUTING.md](/CONTRIBUTING.md) file for informations on how to contribute. Please open an [issue][issues-url] if you encounter any problems.
*Logo: lego by jon trillana from the Noun Project.*
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg?style=flat
[docs-stable-url]: https://maxmouchet.github.io/HMMBase.jl/stable
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg?style=flat
[docs-dev-url]: https://maxmouchet.github.io/HMMBase.jl/dev
[issues-url]: https://github.com/maxmouchet/HMMBase.jl/issues