**UPDATE 2023/Feb/27** Direct Pypi installation is now fixed.
Intro
=======
HMMs is the **Hidden Markov Models library** for *Python*.
It is easy to use **general purpose** library implementing all the important
submethods needed for the training, examining and experimenting with
the data models.
The computationally expensive parts are powered by
*Cython* to ensure high speed.
The library supports the building of two models:
<dl>
<dt>Discrete-time Hidden Markov Model</dt>
<dd>Usually simply referred to as the Hidden Markov Model.</dd>
<dt>Continuous-time Hidden Markov Model</dt>
<dd>The variant of the Hidden Markov Model where the state transition as well as observations occurs in the continuous time. </dd>
</dl>
Before starting work, you may check out **the tutorial with examples**. [the ipython notebook](https://github.com/lopatovsky/CT-HMM/blob/master/hmms.ipynb), covering most of the common use-cases.
For **the deeper understanding** of the topic refer to the corresponding [diploma thesis](https://github.com/lopatovsky/DP).
Or read some of the main referenced articles: [Dt-HMM](http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf), [Ct-HMM](https://web.engr.oregonstate.edu/~lif/nips2015_CTHMM_learning_camera_ready.pdf) .
- Sources of the project:
[Pypi](https://pypi.python.org/pypi/hmms),
[Github](https://github.com/lopatovsky/CT-HMM),
Requirements
-------------
- python 3.5
- libraries: Cython, ipython, matplotlib, notebook, numpy, pandas, scipy,
- libraries for testing environment: pytest
Download & Install
-------------------
The Numpy and Cython must be installed before installing the library package from pypi.
```
(env)$ python -m pip install numpy cython
(env)$ python -m pip install hmms
```
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