README

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:

- **Discrete-time Hidden Markov Model**
 
 Usually simply referred to as the Hidden Markov Model.

- **Continuous-time Hidden Markov Model**

 The variant of the Hidden Markov Model where the state transition as well as observations occurs in the continuous time.

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%3E>`__,
`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