This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

Simple, lightweight and easy to read implementation of Markov chains and HMMs.

This is a toy project, don’t expect any exciting speeds or robustness.

Happy hacking!


pip3 install git+git://


git clone
cd smarkov
python3 develop

Train with a corpus

from smarkov import Markov
chain = Markov(["AGACAGACGAC"])


corpus: given corpus (a corpus_entry needs to be a tuple or array)
order: maximal order to look back for a given state (default 1) tokenize: function how to split an element of the corpus (e.g sentences into words)

Generate text from a chain


Generate_text() generates exactly one element from the Markov chain. In other words: It goes in the Markov chain the universal start state to universal end state.

More Examples

See examples


Documentation how to use it with HMM.



Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
smarkov-1.0.tar.gz (4.8 kB) Copy SHA256 Checksum SHA256 Source Jan 21, 2016

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