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://github.com/greenify/smarkov.git
git clone https://github.com/greenify/smarkov
cd smarkov
python3 setup.py develop
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)
print("".join(chain.generate_text()))
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.
See examples
Documentation how to use it with HMM.
MIT