Metadata-Version: 2.1
Name: jajapy
Version: 0.2
Summary: Baum-Welch for all kind of Markov model
Home-page: UNKNOWN
Author: Raphaël Reynouard
Author-email: raphal20@ru.is
License: MIT
Description: # jajapy
        [![test](https://img.shields.io/github/license/Rapfff/jajapy)](https://en.wikipedia.org/wiki/MIT_License)
        [![test](https://img.shields.io/pypi/v/jajapy)](https://pypi.org/project/jajapy/)
        [![Documentation Status](https://readthedocs.org/projects/jajapy/badge/?version=latest)](https://jajapy.readthedocs.io/en/latest/?badge=latest)
        
        ## Introduction
        `jajapy` is a python library implementing the **Baum-Welch** algorithm on various kinds of Markov models.
        **Warning** `jajapy` is still a WIP. 
        
        ## Main features
        `jajapy` provides:
        - BW algorithm for Hidden Markov Models [reference](https://web.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf)
        - BW algorithm for Markov Chains
        - BW algorithm for Gaussian Observation Hidden Markov Models [reference](http://www.inass.org/2020/2020022920.pdf)
        - BW algorithm for Markov Decision Processes [reference](https://arxiv.org/abs/2110.03014)
        - Active BW algorithm for Markov Decision Processes [reference](https://arxiv.org/abs/2110.03014)
        - BW algorithm for CTMC
        - BW algorithm for asynchronous parallel composition of CTMCs
        
        Additionally, it provides other learning algorithms:
        - Alergia, for Markov Chains [reference](https://www.researchgate.net/publication/2543721_Learning_Stochastic_Regular_Grammars_by_Means_of_a_State_Merging_Method/stats)
        - IOAlergia, for Markov Decision Processes [reference](https://link.springer.com/content/pdf/10.1007/s10994-016-5565-9.pdf)
        
        ## Installation
        ``pip install jajapy``
        
        ## Requirements
        - numpy
        - scipy
        
        ## Documentation
        Available on [readthedoc](https://jajapy.readthedocs.io/en/latest/?)
        
        ## TO DO
        - unit tests
        - generate the documentation. Add examples.
        - link with stormpy, prism
        - error management
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
