Flexible State-Merging for Learning (P)DFAs in Python


Christian Hammerschmidt, Benjamin Loos, Radu State, Thomas Engel ;
Proceedings of The 13th International Conference on Grammatical Inference, PMLR 57:154-159, 2017.


We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classification to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners.

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