We present a Python package for learning (non-)probabilistic deterministic nite 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 classication 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 elds such as network trac analysis, software engineering and biology, a stratied package opens opportunities for practitioners.
|Title of host publication||Proceedings of The 13th International Conference on Grammatical Inference|
|Subtitle of host publication||The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe)|
|Editors||S. Verwer, M. van Zaanen, R. Smetsers|
|Number of pages||6|
|Publication status||Published - 2016|
|Event||13th International Conference on Grammatical Inference, ICGI 2016 - Delft, Netherlands|
Duration: 5 Oct 2016 → 7 Oct 2016
|Name||JMLR: Workshop and Conference Proceedings|
|Conference||13th International Conference on Grammatical Inference, ICGI 2016|
|Period||5/10/16 → 7/10/16|
- machine learning
- grammar inference
- automaton learning
- PDFA inference
Hammerschmidt, C., Loos, B., State, R., Engel, T., & Verwer, S. (2016). Flexible State-Merging for learning (P)DFAs in Python. In S. Verwer, M. van Zaanen, & R. Smetsers (Eds.), Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe) (Vol. 57, pp. 154-159). (JMLR: Workshop and Conference Proceedings). JMLR.