PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
- URL: http://arxiv.org/abs/2201.06968v1
- Date: Wed, 12 Jan 2022 07:32:36 GMT
- Title: PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
- Authors: Fernando Moreno-Pino, Emese S\"ukei, Pablo M. Olmos, and Antonio
Art\'es-Rodr\'iguez
- Abstract summary: PyHHMM is an object-oriented Python implementation of Heterogeneous-Hidden Markov Models (HHMMs)
PyHHMM emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training.
PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License.
- Score: 63.01207205641885
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce PyHHMM, an object-oriented open-source Python implementation of
Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core
functionalities, such as different initialization algorithms and classical
observations models, i.e., continuous and multinoulli, PyHHMM distinctively
emphasizes features not supported in similar available frameworks: a
heterogeneous observation model, missing data inference, different model order
selection criterias, and semi-supervised training. These characteristics result
in a feature-rich implementation for researchers working with sequential data.
PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages,
and is distributed under the Apache-2.0 License. PyHHMM's source code is
publicly available on Github (https://github.com/fmorenopino/HeterogeneousHMM)
to facilitate adoptions and future contributions. A detailed documentation
(https://pyhhmm.readthedocs.io/en/latest), which covers examples of use and
models' theoretical explanation, is available. The package can be installed
through the Python Package Index (PyPI), via 'pip install pyhhmm'.
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