论文标题
PYHHMM：用于异质隐藏马尔可夫模型的Python库
PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
论文作者
论文摘要
我们介绍了PYHHMM，这是一种面向对象的开源Python实现异质性黑怪Markov模型（HHMMS）。除了HMM的基本核心功能，例如不同的初始化算法和经典观察模型，即连续和多元素，PYHHMM，PYHHMM明显地强调了类似可用的可用框架中不支持的功能：异质观察模型，缺少数据推进，不同的模型订单选择标准，以及半熟练的培训。这些特征导致针对使用顺序数据的研究人员实现功能丰富的实现。 Pyhhmm依靠Numpy，Scipy，Scikit-Learn和Seaborn Python套件，并根据Apache-2.0许可分发。 PYHHMM的源代码可在GitHub（https://github.com/fmorenopino/heterogeneoushmm）上公开获得，可促进收养和未来的贡献。可以使用详细的文档（https://pyhhmm.readthedocs.io/en/latest），其中涵盖了使用示例和模型的理论解释。该软件包可以通过Python软件包索引（PYPI）通过“ PIP安装Pyhhmm”安装。
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'.
