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'.
翻译:我们引入了PyHHMM, 这是一种面向目标的开放源码开源Python, 一种面向目标的开放型Python, 一种面向异质的Hidden Markov 模型(HHMMs)的实施。 除了HMM的基本核心功能,例如不同的初始算法和古典观测模型,即连续和多努尔利外, PyHHMM 明显强调一些在类似现有框架中不支持的特征: 异质观测模型、 缺失的数据推断、 不同的示范订单选择标准 和半监督的培训。 这些特征导致研究人员在使用连续数据时, 具有丰富的特性。 PyHMM 依赖numpy、 scipy、 scikt-learn 和海产Python 软件包等基本核心功能。 PyHMMM的源代码在 Github(https://github.com/fmornopino/ Heterogeneous HMMM) 上公开提供采纳和今后的贡献。 详细的文件 (http://pydocs.iodocs.io/en/statp palp pasp pasp) exmmexmexmexmexmexmexmexmexmex) exexexpecument ex, ex, 中可以提供理论解释。