{
    "info": {
        "author": "Lukas Lopatovsky",
        "author_email": "lopatovsky@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: Public Domain",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "Intro\r\n=====\r\n\r\nHMMs is the **Hidden Markov Models library** for *Python*. It is easy to\r\nuse, **general purpose** library, implementing all the important\r\nsubmethods, needed for the training, examining and experimenting with\r\nthe data models.\r\n\r\nThe effectivness of the computationally expensive parts is powered by\r\n*Cython*.\r\n\r\nYou can build two models:\r\n\r\n- **Discrete-time Hidden Markov Model**\r\n \r\n Usually just reffered as the Hidden Markov Model.\r\n\r\n- **Continuous-time Hidden Markov Model**\r\n\r\n The variant of the Hidden Markov Model, where the state transition can occure in the continuous time, and that allows random distribution of the observation times.\r\n\r\nBefore starting to work, it is recommended to go trough **tutorial with\r\nexamples**, `the ipython\r\nnotebook <https://github.com/lopatovsky/CT-HMM/blob/master/hmms.ipynb>`__,\r\ncovering most of the main usecases.\r\n\r\nFor **deeper understanding** of the topic you can see the corresponding\r\n`diploma thesis <https://github.com/lopatovsky/DP>`__. Or read the\r\nmain referenced articles:\r\n`Dt-HMM <http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf%3E>`__,\r\n`Ct-HMM <https://web.engr.oregonstate.edu/~lif/nips2015_CTHMM_learning_camera_ready.pdf>`__\r\n.\r\n\r\n-  Sources of the project:\r\n   `Pypi <https://pypi.python.org/pypi/hmms>`__,\r\n   `Github <https://github.com/lopatovsky/CT-HMM>`__.\r\n\r\nRequirements\r\n------------\r\n\r\n-  python 3.5\r\n-  libraries: Cython, ipython, matplotlib, notebook, numpy, pandas,\r\n   scipy,\r\n-  libraries for testing environment: pytest\r\n\r\nDownload & Install\r\n------------------\r\n\r\nAfter installing Numpy and Cython, you can install the package directly\r\nfrom pypi.\r\n\r\n::\r\n\r\n    (env)$ python -m pip install numpy cython\r\n    (env)$ python -m pip install hmms",
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        "keywords": "Hidden Markov Model,Continuous-time Hidden Markov Model,HMM,CT-HMM,DT-HMM",
        "license": "Public Domain",
        "maintainer": "",
        "maintainer_email": "",
        "name": "hmms",
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        "summary": "Discrete-time and continuous-time hidden Markov model library able to handle hundreds of hidden states",
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