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        "author": "John Quinn <jquinn@cit.ac.ug>, David Westerhoff <dmwesterhoff@gmail.com>",
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            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Table of Contents\n-----------------\n\n-  `Features`_\n-  `Installation`_\n-  `Usage`_\n-  `Reference`_\n-  `Documentation`_\n-  `History`_\n-  `License`_\n\nFeatures\n--------\n\n-  Compatible with scikit-learn package modules\n-  Probabilistic outlier detection model\n-  Robust classifier when given multiple inlier classes\n-  Easy to install and get started\n\nInstallation\n------------\n\nThe best way to install lsanomaly is to:\n\n::\n\n    pip install lsanomaly\n\nBecause lsanomaly requires scikit-learn it also requires numpy and scipy\ninherintly. Make sure you have successfully installed these packages if you're\nhaving trouble getting lsanomaly to install.\n\nUsage\n-----\n\nFor those familiar with scikit-learn the interface will be familiar, in fact lsanomaly was built to be compatible with sklearn modules where applicable. Here is basic usage of lsanomaly to get started quick as possible.\n\n**Configuring the Model**\n\nThe LSAnomaly provides reasonable default parameters when given an empty init or it can be passed values for rho and sigma. The value rho controls sensitivity to outliers and sigma determines the \u2018smoothness\u2019 of the\nboundary. These values can be tuned to improve your results using lsanomaly.\n\n.. code:: python\n\n    from lsanomaly import LSAnomaly\n\n    # At train time lsanomaly calculates parameters rho and sigma\n    lsanomaly = LSAnomaly()\n    # or alternatively\n    lsanomaly = LSAnomaly(sigma=3, rho=0.1)\n\n**Training the Model**\n\nAfter the model is configured the training data can be fit.\n\n.. code:: python\n\n    import numpy as np\n    lsanomaly.fit(np.array([[1],[2],[3],[1],[2],[3]]))\n\n**Making Predictions**\n\nNow that the data is fit, we will probably want to try and predict on some data not in the training set.\n\n.. code:: python\n\n    >>> lsanomaly.predict([0])\n    [0.0]\n    >>> lsanomaly.predict_proba([0])\n    array([[ 0.5760205,  0.4239795]])\n\nReference\n---------\n\nJ.A. Quinn, M. Sugiyama. A least-squares approach to anomaly detection in static and sequential data. Pattern Recognition Letters 40:36-40, 2014.  \n\n[`pdf`_]\n\nDocumentation\n-------------\n\nComing soon...\n\nHistory\n-------\n\nTo check out the complete release notes see the `changelog`_.\n\nLicense\n-------\n\nThe MIT License (MIT)\n\nCopyright (c) 2016 John Quinn\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the\n\u201cSoftware\u201d), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be included\nin all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \u201cAS IS\u201d, WITHOUT WARRANTY OF ANY KIND, EXPRESS\nOR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL TH\n\n.. _Features: #features\n.. _Installation: #installation\n.. _Usage: #usage\n.. _Documentation: #documentation\n.. _History: #history\n.. _License: #license\n.. _here: https://\n.. _changelog: https://github.com/lsanomaly/lsanomaly/blob/master/CHANGELOG.md\n.. _pdf: http://air.ug/~jquinn/papers/PRLetters_LSAnomalyDetection.pdf\n\n.. |Logo| image:: https://github.com/lsanomaly/lsanomaly/blob/master/docs/logo.png\n.. |PyPI| image:: https://img.shields.io/pypi/v/lsanomaly.svg?maxAge=259200\n          :target: https://pypi.python.org/pypi/lsanomaly\n.. |Language| image:: https://img.shields.io/badge/language-python-blue.svg?maxAge=259200\n.. |Documentation| image:: https://img.shields.io/badge/docs-100%25-brightgreen.svg?maxAge=259200\n.. |License| image:: https://img.shields.io/badge/license-MIT-7f7f7f.svg?maxAge=259200",
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