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            "Intended Audience :: Education",
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            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": ".. image:: logo/logo.png\n    :alt: Optunity\n    :align: left\n\n.. image:: https://travis-ci.org/claesenm/optunity.svg?branch=master\n    :target: https://travis-ci.org/claesenm/optunity\n    :align: right\n\n.. image:: https://readthedocs.org/projects/optunity/badge/?version=latest\n    :alt: Documentation Status\n    :scale: 100%\n    :target: https://readthedocs.org/projects/optunity/\n\n.. image:: https://img.shields.io/pypi/dm/Optunity.svg           \n    :target: https://pypi.python.org/pypi/optunity\n\n.. image:: https://img.shields.io/pypi/v/Optunity.svg            \n    :target: https://pypi.python.org/pypi/optunity\n\n\n=========\n\nOptunity is a library containing various optimizers for hyperparameter tuning.\nHyperparameter tuning is a recurrent problem in many machine learning tasks,\nboth supervised and unsupervised. Tuning examples include optimizing \nregularization or kernel parameters.\n\nFrom an optimization point of view, the tuning problem can be considered as \nfollows: the objective function is non-convex, non-differentiable and \ntypically expensive to evaluate.\n\nThis package provides several distinct approaches to solve such problems including \nsome helpful facilities such as cross-validation and a plethora of score functions.\n\nThe Optunity library is implemented in Python and allows straightforward\nintegration in other machine learning environments, including R and MATLAB.\n\nIf you have any comments, suggestions you can get in touch with us at gitter:\n\n.. image:: https://badges.gitter.im/Join%20Chat.svg\n   :alt: Join the chat at https://gitter.im/claesenm/optunity\n   :target: https://gitter.im/claesenm/optunity?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n\nTo get started with Optunity on Linux, issue the following commands::\n\n    git clone https://github.com/claesenm/optunity.git\n    echo \"export PYTHONPATH=$PYTHONPATH:$(pwd)/optunity\" >> ~/.bashrc\n\nAfterwards, importing ``optunity`` should work in Python::\n\n    #!/usr/bin/env python\n    import optunity\n\nOptunity is developed at the STADIUS lab of the dept. of electrical engineering\nat KU Leuven (ESAT). Optunity is free software, using a BSD license.\n\nFor more information, please refer to the following pages:\nhttp://www.optunity.net\n\nContributors\n============\n\nThe main contributors to Optunity are:\n\n* Marc Claesen: framework design & implementation, communication infrastructure,\n  MATLAB wrapper and all solvers.\n\n* Jaak Simm: R wrapper.",
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