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        "author": "Romain Brault",
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            "Operating System :: POSIX",
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            "Programming Language :: C",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": ".. -*- mode: rst -*-\n\n.. |Travis| image:: https://travis-ci.org/operalib/operalib.svg?branch=master\n.. _Travis: https://travis-ci.org/operalib/operalib\n\n.. |Codecov| image:: https://codecov.io/gh/operalib/operalib/branch/master/graph/badge.svg\n.. _Codecov: https://codecov.io/gh/operalib/operalib\n\n.. |CircleCI| image:: https://circleci.com/gh/operalib/operalib/tree/master.svg?style=shield&circle-token=:circle-token\n.. _CircleCI: https://circleci.com/gh/operalib/operalib\n\n.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg\n.. _Python27: https://github.com/operalib/operalib\n\n.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg\n.. _Python35: https://github.com/operalib/operalib\n\n.. |PyPi| image:: https://badge.fury.io/py/operalib.svg\n.. _PyPi: https://badge.fury.io/py/operalib\n\nOperalib\n========\n|PyPi|_ |Travis|_ |Codecov|_ |CircleCI|_ |Python27|_ |Python35|_\n\nOperalib is a library for structured learning and prediction for\n`python <https://www.python.org>`_ based on operator-valued kernels (OVKs).\nOVKs are an extension of scalar kernels to matrix-valued kernels.\nThe idea is to predict silmultaneously several targets while, for instance,\nencoding the output structure with the operator-valued kernel.\n\nWe aim at providing an easy-to-use standard implementation of operator-valued\nkernel methods. Operalib is designed for compatilibity to\n`scikit-learn <http://scikit-learn.org>`_ interface and conventions.\nIt uses `numpy <http://www.numpy.org>`_,\n`scipy <http://www.scipy.org>`_ and `cvxopt <http://www.cvxopt.org>`_ as\nunderlying libraries.\n\nThe project is developed by the\n`AROBAS <https://www.ibisc.univ-evry.fr/arobas>`_ group of the\n`IBISC laboratory <https://www.ibisc.univ-evry.fr/en/start>`_ of the\nUniversity of Evry, France.\n\nDocumentation\n=============\nIs available at: http://operalib.github.io/operalib/documentation/.\n\nInstall\n=======\nThe package is available on PyPi, and the installation should be as simple as::\n\n  pip install operalib\n\nThis package uses distutils, which is the default way of installing\npython modules. To install in your home directory, use::\n\n  python setup.py install --user\n\nTo install for all users on Unix/Linux::\n\n  python setup.py build\n  sudo python setup.py install\n\n.. For more detailed installation instructions,\n.. see the web page http://scikit-learn.org/stable/install.html\n\nGIT\n~~~\n\nYou can check the latest sources with the command::\n\n    git clone https://github.com/operalib/operalib\n\nor through ssh, instead of https, if you have write privileges::\n\n    git clone git@github.com:operalib/operalib.git\n\nReferences\n==========\nA non-exhaustive list of publications related to operator-valued kernel is\navailable here:\n\nhttp://operalib.github.io/operalib/documentation/reference_papers/index.html.\n",
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