{ "info": { "author": "Dougal J. Sutherland", "author_email": "dsutherl@cs.cmu.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "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 :: Implementation :: CPython", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Image Recognition", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "|Travis|_\n\n.. |Travis| image:: https://api.travis-ci.org/dougalsutherland/skl-groups.png?branch=master\n.. _Travis: https://travis-ci.org/dougalsutherland/skl-groups\n\nskl-groups\n==========\n\nskl-groups is a package to perform machine learning on sets (or \"groups\") of\nfeatures in Python. It extends the `scikit-learn `_\nlibrary with support for either transforming sets into feature vectors that\ncan be operated on with standard scikit-learn constructs or obtaining\npairwise similarity/etc matrices that can be turned into kernels for use in\nscikit-learn.\n\nFor an introduction to the package, why you might want to use it, and how to\ndo so, check out\n`the documentation `_.\n\nskl-groups is still in fairly early development.\nThe precursor package, `py-sdm `_,\nis still somewhat easier to use for some tasks (though it has less functionality\nand less documentation); skl-groups will hopefully match it in the next few weeks.\nFeel free to get in touch (dsutherl@cs.cmu.edu) if you're interested.\n\n\nInstallation\n------------\n\nFull instructions are\n`in the documentation `_,\nbut the short version is to do::\n\n $ conda install -c dougal -c r skl-groups\n\nif you use conda, or::\n\n $ pip install skl-groups\n\nif not. 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