{
    "info": {
        "author": "Michal Ciesielczyk",
        "author_email": "michal.ciesielczyk@put.poznan.pl",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "sklearn-rri\n===========\n\nPython package providing scikit-learn compatible classifier based on Reflective\nRandom Indexing (RRI) [1].\n\nDocumentation\n-------------\nThe documentation is hosted on http://sklearn-rri.readthedocs.io/\n\nInstallation\n------------\nLatest from the `source <https://github.com/cmick/sklearn-rri>`_::\n\n    git clone https://github.com/cmick/sklearn-rri.git\n    cd sklearn-rri\n    python setup.py install\n\nUsing `PyPI <https://pypi.python.org/pypi/sklearn-rri>`_::\n\n    pip install sklearn-rri\n\nDependencies\n------------\nsklearn-rri requires:\n\n- NumPy (>= 1.11.0)\n- SciPy (>= 0.16.0)\n- scikit-learn (>= 0.17.0)\n\nExamples\n--------\n.. code :: pycon\n\n    >>> from sklearn_rri import ReflectiveRandomIndexing\n    >>> from sklearn.random_projection import sparse_random_matrix\n    >>> X = sparse_random_matrix(100, 100, density=0.01, random_state=42)\n    >>> rri = ReflectiveRandomIndexing(50, random_state=42)\n    >>> rri.fit(X)\n    ReflectiveRandomIndexing(n_components=50, n_iter=3, norm=True,\n            random_state=42, seed='auto')\n    >>> rri.transform(X)\n    <100x50 sparse matrix of type '<class 'numpy.float64'>'\n            with 1154 stored elements in Compressed Sparse Row format>\n\nReferences\n----------\n[1] Trevor Cohen, Roger Schaneveldt, and Dominic Widdows,, Reflective Random\nIndexing and Indirect Inference: A Scalable Method for Discovery of Implicit\nConnections, 2010. https://www.ncbi.nlm.nih.gov/pubmed/19761870",
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        "keywords": "",
        "license": "BSD",
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        "name": "sklearn-rri",
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        "summary": "scikit-learn compatible classifier based on RRI",
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