Metadata-Version: 1.1
Name: sklearn-rri
Version: 0.1.0
Summary: scikit-learn compatible classifier based on RRI
Home-page: https://github.com/cmick/sklearn-rri
Author: Michal Ciesielczyk
Author-email: michal.ciesielczyk@put.poznan.pl
License: BSD
Description-Content-Type: UNKNOWN
Description: sklearn-rri
        ===========
        
        Python package providing scikit-learn compatible classifier based on Reflective
        Random Indexing (RRI) [1].
        
        Documentation
        -------------
        The documentation is hosted on http://sklearn-rri.readthedocs.io/
        
        Installation
        ------------
        Latest from the `source <https://github.com/cmick/sklearn-rri>`_::
        
            git clone https://github.com/cmick/sklearn-rri.git
            cd sklearn-rri
            python setup.py install
        
        Using `PyPI <https://pypi.python.org/pypi/sklearn-rri>`_::
        
            pip install sklearn-rri
        
        Dependencies
        ------------
        sklearn-rri requires:
        
        - NumPy (>= 1.11.0)
        - SciPy (>= 0.16.0)
        - scikit-learn (>= 0.17.0)
        
        Examples
        --------
        .. code :: pycon
        
            >>> from sklearn_rri import ReflectiveRandomIndexing
            >>> from sklearn.random_projection import sparse_random_matrix
            >>> X = sparse_random_matrix(100, 100, density=0.01, random_state=42)
            >>> rri = ReflectiveRandomIndexing(50, random_state=42)
            >>> rri.fit(X)
            ReflectiveRandomIndexing(n_components=50, n_iter=3, norm=True,
                    random_state=42, seed='auto')
            >>> rri.transform(X)
            <100x50 sparse matrix of type '<class 'numpy.float64'>'
                    with 1154 stored elements in Compressed Sparse Row format>
        
        References
        ----------
        [1] Trevor Cohen, Roger Schaneveldt, and Dominic Widdows,, Reflective Random
        Indexing and Indirect Inference: A Scalable Method for Discovery of Implicit
        Connections, 2010. https://www.ncbi.nlm.nih.gov/pubmed/19761870
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
