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:

git clone https://github.com/cmick/sklearn-rri.git
cd sklearn-rri
python setup.py install

Using PyPI:

pip install sklearn-rri

Dependencies

sklearn-rri requires:

• NumPy (>= 1.11.0)
• SciPy (>= 0.16.0)
• scikit-learn (>= 0.17.0)

Examples

>>> 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