MopheusPy is a Python implementation of factorized learning described in paper: Towards Linear Algebra over Normalized Data.

• Python 2.7
• NumPy 1.13
• SciPy 1.0.0
• SciKit Learn
• Python developer kit (C++ rewrite is used)

• To install, simply run python setup.py install.
• To build C++ module, run python setup.py build_ext --inplace.

Simply import NormalizedMatrix like a NumPy matrix. Then apply linear algebra operations on it. E.g

s = np.matrix([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0], [9.0, 1.0]])
k = [np.array([0, 1, 1, 0, 1])]
r = [np.matrix([[1.1, 2.2], [3.3, 4.4]])]
x = np.matrix([[1.0], [2.0], [3.0], [4.0]])

# materialized matrix
m = np.matrix([[1.0, 2.0, 1.1, 2.2],
               [4.0, 3.0, 3.3, 4.4],
               [5.0, 6.0, 3.3, 4.4],
               [8.0, 7.0, 1.1, 2.2],
               [9.0, 1.0, 3.3, 4.4]])
# normalized matrix
n_matrix = nm.NormalizedMatrix(s, r, k)

# result:
n_matrix.dot(x) == m_matrix.dot(x)

add, substract, divide, multiply, dot product, cross product, inverse

This library is implemented as a high-level wrapper. It might conflict with existing machine learning libraries if the libraries skip high-level implementation to optimize linear algebra in C kernel.