This is a library to run the QNDIAG algorithm [1]. This algorithm exploits a state-of-the-art quasi-Newton strategy for approximate joint diagonalization of a list of matrices.
To install qndiag:
$ pip install qndiag
If you do not have admin privileges on the computer, use the --user flag
with pip. To upgrade, use the --upgrade flag provided by pip.
To check if everything worked fine, you can do:
$ python -c 'import qndiag'
and it should not give any error message.
The easiest way to get started is to copy the following lines of code in your script:
>>> import numpy as np
>>> from qndiag import qndiag
>>> n, p = 10, 3
>>> diagonals = np.random.uniform(size=(n, p))
>>> A = np.random.randn(p, p) # mixing matrix
>>> C = np.array([A.dot(d[:, None] * A.T) for d in diagonals]) # dataset
>>> B, _ = qndiag(C)
>>> print(B.dot(A)) # Should be a permutation + scale matrix
Use the github issue tracker to report bugs.
[1] P. Ablin, J.F. Cardoso and A. Gramfort. Beyond Pham’s algorithm for joint diagonalization. Proc. ESANN 2019. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-119.pdf https://hal.archives-ouvertes.fr/hal-01936887v1 https://arxiv.org/abs/1811.11433