Picard

This is a library to run the Preconditioned ICA for Real Data (PICARD) algorithm [1] and its orthogonal version (PICARD-O) [2]. These algorithms show fast convergence even on real data for which sources independence do not perfectly hold.

Installation

We recommend the Anaconda Python distribution. Otherwise, to install picard, you first need to install its dependencies:

$ pip install numpy matplotlib numexpr scipy

Then install Picard:

$ pip install python-picard

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 picard'

and it should not give any error message.

Quickstart

The easiest way to get started is to copy the following lines of code in your script:

>>> import numpy as np
>>> from picard import picard
>>> N, T = 3, 1000
>>> S = np.random.laplace(size=(N, T))
>>> A = np.random.randn(N, N)
>>> X = np.dot(A, S)
>>> K, W, Y = picard(X)  

Picard outputs the whitening matrix, K, the estimated unmixing matrix, W, and the estimated sources Y. It means that:

\[Y = W K X\]

Bug reports

Use the github issue tracker to report bugs.

Cite

[1] Pierre Ablin, Jean-Francois Cardoso, and Alexandre Gramfort “Faster independent component analysis by preconditioning with Hessian approximations” IEEE Transactions on Signal Processing, 2018, https://arxiv.org/abs/1706.08171

[2] Pierre Ablin, Jean-Francois Cardoso, and Alexandre Gramfort “Faster ICA under orthogonal constraint” ICASSP, 2018, https://arxiv.org/abs/1711.10873

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