Picard : Preconditioned ICA for Real Data
This repository hosts Python/Octave/Matlab code of the Preconditioned ICA for Real Data (Picard) and Picard-O algorithms.
See the documentation.
Picard is an algorithm for maximum likelihood independent component analysis. It shows state of the art speed of convergence, and solves the same problems as the widely used FastICA, Infomax and extended-Infomax, faster.
The parameter ortho choses whether to work under orthogonal constraint (i.e. enforce the decorrelation of the output) or not. It also comes with an extended version just like extended-infomax, which makes separation of both sub and super-Gaussian signals possible. It is chosen with the parameter extended.
- ortho=False, extended=False: same solution as Infomax
- ortho=False, extended=True: same solution as extended-Infomax
- ortho=True, extended=True: same solution as FastICA
- ortho=True, extended=False: finds the same solutions as Infomax under orthogonal constraint.
Picard requires Python >= 3.6.
To install the package, the simplest way is to use pip to get the latest release:
$ pip install python-picard
or to get the latest version of the code:
$ pip install git+https://github.com/pierreablin/picard.git#egg=picard
The Matlab/Octave version of Picard and Picard-O is available here.
To get started, you can build a synthetic mixed signals matrix:
>>> import numpy as np >>> N, T = 3, 1000 >>> S = np.random.laplace(size=(N, T)) >>> A = np.random.randn(N, N) >>> X = np.dot(A, S)
And then use Picard to separate the signals:
>>> from picard import picard >>> 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
These are the dependencies to use Picard:
- numpy (>=1.8)
- matplotlib (>=1.3)
- numexpr (>= 2.0)
- scipy (>=0.19)
These are the dependencies to run the EEG example:
- mne (>=0.14)
If you use this code in your project, please cite:
Pierre Ablin, Jean-Francois Cardoso, Alexandre Gramfort Faster independent component analysis by preconditioning with Hessian approximations IEEE Transactions on Signal Processing, 2018 https://arxiv.org/abs/1706.08171 Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort Faster ICA under orthogonal constraint ICASSP, 2018 https://arxiv.org/abs/1711.10873