scot.external package¶
Submodules¶
scot.external.infomax_ module¶
- scot.external.infomax_.infomax(data, weights=None, l_rate=None, block=None, w_change=1e-12, anneal_deg=60.0, anneal_step=0.9, extended=False, n_subgauss=1, kurt_size=6000, ext_blocks=1, max_iter=200, random_state=None, verbose=None)¶
Run the (extended) Infomax ICA decomposition on raw data
based on the publications of Bell & Sejnowski 1995 (Infomax) and Lee, Girolami & Sejnowski, 1999 (extended Infomax)
- Parameters:
- datanp.ndarray, shape (n_samples, n_features)
The data to unmix.
- w_initnp.ndarray, shape (n_features, n_features)
The initialized unmixing matrix. Defaults to None. If None, the identity matrix is used.
- l_ratefloat
This quantity indicates the relative size of the change in weights. Note. Smaller learining rates will slow down the procedure. Defaults to 0.010d / alog(n_features ^ 2.0)
- blockint
The block size of randomly chosen data segment. Defaults to floor(sqrt(n_times / 3d))
- w_changefloat
The change at which to stop iteration. Defaults to 1e-12.
- anneal_degfloat
The angle at which (in degree) the learning rate will be reduced. Defaults to 60.0
- anneal_stepfloat
The factor by which the learning rate will be reduced once anneal_deg is exceeded: l_rate *= anneal_step Defaults to 0.9
- extendedbool
Wheather to use the extended infomax algorithm or not. Defaults to True.
- n_subgaussint
The number of subgaussian components. Only considered for extended Infomax.
- kurt_sizeint
The window size for kurtosis estimation. Only considered for extended Infomax.
- ext_blocksint
The number of blocks after which to recompute Kurtosis. Only considered for extended Infomax.
- max_iterint
The maximum number of iterations. Defaults to 200.
- verbosebool, str, int, or None
If not None, override default verbose level (see mne.verbose).
- Returns:
- unmixing_matrixnp.ndarray of float, shape (n_features, n_features)
The linear unmixing operator.
Module contents¶
External sources and code snippets