- data : np.ndarray, shape (n_samples, n_features)
The whitened data to unmix.
- weights : np.ndarray, shape (n_features, n_features)
The initialized unmixing matrix.
Defaults to None, which means the identity matrix is used.
- l_rate : float
This quantity indicates the relative size of the change in weights.
Defaults to 0.01 / log(n_features ** 2)
.
Note
Smaller learning rates will slow down the ICA procedure.
- block : int
The block size of randomly chosen data segments.
Defaults to floor(sqrt(n_times / 3.)).
- w_change : float
The change at which to stop iteration. Defaults to 1e-12.
- anneal_deg : float
The angle (in degrees) at which the learning rate will be reduced.
Defaults to 60.0.
- anneal_step : float
The factor by which the learning rate will be reduced once
anneal_deg
is exceeded: l_rate *= anneal_step.
Defaults to 0.9.
- extended : bool
Whether to use the extended Infomax algorithm or not.
Defaults to True.
- n_subgauss : int
The number of subgaussian components. Only considered for extended
Infomax. Defaults to 1.
- kurt_size : int
The window size for kurtosis estimation. Only considered for extended
Infomax. Defaults to 6000.
- ext_blocks : int
Only considered for extended Infomax. If positive, denotes the number
of blocks after which to recompute the kurtosis, which is used to
estimate the signs of the sources. In this case, the number of
sub-gaussian sources is automatically determined.
If negative, the number of sub-gaussian sources to be used is fixed
and equal to n_subgauss. In this case, the kurtosis is not estimated.
Defaults to 1.
- max_iter : int
The maximum number of iterations. Defaults to 200.
- random_state : int | np.random.RandomState
If random_state is an int, use random_state to seed the random number
generator. If random_state is already a np.random.RandomState instance,
use random_state as random number generator.
- blowup : float
The maximum difference allowed between two successive estimations of
the unmixing matrix. Defaults to 10000.
- blowup_fac : float
The factor by which the learning rate will be reduced if the difference
between two successive estimations of the unmixing matrix exceededs
blowup
: l_rate *= blowup_fac
. Defaults to 0.5.
- n_small_angle : int | None
The maximum number of allowed steps in which the angle between two
successive estimations of the unmixing matrix is less than
anneal_deg
. If None, this parameter is not taken into account to
stop the iterations. Defaults to 20.
- use_bias : bool
This quantity indicates if the bias should be computed.
Defaults to True.
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).