Run (extended) Infomax ICA decomposition on raw data.
np.ndarray
, shape (n_samples, n_features)The whitened data to unmix.
np.ndarray
, shape (n_features, n_features)The initialized unmixing matrix. Defaults to None, which means the identity matrix is used.
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.
int
The block size of randomly chosen data segments. Defaults to floor(sqrt(n_times / 3.)).
float
The change at which to stop iteration. Defaults to 1e-12.
float
The angle (in degrees) at which the learning rate will be reduced. Defaults to 60.0.
float
The factor by which the learning rate will be reduced once
anneal_deg
is exceeded: l_rate *= anneal_step.
Defaults to 0.9.
Whether to use the extended Infomax algorithm or not. Defaults to True.
int
The number of subgaussian components. Only considered for extended Infomax. Defaults to 1.
int
The window size for kurtosis estimation. Only considered for extended Infomax. Defaults to 6000.
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.
int
The maximum number of iterations. Defaults to 200.
None
| int
| instance of RandomState
A seed for the NumPy random number generator (RNG). If None
(default),
the seed will be obtained from the operating system
(see RandomState
for details), meaning it will most
likely produce different output every time this function or method is run.
To achieve reproducible results, pass a value here to explicitly initialize
the RNG with a defined state.
float
The maximum difference allowed between two successive estimations of the unmixing matrix. Defaults to 10000.
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.
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.
This quantity indicates if the bias should be computed. Defaults to True.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
Whether to return the number of iterations performed. Defaults to False.
np.ndarray
, shape (n_features, n_features)The linear unmixing operator.
int
The number of iterations. Only returned if return_max_iter=True
.
References
A. J. Bell, T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159, 1995.
T. W. Lee, M. Girolami, T. J. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 11(2), 417-441, 1999.