Source code for tvb.analyzers.ica

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Perform Independent Component Analysis on a TimeSeries Object and returns an
IndependentComponents datatype.

.. moduleauthor:: Paula Sanz Leon <Paula@tvb.invalid>


import numpy
import tvb.datatypes.mode_decompositions as mode_decompositions
from tvb.analyzers.ica_algorithm import fastica

Takes a TimeSeries datatype (x) and returns the unmixed temporal sources (S) 
and the estimated mixing matrix (A).
:math: x = A S
ICA takes time-points as observations and nodes as variables.
It uses the FastICA algorithm implemented in the scikit-learn toolkit, and
its intended usage is as a `blind source separation` method.
See also:


[docs]def compute_ica_decomposition(time_series, n_components): """ # type: (TimeSeries, int) -> IndependentComponents Run FastICA on the given time series data. Parameters __________ time_series : TimeSeries The timeseries to which the ICA is to be applied. n_components : int Number of principal components to unmix. """ # problem dimensions data = n_time, n_svar, n_node, n_mode = data.shape n_components = n_comp = n_components or n_node if n_time < n_comp: msg = ("ICA requires more time points (received %d) than number of components (received %d)." " Please run a longer simulation, use a higher sampling frequency or specify a lower" " number of components to extract.") msg %= n_time, n_comp raise ValueError(msg) # ICA operates on matrices, here we perform for all state variables and modes W = numpy.zeros((n_comp, n_comp, n_svar, n_mode)) # unmixing K = numpy.zeros((n_comp, n_node, n_svar, n_mode)) # whitening matrix src = numpy.zeros((n_time, n_comp, n_svar, n_mode)) # component time series for mode in range(n_mode): for var in range(n_svar): sl = Ellipsis, var, mode K[sl], W[sl], src[sl] = fastica(data[:, var, :, mode], n_components) return mode_decompositions.IndependentComponents(source=time_series, component_time_series=src, prewhitening_matrix=K, unmixing_matrix=W, n_components=n_comp)