def _fit(self, X, compute_sources=False):
"""Fit the model
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
compute_sources : bool
If False, sources are not computes but only the rotation matrix.
This can save memory when working with big data. Defaults to False.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
fun_args = {} if self.fun_args is None else self.fun_args
whitening, unmixing, sources, X_mean, self.n_iter_ = fastica(
X=X, n_components=self.n_components, algorithm=self.algorithm,
whiten=self.whiten, fun=self.fun, fun_args=fun_args,
max_iter=self.max_iter, tol=self.tol, w_init=self.w_init,
random_state=self.random_state, return_X_mean=True,
compute_sources=compute_sources, return_n_iter=True)
if self.whiten:
self.components_ = np.dot(unmixing, whitening)
self.mean_ = X_mean
self.whitening_ = whitening
else:
self.components_ = unmixing
self.mixing_ = linalg.pinv(self.components_)
if compute_sources:
self.__sources = sources
return sources
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