sklearn.decomposition
.FastICA¶

class
sklearn.decomposition.
FastICA
(n_components=None, *, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None)[source]¶ FastICA: a fast algorithm for Independent Component Analysis.
Read more in the User Guide.
 Parameters
 n_componentsint, default=None
Number of components to use. If None is passed, all are used.
 algorithm{‘parallel’, ‘deflation’}, default=’parallel’
Apply parallel or deflational algorithm for FastICA.
 whitenbool, default=True
If whiten is false, the data is already considered to be whitened, and no whitening is performed.
 fun{‘logcosh’, ‘exp’, ‘cube’} or callable, default=’logcosh’
The functional form of the G function used in the approximation to negentropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. Example:
def my_g(x): return x ** 3, (3 * x ** 2).mean(axis=1)
 fun_argsdict, default=None
Arguments to send to the functional form. If empty and if fun=’logcosh’, fun_args will take value {‘alpha’ : 1.0}.
 max_iterint, default=200
Maximum number of iterations during fit.
 tolfloat, default=1e4
Tolerance on update at each iteration.
 w_initndarray of shape (n_components, n_components), default=None
The mixing matrix to be used to initialize the algorithm.
 random_stateint, RandomState instance or None, default=None
Used to initialize
w_init
when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls. See Glossary.
 Attributes
 components_ndarray of shape (n_components, n_features)
The linear operator to apply to the data to get the independent sources. This is equal to the unmixing matrix when
whiten
is False, and equal tonp.dot(unmixing_matrix, self.whitening_)
whenwhiten
is True. mixing_ndarray of shape (n_features, n_components)
The pseudoinverse of
components_
. It is the linear operator that maps independent sources to the data. mean_ndarray of shape(n_features,)
The mean over features. Only set if
self.whiten
is True. n_iter_int
If the algorithm is “deflation”, n_iter is the maximum number of iterations run across all components. Else they are just the number of iterations taken to converge.
 whitening_ndarray of shape (n_components, n_features)
Only set if whiten is ‘True’. This is the prewhitening matrix that projects data onto the first
n_components
principal components.
Notes
Implementation based on A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(45), 2000, pp. 411430
Examples
>>> from sklearn.datasets import load_digits >>> from sklearn.decomposition import FastICA >>> X, _ = load_digits(return_X_y=True) >>> transformer = FastICA(n_components=7, ... random_state=0) >>> X_transformed = transformer.fit_transform(X) >>> X_transformed.shape (1797, 7)
Methods
fit
(X[, y])Fit the model to X.
fit_transform
(X[, y])Fit the model and recover the sources from X.
get_params
([deep])Get parameters for this estimator.
inverse_transform
(X[, copy])Transform the sources back to the mixed data (apply mixing matrix).
set_params
(**params)Set the parameters of this estimator.
transform
(X[, copy])Recover the sources from X (apply the unmixing matrix).

fit
(X, y=None)[source]¶ Fit the model to X.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 yIgnored
 Returns
 self

fit_transform
(X, y=None)[source]¶ Fit the model and recover the sources from X.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 yIgnored
 Returns
 X_newndarray of shape (n_samples, n_components)

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

inverse_transform
(X, copy=True)[source]¶ Transform the sources back to the mixed data (apply mixing matrix).
 Parameters
 Xarraylike of shape (n_samples, n_components)
Sources, where n_samples is the number of samples and n_components is the number of components.
 copybool, default=True
If False, data passed to fit are overwritten. Defaults to True.
 Returns
 X_newndarray of shape (n_samples, n_features)

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.

transform
(X, copy=True)[source]¶ Recover the sources from X (apply the unmixing matrix).
 Parameters
 Xarraylike of shape (n_samples, n_features)
Data to transform, where n_samples is the number of samples and n_features is the number of features.
 copybool, default=True
If False, data passed to fit can be overwritten. Defaults to True.
 Returns
 X_newndarray of shape (n_samples, n_components)