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 neg-entropy. 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=1e-4

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 to np.dot(unmixing_matrix, self.whitening_) when whiten is True.

mixing_ndarray of shape (n_features, n_components)

The pseudo-inverse 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 pre-whitening 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(4-5), 2000, pp. 411-430

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
Xarray-like 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
Xarray-like 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
Xarray-like 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
Xarray-like 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)