`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.

The implementation is based on [1].

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_features_in_int: Number of features seen during fit.

New in version 0.24.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.
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.

See also

PCA: Principal component analysis (PCA).
IncrementalPCA: Incremental principal components analysis (IPCA).
KernelPCA: Kernel Principal component analysis (KPCA).
MiniBatchSparsePCA: Mini-batch Sparse Principal Components Analysis.
SparsePCA: Sparse Principal Components Analysis (SparsePCA).

References

1: 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: Not used, present for API consistency by convention.

Returns

selfobject: Returns the instance itself.

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: Not used, present for API consistency by convention.

Returns

X_newndarray of shape (n_samples, n_components): Estimated sources obtained by transforming the data with the estimated unmixing matrix.

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): Reconstructed data obtained with the mixing matrix.

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): Estimated sources obtained by transforming the data with the estimated unmixing matrix.

Examples using `sklearn.decomposition.FastICA`¶

sklearn.decomposition.FastICA¶

Examples using sklearn.decomposition.FastICA¶

`sklearn.decomposition`.FastICA¶

Examples using `sklearn.decomposition.FastICA`¶