scikits.learn.decomposition.FastICA

class scikits.learn.decomposition.FastICA(n_components=None, algorithm='parallel', whiten=True, fun='logcosh', fun_prime='', fun_args={}, max_iter=200, tol=0.0001, w_init=None)

FastICA; a fast algorithm for Independent Component Analysis

Parameters :

n_components : int, optional

Number of components to use. If none is passed, all are used.

algorithm: {‘parallel’, ‘deflation’} :

Apply parallel or deflational algorithm for FastICA

whiten: boolean, optional :

If whiten is false, the data is already considered to be whitened, and no whitening is performed.

fun: {‘logcosh’, ‘exp’, or ‘cube’}, or a callable :

The non-linear function used in the FastICA loop to approximate negentropy. If a function is passed, it derivative should be passed as the ‘fun_prime’ argument.

fun_prime: None or a callable :

The derivative of the non-linearity used.

max_iter : int, optional

Maximum number of iterations during fit

tol : float, optional

Tolerance on update at each iteration

w_init: None of an (n_components, n_components) ndarray :

The mixing matrix to be used to initialize the algorithm.

Notes

Implementation based on : A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430

Attributes

unmixing_matrix_

2D array, [n_components, n_samples]

The unmixing matrix

Methods

get_mixing_matrix() :

Returns an estimate of the mixing matrix

__init__(n_components=None, algorithm='parallel', whiten=True, fun='logcosh', fun_prime='', fun_args={}, max_iter=200, tol=0.0001, w_init=None)

get_mixing_matrix()

Compute the mixing matrix

transform(X)

Apply un-mixing matrix “W” to X to recover the sources

S = W * X