6.10.3. scikits.learn.fastica.FastICA¶
- class scikits.learn.fastica.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] 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