Class FastICAScikitsLearnNode

FastICA: a fast algorithm for Independent Component Analysis.

This node has been automatically generated by wrapping the ``sklearn.decomposition.fastica_.FastICA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <ICA>`.

**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 : string or function, optional. 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


fun_args : dictionary, optional
    Arguments to send to the functional form.
    If empty and if fun='logcosh', fun_args will take value
    {'alpha' : 1.0}.

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.

random_state : int or RandomState
    Pseudo number generator state used for random sampling.

**Attributes**

``components_`` : 2D array, shape (n_components, n_features)
    The unmixing matrix.

``mixing_`` : array, shape (n_features, n_components)
    The mixing matrix.

``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.

**Notes**

Implementation based on
`A. Hyvarinen and E. Oja, Independent Component Analysis:

Algorithms and Applications, Neural Networks, 13(4-5), 2000,
pp. 411-430`

Overrides: object.__init__

Recover the sources from X (apply the unmixing matrix).

This node has been automatically generated by wrapping the sklearn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape (n_samples, n_features)

Data to transform, where n_samples is the number of samples and n_features is the number of features.

copy : bool (optional)

If False, data passed to fit are overwritten. Defaults to True.

Returns

X_new : array-like, shape (n_samples, n_components)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model to X.

This node has been automatically generated by wrapping the sklearn.decomposition.fastica_.FastICA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.

Returns

self

Overrides: Node.stop_training