FastICA

class ibex.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)

Bases: sklearn.decomposition.fastica_.FastICA, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

FastICA: a fast algorithm for Independent Component Analysis.

Read more in the User Guide.

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, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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.

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

fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model to X.

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.

y : Ignored.

self

fit_transform(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model and recover the sources from X.

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.

y : Ignored.

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

inverse_transform(X, copy=True)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Transform the sources back to the mixed data (apply mixing matrix).

X : array-like, shape (n_samples, n_components)
Sources, where n_samples is the number of samples and n_components is the number of components.
copy : bool (optional)
If False, data passed to fit are overwritten. Defaults to True.

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

transform(X, y='deprecated', copy=True)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

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.
y : (ignored)

Deprecated since version 0.19: This parameter will be removed in 0.21.

copy : bool (optional)
If False, data passed to fit are overwritten. Defaults to True.

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