lale.lib.autogen.fast_ica module

class lale.lib.autogen.fast_ica.FastICA(*, n_components=None, algorithm='parallel', whiten='arbitrary-variance', fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None, whiten_solver='svd')

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• n_components (union type, default None) –

  Number of components to use

  • integer, >=2 for optimizer, <=256 for optimizer, uniform distribution

  • or None

• algorithm (‘parallel’ or ‘deflation’, default ‘parallel’) – Apply parallel or deflational algorithm for FastICA.

• whiten (union type, default 'arbitrary-variance') –

  Specify the whitening strategy to use.

  • False

    The data is already considered to be whitened, and no whitening is performed.

  • or ‘arbitrary-variance’

    A whitening with variance arbitrary is used

  • or ‘unit-variance’

    The whitening matrix is rescaled to ensure that each recovered source has unit variance.

• fun (‘cube’, ‘exp’, or ‘logcosh’, default ‘logcosh’) – The functional form of the G function used in the approximation to neg-entropy

• fun_args (None, not for optimizer, default None) – Arguments to send to the functional form

• max_iter (integer, >=1, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 200) – Maximum number of iterations during fit.

• tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.0001) – Tolerance on update at each iteration.

• w_init (None, not for optimizer, default None) – The mixing matrix to be used to initialize the algorithm.

• random_state (union type, not for optimizer, 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.

  • integer

  • or numpy.random.RandomState

  • or None

• whiten_solver (union type, optional, not for optimizer, default 'svd') –

  The solver to use for whitening.

  • ’eigh’

    Generally more memory efficient when n_samples >= n_features, and can be faster when n_samples >= 50 * n_features.

  • or ‘svd’

    More stable numerically if the problem is degenerate, and often faster when n_samples <= n_features.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Training data, where n_samples is the number of samples and n_features is the number of features.

• y (any type) –

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Data to transform, where n_samples is the number of samples and n_features is the number of features.

• y (Any, optional) –

• copy (Any, optional) – If False, data passed to fit are overwritten

Returns

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

Return type

array of items : array of items : float