mojolearn.FastICA#

class mojolearn.FastICA(n_components=None, *, algorithm='parallel', whiten='unit-variance', fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, whiten_solver='svd', random_state=None, numeric_mode=None)#

Independent component analysis with the FastICA algorithm.

Follows scikit-learn’s FastICA. Whitening always uses the eigendecomposition of X.T @ X (whiten_solver is accepted but both values take this route), and the default starting matrix is drawn from mojolearn’s own generator, so results can differ from scikit-learn’s for the same seed.

Parameters:

  • n_components (int or None, default None) – Number of components; None, or a value above min(n_samples, n_features), uses min(n_samples, n_features). Ignored when whiten=False.

  • algorithm ({“parallel”, “deflation”}, default “parallel”) – Estimate all components together, or one at a time.

  • whiten ({“unit-variance”, “arbitrary-variance”} or False, default “unit-variance”) – Whitening strategy; False assumes X is already whitened.

  • fun ({“logcosh”, “exp”, “cube”}, default “logcosh”) – Contrast function. A callable is not supported.

  • fun_args (dict or None, default None) – For "logcosh", {"alpha": a} with 1 <= a <= 2 (default 1.0).

  • max_iter (int, default 200) – Maximum number of iterations.

  • tol (float, default 1e-4) – Convergence tolerance.

  • w_init (array-like of shape (n_components, n_components) or None, default None) – Starting unmixing matrix; None draws one from random_state.

  • whiten_solver ({“svd”, “eigh”}, default “svd”) – Accepted for scikit-learn compatibility; see above.

  • random_state (int or None, default None) – Seed for the default w_init; None means 0.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). In identical mode, mojolearn returns the same bits on Apple, NVIDIA and AMD GPUs. "fast" is available on Apple GPUs only.

Variables:

  • components_ (ndarray of shape (n_components, n_features)) – Unmixing matrix applied by transform.

  • mixing_ (ndarray of shape (n_features, n_components)) – Pseudo-inverse of components_.

  • mean_ (ndarray of shape (n_features,)) – Only when whitening.

  • whitening_ (ndarray of shape (n_components, n_features)) – Only when whitening.

  • n_iter_ (int)

  • n_features_in_ (int)

Raises:

ValueError – For an unsupported algorithm, whiten or fun, alpha outside [1, 2], or a w_init of the wrong shape.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((1000, 8), dtype=np.float32)
>>> S = mojolearn.FastICA(n_components=3, random_state=0).fit_transform(X)

See also

User guide: Decomposition and embeddings

Choosing an estimator

Methods

fit(X[, y])

Fit the model.

fit_transform(X[, y])

Fit the model and return the estimated sources.

get_params([deep])

Return this estimator's constructor parameters.

inverse_transform(X[, copy])

Map sources back to the feature space with mixing_.

set_params(**params)

Set constructor parameters by keyword.

transform(X[, copy])

Recover the sources from X.
fit_transform(X, y=None)#

Fit the model and return the estimated sources.

Parameters:

  • X (array-like of shape (n_samples, n_features))

  • y (ignored)

Returns:

ndarray of shape (n_samples, n_components), float32

fit(X, y=None)#

Fit the model.

Parameters:

  • X (array-like of shape (n_samples, n_features))

  • y (ignored)

Returns:

self

transform(X, copy=True)#

Recover the sources from X.

Parameters:

  • X (array-like of shape (n_samples, n_features))

  • copy (bool, default True) – Accepted for scikit-learn compatibility; X is never modified.

Returns:

ndarray of shape (n_samples, n_components), float32

inverse_transform(X, copy=True)#

Map sources back to the feature space with mixing_.

Parameters:

  • X (array-like of shape (n_samples, n_components))

  • copy (bool, default True) – Accepted for scikit-learn compatibility; X is never modified.

Returns:

ndarray of shape (n_samples, n_features), float32

get_params(deep=True)#

Return this estimator’s constructor parameters.

Parameters:

deep (bool, default True) – Accepted for scikit-learn compatibility; it has no effect.

Returns:

dict – Parameter name to current value.

set_params(**params)#

Set constructor parameters by keyword.

Raises:

ValueError – If a keyword is not a parameter of this estimator.

Returns:

self