sklearn.decomposition.dict_learning()

X : array of shape (n_samples, n_features)

Data matrix.

n_components : int,

Number of dictionary atoms to extract.

alpha : int,

Sparsity controlling parameter.

max_iter : int,

Maximum number of iterations to perform.

tol : float,

Tolerance for the stopping condition.

method : {?lars?, ?cd?}

lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.

n_jobs : int,

Number of parallel jobs to run, or -1 to autodetect.

dict_init : array of shape (n_components, n_features),

Initial value for the dictionary for warm restart scenarios.

code_init : array of shape (n_samples, n_components),

Initial value for the sparse code for warm restart scenarios.

callback : :

Callable that gets invoked every five iterations.

verbose : :

Degree of output the procedure will print.

random_state : int or RandomState

Pseudo number generator state used for random sampling.

return_n_iter : bool

Whether or not to return the number of iterations.