ler', 'itakura-saito'}.
    Beta divergence to be minimized, measuring the distance between X
    and the dot product WH. Note that values different from 'frobenius'
    (or 2) and 'kullback-leibler' (or 1) lead to significantly slower
    fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
    matrix X cannot contain zeros.

max_iter : int, default=200
    Number of iterations.

tol : float, default=1e-4
    Tolerance of the stopping condition.

l1_reg_W : float, default=0.
    L1 regularization parameter for W.

l1_reg_H : float, default=0.
    L1 regularization parameter for H.

l2_reg_W : float, default=0.
    L2 regularization parameter for W.

l2_reg_H : float, default=0.
    L2 regularization parameter for H.

update_H : bool, default=True
    Set to True, both W and H will be estimated from initial guesses.
    Set to False, only W will be estimated.

verbose : int, default=0
    The verbosity level.

Returns
-------
W : ndarray of shape (n_samples, n_components)
    Solution to the non-negative least squares problem.

H : ndarray of shape (n_components, n_features)
    Solution to the non-negative least squares problem.

n_iter : int
    The number of iterations done by the algorithm.

References
----------
Lee, D. D., & Seung, H., S. (2001). Algorithms for Non-negative Matrix
Factorization. Adv. Neural Inform. Process. Syst.. 13.
Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix
factorization with the beta-divergence. Neural Computation, 23(9).
r