tensorly.decomposition.randomised_parafac

randomised_parafac(tensor, rank, n_samples, n_iter_max=100, init='random', svd='numpy_svd', tol=1e-08, max_stagnation=20, random_state=None, verbose=1)[source]

Randomised CP decomposition via sampled ALS

Parameters:

tensor : ndarray

rank : int

number of components

n_samples : int

number of samples per ALS step

n_iter_max : int

maximum number of iteration

init : {‘svd’, ‘random’}, optional

svd : str, default is ‘numpy_svd’

function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS

tol : float, optional

tolerance: the algorithm stops when the variation in the reconstruction error is less than the tolerance

max_stagnation: int, optional, default is 0

if not zero, the maximum allowed number of iterations with no decrease in fit

random_state : {None, int, np.random.RandomState}, default is None

verbose : int, optional

level of verbosity

Returns:

factors : ndarray list

list of positive factors of the CP decomposition element i is of shape (tensor.shape[i], rank)

References

[R19]Casey Battaglino, Grey Ballard and Tamara G. Kolda, “A Practical Randomized CP Tensor Decomposition”,