tensorly.decomposition.randomised_parafac
- randomised_parafac(tensor, rank, n_samples, n_iter_max=100, init='random', svd='truncated_svd', tol=1e-08, max_stagnation=20, return_errors=False, random_state=None, verbose=0, callback=None)[source]
Randomised CP decomposition via sampled ALS [3]
- Parameters:
- tensorndarray
- rankint
number of components
- n_samplesint
number of samples per ALS step
- n_iter_maxint
maximum number of iteration
- init{‘svd’, ‘random’}, optional
- svdstr, default is ‘truncated_svd’
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
- tolfloat, 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
- return_errorsbool, default is False
if True, return a list of all errors
- verboseint, optional, default is 0
level of verbosity
- Returns:
- factorsndarray list
list of positive factors of the CP decomposition element i is of shape
(tensor.shape[i], rank)
References
[3]Casey Battaglino, Grey Ballard and Tamara G. Kolda, “A Practical Randomized CP Tensor Decomposition”,