tensorly.regression.cp_regression
.CPRegressor¶
-
class
CPRegressor
(weight_rank, tol=1e-06, reg_W=1, n_iter_max=100, random_state=None, verbose=1)[source]¶ CP tensor regression
Learns a low rank CP tensor weight
- Parameters
- weight_rankint
rank of the CP decomposition of the regression weights
- tolfloat
convergence value
- reg_Wint, optional, default is 1
regularisation on the weights
- n_iter_maxint, optional, default is 100
maximum number of iteration
- random_stateNone, int or RandomState, optional, default is None
- verboseint, default is 1
level of verbosity
-
fit
(X, y)[source]¶ Fits the model to the data (X, y)
- Parameters
- Xndarray
tensor data of shape (n_samples, N1, …, NS)
- y1D-array of shape (n_samples, )
labels associated with each sample
- Returns
- self