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
- get_params(**kwargs)[source]
Returns a dictionary of parameters
- predict(X)[source]
Returns the predicted labels for a new data tensor
- Parameters:
- Xndarray
tensor data of shape (n_samples, N1, …, NS)
- set_params(**parameters)[source]
Sets the value of the provided parameters