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