#window_length=200
#overlap_points=185
#kt=2
#kf=2
#perf=69.04%
tol = np.power(10, -6, dtype=float)
maximum_iterations = 10
C_values = np.power(10, np.linspace(-2, 9, 6), dtype=float)
#C_values=np.linspace(0.001,10000,20)
G_values = np.power(10, np.linspace(-4, -1, 4), dtype=float)
Hyper_values = np.array([np.power(10, -8, dtype=float)]) #10^(-2)
C_svm, G_svm, hyperparam, matrixscore = Methods.Cross_valHALs(
Tensor_train, y_train, C_values, G_values, Hyper_values, nbclasses, kf,
kt, maximum_iterations, tol)
#We perform the decomposition of the training tensor
#The decomposition yields:
#The error related to each updating error_list;
#The temporal and spectral dictionary components A_f and A_t;
#The number of iterations and the activation coefficients G;
#We dimension purpose, we can reduce the size of the tensor to be decomposed.This can be done in the following way:
#Tensor_train=dtensor(Preprocessing.rescaling(Tensor_train,If,It)) where If and It correspond to the desired sizes.
G, A_f, A_t, error_list = Methods.PenalizedTuckerHals(
Tensor_train, y_train, nbclasses, kf, kt, hyperparam,
maximum_iterations, tol)
#
#We define the training features. They are obtained by vectorizing the matrices G[k,:,:]
#Training_features=Methods.Training_features_extraction(G)
