t3_weighted_norm#

t3toolbox.tucker_tensor_train.t3_weighted_norm(x, weights, use_orthogonalization=True)#
def t3_weighted_norm(
        x:       'TuckerTensorTrain',
        weights: T3Weights,

        use_orthogonalization: bool = True,   # for numerical stability (as the backend / uniform twins)
) -> NDArray:

Weighted Hilbert-Schmidt norm ||absorb_weights(x, weights)|| (returns an array of shape stack_shape; a scalar when unstacked). use_orthogonalization passes through to the backend (the kwarg its backend and uniform twins always had – review H2-6).

Parameters:
Return type:

NDArray