t3_from_vector

t3toolbox.backend.t3_conversions.t3_from_vector(x_flat, shape, tucker_ranks, tt_ranks, stack_shape=())

def t3_from_vector(
        x_flat:       NDArray,                 # shape=(x_size,), all core entries flattened
        shape:        typ.Sequence[int],       # len=d, the tensor mode sizes (N0,...,N(d-1))
        tucker_ranks: typ.Sequence[int],       # len=d
        tt_ranks:     typ.Sequence[int],       # len=d+1
        stack_shape:  typ.Sequence[int] = (),  # leading batch axes
) -> typ.Tuple[
    typ.Sequence[NDArray],  # tucker_cores. len=d, elm_shape=stack_shape+(ni,Ni)
    typ.Sequence[NDArray],  # tt_cores.     len=d, elm_shape=stack_shape+(rLi,ni,rR(i+1))
]:

Constructs a T3 from a 1D vector containing the core entries

Parameters:

• x_flat (NDArray)

• shape (t3toolbox.backend.common.typ.Sequence[int])

• tucker_ranks (t3toolbox.backend.common.typ.Sequence[int])

• tt_ranks (t3toolbox.backend.common.typ.Sequence[int])

• stack_shape (t3toolbox.backend.common.typ.Sequence[int])

Return type:

t3toolbox.backend.common.typ.Tuple[t3toolbox.backend.common.typ.Sequence[NDArray], t3toolbox.backend.common.typ.Sequence[NDArray]]