t3toolbox.tucker_tensor_train.t3_zeros#

t3toolbox.tucker_tensor_train.t3_zeros(shape: t3toolbox.backend.common.typ.Tuple[int, Ellipsis], tucker_ranks: t3toolbox.backend.common.typ.Tuple[int, Ellipsis], tt_ranks: t3toolbox.backend.common.typ.Tuple[int, Ellipsis], stack_shape: t3toolbox.backend.common.typ.Tuple[int, Ellipsis] = (), use_jax: bool = False) → TuckerTensorTrain#

Construct a Tucker tensor train of zeros.

Parameters:

  • structure (T3Structure) – Tucker tensor train structure, (shape, tucker_ranks, tt_ranks)=((N0,…,N(d-1)), (n0,…,n(d-1)), (1,r1,…,r(d-1),1))).

  • xnp – Linear algebra backend. Default: np (numpy)

Returns:

Dense tensor represented by x, which has shape (N0, …, N(d-1))

Return type:

NDArray

See also

TuckerTensorTrain, T3Structure

Examples

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> shape = (14, 15, 16)
>>> tucker_ranks = (4, 5, 6)
>>> tt_ranks = (1, 3, 2, 1)
>>> vs = (2,3)
>>> z = t3.t3_zeros(shape, tucker_ranks, tt_ranks, stack_shape=vs)
>>> print(np.linalg.norm(z.to_dense()))
0.0