TuckerTensorTrain.ones#

static t3toolbox.tucker_tensor_train.TuckerTensorTrain.ones(shape, stack_shape=(), use_jax=False)#

def ones(
        shape: Tuple[int, ...],
        stack_shape: Tuple[int, ...] = (),
        use_jax: bool = False,
) -> 'TuckerTensorTrain':

Construct TuckerTensorTrain representation of dense tensor filled with ones. Has Tucker and TT ranks equal to 1.

Parameters:

shape (Sequence[int]) – Shape of the TuckerTensorTrain. len(shape)=d.
stack_shape (Sequence[int], optional) – Stack shape. Default (stack_shape=()): No stacking.
use_jax (bool, optional) – Cores are jax arrays if True, and numpy arrays if False. (default: use_jax=False)

Returns:

Ones TuckerTensorTrain with the desired shape. tucker_ranks=(1,...,1) and tt_ranks=(1,...,1)

Return type:

TuckerTensorTrain

Examples

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> shape = (14, 15, 16)
>>> stack_shape = (2,3)
>>> x = t3.TuckerTensorTrain.ones(shape, stack_shape=stack_shape)
>>> print(np.linalg.norm(x.to_dense() - np.ones(stack_shape+shape)))
0.0
>>> print(x.tucker_ranks)
(1, 1, 1)
>>> print(x.tt_ranks)
(1, 1, 1, 1)