TuckerTensorTrain.ones
======================

.. py:method:: t3toolbox.tucker_tensor_train.TuckerTensorTrain.ones(shape, stack_shape = (), use_jax = False)
   :staticmethod:


   .. code-block:: python

      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.

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

   :returns: Ones TuckerTensorTrain with the desired shape. ``tucker_ranks=(1,...,1)`` and ``tt_ranks=(1,...,1)``
   :rtype: TuckerTensorTrain

   .. seealso:: :py:meth:`.TuckerTensorTrain.zeros`, :py:meth:`.TuckerTensorTrain.randn`

   .. rubric:: 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)