TuckerTensorTrain.concatenate
=============================

.. py:method:: t3toolbox.tucker_tensor_train.TuckerTensorTrain.concatenate(xx)
   :staticmethod:


   .. code-block:: python

      def concatenate(
              xx: Sequence['TuckerTensorTrain'],
      ) -> 'TuckerTensorTrain':


   Concatenates TuckerTensorTrain segments.

   :param xx: TuckerTensorTrain segments to be concatenated
   :type xx: Sequence[TuckerTensorTrain]

   :returns: Concatenated TuckerTensorTrain.
   :rtype: TuckerTensorTrain

   :raises ValueError: If segments have incompatible leading and trailing TT ranks.
       I.e., if ``x[ii].tt_ranks[-1] != x[ii+1].tt_ranks[0]``.

   .. seealso:: :py:meth:`.TuckerTensorTrain.segment`

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> np.random.seed(0)
   >>> randn = np.random.randn
   >>> tk = (randn(4,14), randn(5,15), randn(6,16), randn(7,17), randn(8,18), randn(9,19))
   >>> tt = (randn(2,4,3), randn(3,5,2), randn(2,6,2), randn(2,7,3), (randn(3,8,4)), (randn(4,9,1)))
   >>> x = t3.TuckerTensorTrain(tk[:3], tt[:3])
   >>> y = t3.TuckerTensorTrain(tk[3:4], tt[3:4])
   >>> z = t3.TuckerTensorTrain(tk[4:], tt[4:])
   >>> xyz = t3.TuckerTensorTrain.concatenate([x, y, z])
   >>> xyz2 = t3.TuckerTensorTrain(tk, tt)              # the same train, built in one piece
   >>> print(np.allclose(xyz.to_dense(), xyz2.to_dense()))
   True