t3toolbox.uniform_tucker_tensor_train.ut3_add#

t3toolbox.uniform_tucker_tensor_train.ut3_add(x: UniformTuckerTensorTrain, y: UniformTuckerTensorTrain, squash: bool = True, use_jax: bool = False) → UniformTuckerTensorTrain#

Add two UniformTuckerTensorTrains, x,y -> x+y.

Parameters:

  • x_cores (UniformTuckerTensorTrainCores) – First summand cores

  • x_masks (UniformTuckerTensorTrainMasks) – First summand masks

  • y_cores (UniformTuckerTensorTrainCores) – Second summand cores

  • y_masks (UniformTuckerTensorTrainMasks) – Second summand masks

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

Returns:

  • UniformTuckerTensorTrainCores – Cores for sum, x+y

  • UniformTuckerTensorTrainMasks – Cores for sum x+y

Examples

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> import t3toolbox.uniform_tucker_tensor_train as ut3
>>> x = t3.t3_corewise_randn((14,15,16), (4,6,5), (2,3,2,2), stack_shape=(2,3))
>>> ux = ut3.t3_to_ut3(x)
>>> y = t3.t3_corewise_randn((14,15,16), (6,7,8), (3,5,6,1), stack_shape=(2,3))
>>> uy = ut3.t3_to_ut3(y)
>>> ux_plus_uy = ut3.ut3_add(ux, uy) # add x+y
>>> print(np.linalg.norm(x.to_dense() + y.to_dense() - ux_plus_uy.to_dense()))
3.250578545971108e-12