TuckerTensorTrain.from_vector#

static t3toolbox.tucker_tensor_train.TuckerTensorTrain.from_vector(x_flat, shape, tucker_ranks, tt_ranks, stack_shape=())#
def from_vector(
        x_flat:         NDArray,             # shape=(data_size,)
        shape:          Sequence[int],       # len=d
        tucker_ranks:   Sequence[int],       # len=d
        tt_ranks:       Sequence[int],       # len=d+1
        stack_shape:    Sequence[int] = (),  # () if unstacked
) -> 'TuckerTensorTrain':

Constructs a TuckerTensorTrain from a 1D vector containing the core entries.

Parameters:

  • x_flat (NDArray) – The flattened vector of core entries. x_flat.shape=(data_size,)

  • shape (Sequence[int]) – Shape of the tensor.

  • tucker_ranks (Sequence[int]) – Tucker ranks of the tensor.

  • tt_ranks (Sequence[int]) – TT ranks.

  • stack_shape (Sequence[int], optional) – Stack shape. Default (stack_shape=()): No stacking.

Return type:

TuckerTensorTrain

See also

TuckerTensorTrain.to_vector()

Returns:

T – TuckerTensorTrain constructed from the vector of all core entries. T.data_size=len(x_flat), T.shape=shape, T.tucker_ranks=tucker_ranks, T.tt_ranks=tt_ranks, T.stack_shape=stack_shape.

Return type:

TuckerTensorTrain

Parameters:

  • x_flat (NDArray)

  • shape (collections.abc.Sequence[int])

  • tucker_ranks (collections.abc.Sequence[int])

  • tt_ranks (collections.abc.Sequence[int])

  • stack_shape (collections.abc.Sequence[int])

Examples

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> import t3toolbox.corewise as cw
>>> x = t3.TuckerTensorTrain.randn((14,15,16), (4,5,6), (1,3,4,5), stack_shape=(2,3))
>>> x_flat = x.to_vector()
>>> x2 = t3.TuckerTensorTrain.from_vector(x_flat, x.shape, x.tucker_ranks, x.tt_ranks, stack_shape=x.stack_shape)
>>> print(cw.corewise_norm(cw.corewise_sub(x.data, x2.data)))
0.0