Dense Tensor (pyttb.tensor)

Note

Classes and functions defined in tensor.py have been promoted to the pyttb namespace.

For all examples in this document, the following module imports are assumed:

>>> import pyttb as ttb
>>> import numpy as np

Class for dense tensors.

pyttb.tensor.data

Data of the tensor

Type:

numpy.ndarray

pyttb.tensor.shape

Size of the tensor

Type:

Tuple[int]

Instances of pyttb.tensor can be created using pyttb.tensor.tensor.__init__() or the following methods:

  • from_function() - Create a tensor from a function

  • copy() - Make a deep copy of a tensor

  • tenones() - Create an all ones tensor of a specified size

  • tenzeros() - Create an all zeros tensor of a specified size

  • tenrand() - Create a random tensor of a specified size

  • tendiag() - Create a tensor with a specified diagonal

  • teneye() - Create an identity tensor

  • pyttb.sptensor.to_tensor() - Convert a sparse tensor to a dense tensor

  • pyttb.ktensor.to_tensor() - Convert a Kruskal tensor to a dense tensor

  • pyttb.ttensor.to_tensor() - Convert a Tucker tensor to a dense tensor

  • pyttb.tenmat.to_tensor() - Convert a tenmat to a dense tensor

See Tensors for getting started with the tensor class.

pyttb.tensor.__delattr__(self, name, /)

Implement delattr(self, name).

pyttb.tensor.__dir__(self, /)

Default dir() implementation.

pyttb.tensor.__format__(self, format_spec, /)

Default object formatter.

pyttb.tensor.__getattribute__(self, name, /)

Return getattr(self, name).

pyttb.tensor.__init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

pyttb.tensor.__new__(*args, **kwargs)

Create and return a new object. See help(type) for accurate signature.

pyttb.tensor.__reduce__(self, /)

Helper for pickle.

pyttb.tensor.__reduce_ex__(self, protocol, /)

Helper for pickle.

pyttb.tensor.__setattr__(self, name, value, /)

Implement setattr(self, name, value).

pyttb.tensor.__sizeof__(self, /)

Size of object in memory, in bytes.

pyttb.tensor.__subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

pyttb.tenones(shape: int | Iterable[int], order: Literal['F', 'C'] = 'F') → tensor[source]

Create a tensor of all ones.

Parameters:

  • shape – Shape of resulting tensor.

  • order – Memory layout for resulting tensor.

Returns:

Constructed tensor.

Examples

>>> T = ttb.tenones((3,))
>>> T
tensor of shape (3,) with order F
data[:] =
[1. 1. 1.]
>>> T = ttb.tenones((3, 3))
>>> T
tensor of shape (3, 3) with order F
data[:, :] =
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
pyttb.tenzeros(shape: int | Iterable[int], order: Literal['F', 'C'] = 'F') → tensor[source]

Create a tensor of all zeros.

Parameters:

  • shape – Shape of resulting tensor.

  • order – Memory layout for resulting tensor.

Returns:

Constructed tensor.

Examples

>>> T = ttb.tenzeros((3,))
>>> T
tensor of shape (3,) with order F
data[:] =
[0. 0. 0.]
>>> T = ttb.tenzeros((3, 3))
>>> T
tensor of shape (3, 3) with order F
data[:, :] =
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]
pyttb.tenrand(shape: int | Iterable[int], order: Literal['F', 'C'] = 'F') → tensor[source]

Create a tensor with entries drawn from a uniform distribution on [0, 1].

Parameters:

  • shape – Shape of resulting tensor.

  • order – Memory layout for resulting tensor.

Returns:

Constructed tensor.

Examples

>>> np.random.seed(1)
>>> T = ttb.tenrand((3,))
>>> T  
tensor of shape (3,) with order F
data[:] =
[4.170...e-01 7.203...e-01 1.143...e-04]
pyttb.tendiag(elements: OneDArray, shape: Shape | None = None, order: MemoryLayout = 'F') → tensor[source]

Create a tensor with elements along super diagonal.

If provided shape is too small the tensor will be enlarged to accommodate.

Parameters:

  • elements – Elements to set along the diagonal.

  • shape – Shape of resulting tensor.

  • order – Memory layout for resulting tensor.

Returns:

Constructed tensor.

Examples

>>> shape = (3,)
>>> values = np.ones(shape)
>>> T1 = ttb.tendiag(values)
>>> T2 = ttb.tendiag(values, (3, 3, 3))
>>> T1.isequal(T2)
True
pyttb.teneye(ndims: int, size: int, order: Literal['F', 'C'] = 'F') → tensor[source]

Create identity tensor of specified shape.

T is an “identity tensor if T.ttsv(x, skip_dim=0) = x for all x such that norm(x) == 1.

An identity tensor only exists if order is even. This method is resource intensive for even moderate orders or sizes (>=6).

Parameters:

  • ndims (Number of dimensions of tensor.)

  • size (Number of elements in any dimension of the tensor.)

  • order – Memory layout for resulting tensor.

Examples

>>> ttb.teneye(2, 3)
tensor of shape (3, 3) with order F
data[:, :] =
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
>>> x = np.ones((5,))
>>> x /= np.linalg.norm(x)
>>> T = ttb.teneye(4, 5)
>>> np.allclose(T.ttsv(x, 0), x)
True
Returns:

Identity tensor.