pyttb.tensor

class pyttb.tensor(data: ndarray | None = None, shape: Tuple[int, ...] | None = None, copy: bool = True)

Bases: object

TENSOR Class for dense tensors.

Contains the following data members:

data: numpy.ndarray dense array containing the data elements of the tensor.

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

• from_function()

Examples

For all examples listed below, the following module imports are assumed:

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

Creates a pyttb.tensor from a numpy.ndarray

Note that 1D tensors (i.e., when len(shape)==1) contains a data array that follow the Numpy convention of being a row vector.

Parameters:

• data – Tensor source data.

• shape – Shape of resulting tensor if not the same as data shape.

• copy – Whether to make a copy of provided data or just reference it.

Examples

Create an empty pyttb.tensor:

>>> T = ttb.tensor()
>>> print(T)
empty tensor of shape ()
data = []

Create a pyttb.tensor from a numpy.ndarray:

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> print(T)
tensor of shape (2, 2)
data[:, :] =
[[1 2]
 [3 4]]

classmethod from_function(function_handle: Callable[[Tuple[int, ...]], ndarray], shape: Tuple[int, ...]) → tensor

Construct a pyttb.tensor whose data entries are set using a function.

Parameters:

• function_handle – A function that can accept a shape (i.e., tuple of dimension sizes) and return a numpy.ndarray of that shape. numpy.zeros, numpy.ones.

• shape – Shape of the resulting tensor.

Returns:

Constructed tensor.

Examples

Create a pyttb.tensor with entries equal to 1:

>>> T = ttb.tensor.from_function(np.ones, (2, 3, 4))
>>> print(T)
tensor of shape (2, 3, 4)
data[0, :, :] =
[[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]
data[1, :, :] =
[[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]

copy() → tensor

Make a deep copy of a pyttb.tensor.

Returns:

Copy of original tensor.

Examples

>>> T1 = ttb.tensor(np.ones((3, 2)))
>>> T2 = T1
>>> T3 = T2.copy()
>>> T1[0, 0] = 3
>>> T1[0, 0] == T2[0, 0]
True
>>> T1[0, 0] == T3[0, 0]
False

collapse(dims: ndarray | None = None, fun: Callable[[ndarray], float | ndarray] = np.sum) → float | ndarray | tensor

Collapse tensor along specified dimensions.

Parameters:

• dims – Dimensions to collapse.

• fun – Method used to collapse dimensions.

Returns:

Collapsed value.

Examples

>>> T = ttb.tensor(np.ones((2, 2)))
>>> T.collapse()
4.0
>>> T.collapse(np.array([0]))
tensor of shape (2,)
data[:] =
[2. 2.]
>>> T.collapse(np.arange(T.ndims), sum)
4.0
>>> T.collapse(np.arange(T.ndims), np.prod)
1.0

contract(i1: int, i2: int) → ndarray | tensor

Contract tensor along two dimensions (array trace).

Parameters:

• i1 – First dimension

• i2 – Second dimension

Returns:

Contracted tensor.

Examples

>>> T = ttb.tensor(np.ones((2, 2)))
>>> T.contract(0, 1)
2.0
>>> T = ttb.tensor(np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]))
>>> print(T)
tensor of shape (2, 2, 2)
data[0, :, :] =
[[1 2]
 [3 4]]
data[1, :, :] =
[[5 6]
 [7 8]]
>>> T.contract(0, 1)
tensor of shape (2,)
data[:] =
[ 8. 10.]
>>> T.contract(0, 2)
tensor of shape (2,)
data[:] =
[ 7. 11.]
>>> T.contract(1, 2)
tensor of shape (2,)
data[:] =
[ 5. 13.]

double() → ndarray

Convert :class:pyttb.tensor to an :class:numpy.ndarray of doubles.

Returns:

Copy of tensor data.

Examples

>>> T = ttb.tensor(np.ones((2, 2)))
>>> T.double()
array([[1., 1.],
       [1., 1.]])

exp() → tensor

Exponential of the elements of tensor.

Returns:

Copy of tensor data with the exponential function applied to data element-wise.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T.exp().data  
array([[ 2.7182...,  7.3890... ],
       [20.0855..., 54.5981...]])

find() → Tuple[ndarray, ndarray]

Find subscripts of nonzero elements in a tensor.

Returns:

Array of subscripts of the nonzero values in the tensor and a column vector of the corresponding values.

Examples

>>> T = ttb.tensor(np.array([[1,2],[3,4]]))
>>> print(T)
tensor of shape (2, 2)
data[:, :] =
[[1 2]
 [3 4]]
>>> T_threshold = T > 2
>>> subs, vals = T_threshold.find()
>>> subs.astype(int)
array([[1, 0],
       [1, 1]])
>>> vals
array([[ True],
       [ True]])

to_sptensor() → sptensor

Contruct a pyttb.sptensor from :class:pyttb.tensor

Returns:

Generated Sparse Tensor

Examples

>>> T = ttb.tensor(np.array([[0, 2], [3, 0]]))
>>> print(T)
tensor of shape (2, 2)
data[:, :] =
[[0 2]
 [3 0]]
>>> S = T.to_sptensor()
>>> print(S)
sparse tensor of shape (2, 2) with 2 nonzeros
[1, 0] = 3
[0, 1] = 2

full() → tensor

Convert dense tensor to dense tensor.

Returns:

Deep copy

to_tenmat(rdims: ndarray | None = None, cdims: ndarray | None = None, cdims_cyclic: Literal['fc'] | Literal['bc'] | Literal['t'] | None = None, copy: bool = True) → tenmat

Construct a pyttb.tenmat from a pyttb.tensor and unwrapping details.

Parameters:

• rdims – Mapping of row indices.

• cdims – Mapping of column indices.

• cdims_cyclic –

  When only rdims is specified maps a single rdim to the rows and

  the remaining dimensons span the columns. _fc_ (forward cyclic) in the order range(rdims,self.ndims()) followed by range(0, rdims). _bc_ (backward cyclic) range(rdims-1, -1, -1) then range(self.ndims(), rdims, -1).

• copy – Whether to make a copy of provided data or just reference it.

Notes

Forward cyclic is defined by Kiers [1] and backward cyclic is defined by

De Lathauwer, De Moor, and Vandewalle [2].

References

[1]

KIERS, H. A. L. 2000. Towards a standardized notation and terminology in multiway analysis. J. Chemometrics 14, 105-122.

[2]

DE LATHAUWER, L., DE MOOR, B., AND VANDEWALLE, J. 2000b. On the best rank-1 and rank-(R1, R2, … , RN ) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21, 4, 1324-1342.

Examples

Create a pyttb.tensor.

>>> tshape = (2, 2, 2)
>>> data = np.reshape(np.arange(np.prod(tshape)), tshape)
>>> T = ttb.tensor(data)
>>> T  
tensor of shape (2, 2, 2)
data[0, :, :] =
[[0 1]
 [2 3]]
data[1, :, :] =
[[4 5]
 [6 7]]

Convert to a pyttb.tenmat unwrapping around the first dimension.

Either allow for implicit column or explicit column dimension specification.

>>> TM1 = T.to_tenmat(rdims=np.array([0]))
>>> TM2 = T.to_tenmat(rdims=np.array([0]), cdims=np.array([1, 2]))
>>> TM1.isequal(TM2)
True

Convert using cyclic column ordering. For the three mode case _fc_ is the same

result.

>>> TM3 = T.to_tenmat(rdims=np.array([0]), cdims_cyclic="fc")
>>> TM3  
matrix corresponding to a tensor of shape (2, 2, 2)
rindices = [ 0 ] (modes of tensor corresponding to rows)
cindices = [ 1, 2 ] (modes of tensor corresponding to columns)
data[:, :] =
[[0 2 1 3]
 [4 6 5 7]]

Backwards cyclic reverses the order.

>>> TM4 = T.to_tenmat(rdims=np.array([0]), cdims_cyclic="bc")
>>> TM4  
matrix corresponding to a tensor of shape (2, 2, 2)
rindices = [ 0 ] (modes of tensor corresponding to rows)
cindices = [ 2, 1 ] (modes of tensor corresponding to columns)
data[:, :] =
[[0 1 2 3]
 [4 5 6 7]]

innerprod(other: tensor | sptensor | ktensor | ttensor) → float

Efficient inner product between a tensor and other pyttb tensors (tensor, sptensor, ktensor, or ttensor).

Parameters:

other – Tensor to take an innerproduct with.

Examples

>>> T = ttb.tensor(np.array([[1.0, 0.0], [0.0, 4.0]]))
>>> T.innerprod(T)
17.0
>>> S = T.to_sptensor()
>>> T.innerprod(S)
17.0

isequal(other: tensor | sptensor) → bool

Exact equality for tensors.

Parameters:

other – Tensor to compare against.

Examples

>>> T1 = ttb.tensor(2 * np.ones((2, 2)))
>>> T2 = 2 * ttb.tensor(np.ones((2, 2)))
>>> T1.isequal(T2)
True
>>> T2[0, 0] = 1
>>> T1.isequal(T2)
False

issymmetric(grps: ndarray | None, version: Any | None, return_details: Literal[False]) → bool

issymmetric(grps: ndarray | None, version: Any | None, return_details: Literal[True]) → Tuple[bool, ndarray, ndarray]

Determine if a dense tensor is symmetric in specified modes.

Parameters:

• grps – Modes to check for symmetry

• version – Any non-None value will call the non-default old version

• return_details – Flag to return symmetry details in addition to bool

Returns:

If symmetric in modes, optionally all differences and permutations

Examples

>>> T = ttb.tensor(np.ones((2,2)))
>>> T.issymmetric()
True
>>> T.issymmetric(grps=np.arange(T.ndims))
True
>>> is_sym, diffs, perms =             T.issymmetric(grps=np.arange(T.ndims), version=1, return_details=True)
>>> print(f"Tensor is symmetric: {is_sym}")
Tensor is symmetric: True
>>> print(f"Differences in modes: {diffs}")
Differences in modes: [[0.]
 [0.]]
>>> print(f"Permutations: {perms}")
Permutations: [[0. 1.]
 [1. 0.]]

logical_and(other: float | tensor) → tensor

Logical and for tensors.

Parameters:

other – Value to perform and against.

Examples

>>> T = ttb.tenones((2, 2))
>>> T.logical_and(T).collapse()  # All true
4.0

logical_not() → tensor

Logical not for tensors.

Examples

>>> T = ttb.tenones((2, 2))
>>> T.logical_not().collapse()  # All false
0.0

logical_or(other: float | tensor) → tensor

Logical or for tensors.

Parameters:

other – Value to perform or against.

Examples

>>> T = ttb.tenones((2, 2))
>>> T.logical_or(T.logical_not()).collapse()  # All true
4.0

logical_xor(other: float | tensor) → tensor

Logical xor for tensors.

Parameters:

other – Value to perform xor against.

Examples

>>> T = ttb.tenones((2, 2))
>>> T.logical_xor(T.logical_not()).collapse()  # All true
4.0

mask(W: tensor) → ndarray

Extract non-zero values at locations specified by mask tensor W.

Parameters:

W – Mask tensor.

Returns:

Array of extracted values.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> W = ttb.tenones((2, 2))
>>> T.mask(W)
array([1, 3, 2, 4])

mttkrp(U: ktensor | List[ndarray], n: int) → ndarray

Matricized tensor times Khatri-Rao product. The matrices used in the Khatri-Rao product are passed as a pyttb.ktensor (where the factor matrices are used) or as a list of numpy.ndarray objects.

Parameters:

• U – Matrices to create the Khatri-Rao product.

• n – Mode used to matricize tensor.

Returns:

Array containing matrix product.

Examples

>>> T = ttb.tenones((2, 2, 2))
>>> U = [np.ones((2, 2))] * 3
>>> T.mttkrp(U, 2)
array([[4., 4.],
       [4., 4.]])

mttkrps(U: ktensor | List[ndarray]) → List[ndarray]

Sequence of MTTKRP calculations for a tensor.

Result is equivalent to [T.mttkrp(U, k) for k in range(T.ndims)].

Parameters:

U – Matrices to create the Khatri-Rao product.

Returns:

Array containing matrix product.

Examples

>>> T = ttb.tenones((2, 2, 2))
>>> U = [np.ones((2, 2))] * 3
>>> T.mttkrps(U)
[array([[4., 4.],
       [4., 4.]]), array([[4., 4.],
       [4., 4.]]), array([[4., 4.],
       [4., 4.]])]

property ndims: int

Number of dimensions of the tensor.

Examples

>>> T = ttb.tenones((2, 2))
>>> T.ndims
2

property nnz: int

Number of non-zero elements in the tensor.

Examples

>>> T = ttb.tenones((2, 2, 2))
>>> T.nnz
8

norm() → float

Frobenius norm of the tensor, defined as the square root of the sum of the squares of the elements of the tensor.

Examples

>>> T = ttb.tenones((2, 2, 2, 2))
>>> T.norm()
4.0

nvecs(n: int, r: int, flipsign: bool = True) → ndarray

Compute the leading mode-n vectors of the tensor.

Computes the r leading eigenvectors of Tn*Tn.T (where Tn is the mode-n matricization/unfolding of self), which provides information about the mode-n fibers. In two-dimensions, the r leading mode-1 vectors are the same as the r left singular vectors and the r leading mode-2 vectors are the same as the r right singular vectors. By default, this method computes the top r eigenvectors of Tn*Tn.T.

Parameters:

• n – Mode for tensor matricization.

• r – Number of eigenvectors to compute and use.

• flipsign – If True, make each column’s largest element positive.

Returns:

Computed eigenvectors.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T.nvecs(0, 1)  
array([[0.4045...],
       [0.9145...]])
>>> T.nvecs(0, 2)  
array([[ 0.4045...,  0.9145...],
       [ 0.9145..., -0.4045...]])

permute(order: ndarray) → tensor

Permute tensor dimensions. The result is a tensor that has the same values, but the order of the subscripts needed to access any particular element are rearranged as specified by order.

Parameters:

order – New order of tensor dimensions.

Returns:

New tensor with permuted dimensions.

Examples

>>> T1 = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T1
tensor of shape (2, 2)
data[:, :] =
[[1 2]
 [3 4]]
>>> T1.permute(np.array((1, 0)))
tensor of shape (2, 2)
data[:, :] =
[[1 3]
 [2 4]]

reshape(shape: Tuple[int, ...]) → tensor

Reshape the tensor.

Parameters:

shape – New shape

Examples

>>> T1 = ttb.tenones((2, 2))
>>> T1.shape
(2, 2)
>>> T2 = T1.reshape((4, 1))
>>> T2.shape
(4, 1)

scale(factor: ndarray | tensor, dims: float | ndarray) → tensor

Scale along specified dimensions for tensors.

Parameters:

• factor (Scaling factor)

• dims (Dimensions to scale)

Returns:

Scaled Tensor.

Examples

>>> T = ttb.tenones((3, 4, 5))
>>> S = np.arange(5)
>>> Y = T.scale(S, 2)
>>> Y.data[0, 0, :]
array([0., 1., 2., 3., 4.])
>>> S = ttb.tensor(np.arange(5))
>>> Y = T.scale(S, 2)
>>> Y.data[0, 0, :]
array([0., 1., 2., 3., 4.])
>>> S = ttb.tensor(np.arange(12), shape=(3, 4))
>>> Y = T.scale(S, [0, 1])
>>> Y.data[:, :, 0]
array([[ 0.,  3.,  6.,  9.],
       [ 1.,  4.,  7., 10.],
       [ 2.,  5.,  8., 11.]])

squeeze() → tensor | float

Removes singleton dimensions from the tensor.

Returns:

Tensor or scalar if all dims squeezed.

Examples

>>> T = ttb.tensor(np.array([[[4]]]))
>>> T.squeeze()
4
>>> T = ttb.tensor(np.array([[1, 2, 3]]))
>>> T.squeeze().data
array([1, 2, 3])

symmetrize(grps: ndarray | None = None, version: Any | None = None) → tensor

Symmetrize a tensor in the specified modes.

It is the same or less work to just call T = T.symmetrize() then to first check if T is symmetric and then symmetrize it, even if T is already symmetric.

Parameters:

• grps – Modes to check for symmetry.

• version – Any non-None value will call the non-default old version.

Returns:

Symmetrized tensor.

Examples

>>> T = ttb.tenones((2, 2, 2))
>>> T.symmetrize(np.array([0, 2]))
tensor of shape (2, 2, 2)
data[0, :, :] =
[[1. 1.]
 [1. 1.]]
data[1, :, :] =
[[1. 1.]
 [1. 1.]]

ttm(matrix: ndarray | List[ndarray], dims: float | ndarray | None = None, exclude_dims: int | ndarray | None = None, transpose: bool = False) → tensor

Tensor times matrix.

Computes the n-mode product of self with the matrix matrix; i.e., self x_n matrix. The integer n specifies the dimension (or mode) along which the matrix should be multiplied. If matrix.shape = (J,I), then the tensor must have self.shape[n] = I. The result will be the same order and shape as self except that the size of dimension n will be J.

Multiplication with more than one matrix is provided using a list of matrices and corresponding dimensions in the tensor to use. Multiplication using the transpose of the matrix (or matrices) is also provided.

The dimensions of the tensor with which to multiply can be provided as dims, or the dimensions to exclude from [0, …, self.ndims] can be specified using exclude_dims.

Parameters:

• matrix – Matrix or matrices to multiple by.

• dims – Dimensions to multiply against.

• exclude_dims – Use all dimensions but these.

• transpose – Transpose matrices during multiplication.

Returns:

Tensor product.

Examples

>>> T = ttb.tenones((2, 2, 2, 2))
>>> A = 2 * np.ones((2, 1))
>>> T.ttm([A, A], dims=[0, 1], transpose=True)
tensor of shape (1, 1, 2, 2)
data[0, 0, :, :] =
[[16. 16.]
 [16. 16.]]
>>> T.ttm([A, A], exclude_dims=[0, 1], transpose=True)
tensor of shape (2, 2, 1, 1)
data[0, 0, :, :] =
[[16.]]
data[1, 0, :, :] =
[[16.]]
data[0, 1, :, :] =
[[16.]]
data[1, 1, :, :] =
[[16.]]

ttt(other: tensor, selfdims: int | ndarray | None = None, otherdims: int | ndarray | None = None) → tensor

Tensor multiplication (tensor times tensor).

Computes the contracted product of tensors, self and other, in the dimensions specified by the selfdims and otherdims. The sizes of the dimensions specified by selfdims and otherdims must match; that is, self.shape(selfdims) must equal other.shape(otherdims). If only selfdims is provided as input, it is used to specify the dimensions for both self and other.

Parameters:

• other – Tensor to multiply by.

• selfdims – Dimensions to contract self by for multiplication.

• otherdims – Dimensions to contract other tensor by for multiplication.

Returns:

Tensor product.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T.ttt(T)
tensor of shape (2, 2, 2, 2)
data[0, 0, :, :] =
[[1 2]
 [3 4]]
data[1, 0, :, :] =
[[ 3  6]
 [ 9 12]]
data[0, 1, :, :] =
[[2 4]
 [6 8]]
data[1, 1, :, :] =
[[ 4  8]
 [12 16]]
>>> T.ttt(T, 0)
tensor of shape (2, 2)
data[:, :] =
[[10 14]
 [14 20]]
>>> T.ttt(T, selfdims=0, otherdims=1)
tensor of shape (2, 2)
data[:, :] =
[[ 7 15]
 [10 22]]

ttv(vector: ndarray | List[ndarray], dims: int | ndarray | None = None, exclude_dims: int | ndarray | None = None) → float | tensor

Tensor times vector.

Computes the n-mode product of self with the vector vector; i.e., self x_n vector. The integer n specifies the dimension (or mode) along which the vector should be multiplied. If vector.shape = (I,), then the tensor must have self.shape[n] = I. The result will be the same order and shape as self except that the size of dimension n will be J. The resulting tensor has one less dimension, as dimension n is removed in the multiplication.

Multiplication with more than one vector is provided using a list of vectors and corresponding dimensions in the tensor to use.

The dimensions of the tensor with which to multiply can be provided as dims, or the dimensions to exclude from [0, …, self.ndims] can be specified using exclude_dims.

Parameters:

• vector – Vector or vectors to multiple by.

• dims – Dimensions to multiply against.

• exclude_dims – Use all dimensions but these.

Returns:

Tensor product.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T.ttv(np.ones(2), 0)
tensor of shape (2,)
data[:] =
[4. 6.]
>>> T.ttv(np.ones(2), 1)
tensor of shape (2,)
data[:] =
[3. 7.]
>>> T.ttv([np.ones(2), np.ones(2)])
10.0

ttsv(vector: ndarray, skip_dim: int | None = None, version: int | None = None) → float | ndarray | tensor

Tensor times same vector in multiple modes.

See ttv() for details on multiplication of a tensor with a vector. When skip_dim is provided, multiply the vector by all but dimensions except [0, …, skip_dim].

Parameters:

• vector – Vector to multiply by.

• skip_dim – Initial dimensions of the tensor to skip when multiplying.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T.ttsv(np.ones(2))
10.0
>>> T.ttsv(np.ones(2), 0)
array([3., 7.])
>>> T.ttsv(np.ones(2), 1)
array([[1, 2],
       [3, 4]])

__setitem__(key, value)

Subscripted assignment for a tensor.

We can assign elements to a tensor in three ways.

Case 1: T[R1,R2,…,Rn] = Y, in which case we replace the rectangular subtensor (or single element) specified by the ranges R1,…,`Rn` with Y. The right-hand-side can be a scalar, a tensor, or a numpy.ndarray.

Case 2a: T[S] = V, where S is a p x n array of subscripts and V is a scalar or a vector containing p values.

Case 2b: T[I] = V, where I is a set of p linear indices and V is a scalar or a vector containing p values. Resizing is not allowed in this case.

Examples

>>> T = tenones((3, 4, 2))
>>> # replaces subtensor
>>> T[0:2, 0:2, 0] = np.ones((2, 2))
>>> # replaces two elements
>>> T[np.array([[1, 1, 1], [1, 1, 2]])] = [5, 7]
>>> # replaces two elements with linear indices
>>> T[np.array([1, 13])] = [5, 7]
>>> # grows tensor to accept new element
>>> T[1, 1, 2:3] = 1
>>> T[1, 1, 4] = 1

__getitem__(item)

Subscripted reference for tensors.

We can extract elements or subtensors from a tensor in the following ways.

Case 1a: y = T[I1,I2,…,In], where each I is an index, returns a scalar.

Case 1b: Y = T[R1,R2,…,Rn], where one or more R is a range and the rest are indices, returns a tensor.

Case 2a: V = T[S] where S is a p x n array of subscripts, returns a vector of p values.

Case 2b: V = T[I] where I is a set of p linear indices, returns a vector of p values.

Any ambiguity results in executing the first valid case. This is particularly an issue if self.ndims == 1.

Examples

>>> T = tenones((3, 4, 2, 1))
>>> T[0, 0, 0, 0]  # produces a scalar
1.0
>>> # produces a tensor of order 1 and size 1
>>> T[1, 1, 1, :]  
tensor of shape (1,)
data[:] =
[1.]
>>> # produces a tensor of size 2 x 2 x 1
>>> T[0:2, [2, 3], 1, :]  
tensor of shape (2, 2, 1)
data[0, :, :] =
[[1.]
 [1.]]
data[1, :, :] =
[[1.]
 [1.]]
>>> # returns a vector of length 2
>>> # Equivalent to selecting [0,0,0,0] and [1,1,1,0] separately
>>> T[np.array([[0, 0, 0, 0], [1, 1, 1, 0]])]
array([1., 1.])
>>> T[[0, 1, 2]]  # extracts the first three linearized indices
array([1., 1., 1.])

__eq__(other)

Equal for tensors (element-wise).

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor of bool.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T == T
tensor of shape (2, 2)
data[:, :] =
[[ True  True]
 [ True  True]]
>>> T == 1
tensor of shape (2, 2)
data[:, :] =
[[ True False]
 [False False]]

__ne__(other)

Not equal (!=) for tensors (element-wise).

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor of bool.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T != T
tensor of shape (2, 2)
data[:, :] =
[[False False]
 [False False]]
>>> T != 1
tensor of shape (2, 2)
data[:, :] =
[[False  True]
 [ True  True]]

__ge__(other)

Greater than or equal (>=) for tensors (element-wise).

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor of bool.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T >= T
tensor of shape (2, 2)
data[:, :] =
[[ True  True]
 [ True  True]]
>>> T >= 1
tensor of shape (2, 2)
data[:, :] =
[[ True  True]
 [ True  True]]

__le__(other)

Less than or equal (<=) for tensors (element-wise).

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor of bool.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T <= T
tensor of shape (2, 2)
data[:, :] =
[[ True  True]
 [ True  True]]
>>> T <= 1
tensor of shape (2, 2)
data[:, :] =
[[ True False]
 [False False]]

__gt__(other)

Greater than (>) for tensors (element-wise).

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor of bool.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T > T
tensor of shape (2, 2)
data[:, :] =
[[False False]
 [False False]]
>>> T > 1
tensor of shape (2, 2)
data[:, :] =
[[False  True]
 [ True  True]]

__lt__(other)

Less than (<) for tensors (element-wise).

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor of bool.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T < T
tensor of shape (2, 2)
data[:, :] =
[[False False]
 [False False]]
>>> T < 1
tensor of shape (2, 2)
data[:, :] =
[[False False]
 [False False]]

__sub__(other)

Binary subtraction (-) for tensors.

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T - T
tensor of shape (2, 2)
data[:, :] =
[[0 0]
 [0 0]]
>>> T - 1
tensor of shape (2, 2)
data[:, :] =
[[0 1]
 [2 3]]

__add__(other)

Binary addition (+) for tensors.

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T + T
tensor of shape (2, 2)
data[:, :] =
[[2 4]
 [6 8]]
>>> T + 1
tensor of shape (2, 2)
data[:, :] =
[[2 3]
 [4 5]]

__radd__(other)

Right binary addition (+) for tensors

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> 1 + T
tensor of shape (2, 2)
data[:, :] =
[[2 3]
 [4 5]]

__pow__(power)

Element-wise Power (**) for tensors.

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T**2
tensor of shape (2, 2)
data[:, :] =
[[ 1  4]
 [ 9 16]]

__mul__(other)

Element-wise multiplication (*) for tensors, self*other

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T * T
tensor of shape (2, 2)
data[:, :] =
[[ 1  4]
 [ 9 16]]
>>> T * 2
tensor of shape (2, 2)
data[:, :] =
[[2 4]
 [6 8]]

__rmul__(other)

Element wise right multiplication (*) for tensors, other*self

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> 2 * T
tensor of shape (2, 2)
data[:, :] =
[[2 4]
 [6 8]]

__truediv__(other)

Element-wise left division (/) for tensors, self/other

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T / T
tensor of shape (2, 2)
data[:, :] =
[[1. 1.]
 [1. 1.]]
>>> T / 2
tensor of shape (2, 2)
data[:, :] =
[[0.5 1. ]
 [1.5 2. ]]

__rtruediv__(other)

Element wise right division (/) for tensors, other/self

Parameters:

other (pyttb.tensor, float, int)

Returns:

pyttb.tensor

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> np.set_printoptions(precision=8)
>>> 2 / T  
tensor of shape (2, 2)
data[:, :] =
[[2.         1.        ]
 [0.66666... 0.5       ]]

__pos__()

Unary plus (+) for tensors.

Returns:

Copy of tensor.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> +T
tensor of shape (2, 2)
data[:, :] =
[[1 2]
 [3 4]]

__neg__()

Unary minus (-) for tensors.

Returns:

Copy of negated tensor.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> -T
tensor of shape (2, 2)
data[:, :] =
[[-1 -2]
 [-3 -4]]

__repr__()

String representation of the tensor.

Returns:

String displaying shape and data as strings on different lines.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T
tensor of shape (2, 2)
data[:, :] =
[[1 2]
 [3 4]]

__str__()

String representation of the tensor.

Returns:

String displaying shape and data as strings on different lines.

Examples

>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T
tensor of shape (2, 2)
data[:, :] =
[[1 2]
 [3 4]]

__hash__ = None