pyttb.sptenmat

class pyttb.sptenmat(subs: ndarray | None = None, vals: ndarray | None = None, rdims: ndarray | None = None, cdims: ndarray | None = None, tshape: Tuple[int, ...] = (), copy: bool = True)

Bases: object

Store sparse tensor as a sparse matrix.

Construct a pyttb.sptenmat.

Constructed from a set of 2D subscripts (subs) and values (vals) along with the mappings of the row (rdims) and column indices (cdims) and the shape of the original tensor (tshape).

If you already have an sparse tensor see :method:`pyttb.sptensor.to_sptenmat`.

Parameters:

• subs – Location of non-zero entries, in sptenmat.

• vals – Values for non-zero entries, in sptenmat.

• rdims – Mapping of row indices.

• cdims – Mapping of column indices.

• tshape – Shape of the original tensor.

• copy – Whether to make a copy of provided data or just reference it. Skips error checking when just setting reference.

Examples

Create an empty pyttb.sptenmat:

>>> S = ttb.sptenmat()
>>> S 
sptenmat corresponding to a sptensor of shape () with 0 nonzeros and order F
rdims = [  ] (modes of sptensor corresponding to rows)
cdims = [  ] (modes of sptensor corresponding to columns)

Create a pyttb.sptenmat from subscripts, values, and unwrapping

dimensions:

>>> subs = np.array([[1, 6], [1, 7]])
>>> vals = np.array([[6], [7]])
>>> tshape = (4, 4, 4)
>>> S = ttb.sptenmat(            subs,            vals,            rdims=np.array([0]),            cdims=np.array([1,2]),            tshape=tshape        )
>>> S 
sptenmat corresponding to a sptensor of shape (4, 4, 4) with 2 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [1, 6] = 6
    [1, 7] = 7

classmethod from_array(array: coo_matrix | ndarray, rdims: ndarray | None = None, cdims: ndarray | None = None, tshape: Tuple[int, ...] = ())

Construct a pyttb.sptenmat.

Constructed from a coo_matrix along with the mappings of the row (rdims) and column indices (cdims) and the shape of the original tensor (tshape).

Parameters:

• array – Representation of sparse tensor data (sparse or dense).

• rdims – Mapping of row indices.

• cdims – Mapping of column indices.

• tshape – Shape of the original tensor.

Examples

Create a pyttb.sptenmat from a sparse matrix and unwrapping

dimensions. Infer column dimensions from row dimensions specification.

>>> data = np.array([6, 7])
>>> rows = np.array([1, 1])
>>> cols = np.array([6, 7])
>>> sparse_matrix = sparse.coo_matrix((data, (rows, cols)))
>>> tshape = (4, 4, 4)
>>> S = ttb.sptenmat.from_array(            sparse_matrix,            rdims=np.array([0]),            tshape=tshape        )
>>> S 
sptenmat corresponding to a sptensor of shape (4, 4, 4) with 2 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [1, 6] = 6
    [1, 7] = 7

property order: Literal['F']

Return the data layout of the underlying storage.

copy() → sptenmat

Return a deep copy of the pyttb.sptenmat.

Examples

Create a pyttb.sptenmat (ST1) and make a deep copy. Verify the deep copy (ST3) is not just a reference (like ST2) to the original.

>>> S1 = ttb.sptensor(shape=(2, 2))
>>> S1[0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST2 = ST1
>>> ST3 = ST1.copy()
>>> ST1[0, 0] = 3
>>> ST1.to_sptensor().isequal(ST2.to_sptensor())
True
>>> ST1.to_sptensor().isequal(ST3.to_sptensor())
False

__deepcopy__(memo)

Return deepcopy of this sptenmat.

to_sptensor() → sptensor

Construct a pyttb.sptensor from :class:pyttb.sptenmat.

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> S1  
sparse tensor of shape (2, 2, 2) with 1 nonzeros and order F
[0, 0, 0] = 1.0
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = 1.0
>>> ST1.to_sptensor()  
sparse tensor of shape (2, 2, 2) with 1 nonzeros and order F
[0, 0, 0] = 1.0

property shape: Tuple[int, ...]

Return the shape of a pyttb.sptenmat.

Examples

>>> ttb.sptenmat().shape  # empty sptenmat
()
>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1.shape
(2, 4)

double() → coo_matrix

Convert a pyttb.sptenmat to a COO scipy.sparse.coo_matrix.

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> COO = ST1.double()
>>> type(COO)  
<class 'scipy.sparse._coo.coo_matrix'>
>>> COO.nnz  
1
>>> COO.toarray()  
array([[1., 0., 0., 0.],
    [0., 0., 0., 0.]])

full() → tenmat

Convert a pyttb.sptenmat to a (dense) pyttb.tenmat.

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1.full()  
matrix corresponding to a tensor of shape (2, 2, 2) with order F
rindices = [ 0 ] (modes of tensor corresponding to rows)
cindices = [ 1, 2 ] (modes of tensor corresponding to columns)
data[:, :] =
[[1. 0. 0. 0.]
 [0. 0. 0. 0.]]

property nnz: int

Number of nonzero values in the pyttb.sptenmat.

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1.nnz
1

norm() → float

Compute the norm of the pyttb.sptenmat.

Frobenius norm, or square root of the sum of squares of entries.

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1.norm()
1.0

isequal(other: sptenmat) → bool

Exact equality for pyttb.sptenmat.

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST2 = ttb.sptenmat()
>>> ST1.isequal(ST2)
False
>>> ST1.isequal(ST1)
True

__pos__()

Unary plus operator (+).

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> +ST1  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = 1.0

__neg__()

Unary minus operator (-).

Examples

>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> -ST1  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = -1.0

__setitem__(key, value)

Subscripted assignment for the pyttb.sptenmat.

Examples

Create an empty pyttb.sptenmat.

>>> ST = ttb.sptenmat(rdims=np.array([0]), tshape=(2, 2, 2))
>>> ST  
sptenmat corresponding to a sptensor of shape (2, 4) with 0 nonzeros and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)

Insert a new value into it.

>>> ST[0, 0] = 1.0
>>> ST  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = 1.0

Update an existing value in it.

>>> ST[0, 0] = 2.0
>>> ST  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = 2.0

__repr__()

Return string representation of a pyttb.sptenmat.

Examples

>>> ttb.sptenmat()  
sptenmat corresponding to a sptensor of shape () with 0 nonzeros         and order F
rdims = [  ] (modes of sptensor corresponding to rows)
cdims = [  ] (modes of sptensor corresponding to columns)
>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = 1.0

Returns:

str –

Contains the shape, row indices (rindices), column indices (cindices)

and data as strings on different lines.

__str__()

Return string representation of a pyttb.sptenmat.

Examples

>>> ttb.sptenmat()  
sptenmat corresponding to a sptensor of shape () with 0 nonzeros         and order F
rdims = [  ] (modes of sptensor corresponding to rows)
cdims = [  ] (modes of sptensor corresponding to columns)
>>> S1 = ttb.sptensor(shape=(2, 2, 2))
>>> S1[0, 0, 0] = 1
>>> ST1 = S1.to_sptenmat(np.array([0]))
>>> ST1  
sptenmat corresponding to a sptensor of shape (2, 2, 2) with 1 nonzeros         and order F
rdims = [ 0 ] (modes of sptensor corresponding to rows)
cdims = [ 1, 2 ] (modes of sptensor corresponding to columns)
    [0, 0] = 1.0

Returns:

str –

Contains the shape, row indices (rindices), column indices (cindices)

and data as strings on different lines.