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Tensor three-way examples #440

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bec4fbb
Changing isequal example from 2-way to 3-way
tgkolda May 28, 2025
60d72a9
Changing exp example from 2-way to 3-way
tgkolda May 28, 2025
482f288
Changing example to 3-way from 2-way
tgkolda May 28, 2025
0cdcf11
Changed example in tensor.find from 2-way to 3-way
tgkolda May 27, 2025
a27d2c3
Changing example in tensor.to_sptensor from 2-way to 3-way.
tgkolda May 27, 2025
9246c2e
Changing example in tensor.contract from 2-way to 3-way
tgkolda May 27, 2025
74366c2
Changing tensor.copy example to be 3-way rather than 2-way.
tgkolda May 27, 2025
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174 changes: 111 additions & 63 deletions pyttb/tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,13 +296,13 @@ def copy(self) -> tensor:
Observing the difference between a shallow copy and a deep copy. When the
original tensor changes, so does the shallow copy, but the deep copy does not::

>>> T = ttb.tensor(np.ones((3, 2)))
>>> T = ttb.tensor(np.ones(8), (2, 2, 2))
>>> T_shallow = T
>>> T_deep = T.copy()
>>> T[0, 0] = 3
>>> T[0, 0] == T_shallow[0, 0]
>>> T[0, 0, 0] = 3
>>> T[0, 0, 0] == T_shallow[0, 0, 0]
True
>>> T[0, 0] == T_deep[0, 0]
>>> T[0, 0, 0] == T_deep[0, 0, 0]
False
"""
return ttb.tensor(self.data, self.shape, copy=True)
Expand Down Expand Up @@ -432,30 +432,35 @@ def contract(self, i1: int, i2: int) -> Union[np.ndarray, 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) with order F
data[:, :, 0] =
[[1 3]
[5 7]]
data[:, :, 1] =
[[2 4]
[6 8]]
>>> T.contract(0, 1)
tensor of shape (2,) with order F
data[:] =
[ 8. 10.]
>>> T.contract(0, 2)
tensor of shape (2,) with order F
data[:] =
[ 7. 11.]
>>> T.contract(1, 2)
tensor of shape (2,) with order F
data[:] =
[ 5. 13.]
Contract a three-way 2 x 2 x 2 tensor along two dimensions
in three possible ways::

>>> T = ttb.tensor(np.ones(8), (2, 2, 2)) # All-ones 2 x 2 x 2 tensor
>>> T.contract(0, 1)
tensor of shape (2,) with order F
data[:] =
[2. 2.]
>>> T = ttb.tensor(np.arange(1, 9), (2, 2, 2))
>>> print(T)
tensor of shape (2, 2, 2) with order F
data[:, :, 0] =
[[1 3]
[2 4]]
data[:, :, 1] =
[[5 7]
[6 8]]
>>> T.contract(0, 1)
tensor of shape (2,) with order F
data[:] =
[ 5. 13.]
>>> T.contract(0, 2)
tensor of shape (2,) with order F
data[:] =
[ 7. 11.]
>>> T.contract(1, 2)
tensor of shape (2,) with order F
data[:] =
[ 8. 10.]
"""
if self.shape[i1] != self.shape[i2]:
assert False, "Must contract along equally sized dimensions"
Expand Down Expand Up @@ -506,10 +511,13 @@ def double(self) -> np.ndarray:

Examples
--------
>>> T = ttb.tensor(np.ones((2, 2)))
>>> T = ttb.tensor(np.ones(8), (2, 2, 2)) # All-ones 2 x 2 x 2 tensor
>>> T.double()
array([[1., 1.],
[1., 1.]])
array([[[1., 1.],
[1., 1.]],
<BLANKLINE>
[[1., 1.],
[1., 1.]]])
"""
return self.data.astype(np.float64, order=self.order, copy=True)

Expand All @@ -524,10 +532,23 @@ def exp(self) -> tensor:

Examples
--------
>>> T = ttb.tensor(np.array([[1, 2], [3, 4]]))
>>> T.exp().data # doctest: +ELLIPSIS
array([[ 2.7182..., 7.3890... ],
[20.0855..., 54.5981...]])
>>> T = ttb.tensor(np.arange(8), (2, 2, 2)) # Tensor with entries 0 to 7
>>> print(T)
tensor of shape (2, 2, 2) with order F
data[:, :, 0] =
[[0 2]
[1 3]]
data[:, :, 1] =
[[4 6]
[5 7]]
>>> print(T.exp())
tensor of shape (2, 2, 2) with order F
data[:, :, 0] =
[[ 1. 7.3890561 ]
[ 2.71828183 20.08553692]]
data[:, :, 1] =
[[ 54.59815003 403.42879349]
[ 148.4131591 1096.63315843]]
"""
return ttb.tensor(np.exp(self.data), copy=False)

Expand All @@ -542,20 +563,35 @@ def find(self) -> Tuple[np.ndarray, np.ndarray]:

Examples
--------
>>> T = ttb.tensor(np.array([[1,2],[3,4]]))
>>> print(T)
tensor of shape (2, 2) with order F
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]])
Create a random tensor with approximately 50% zero entries::

>>> np.random.seed(6) # reproducibility
>>> sprandint = lambda s: np.where(np.random.rand(np.prod(s)) < 0.5,
... 0.0, np.random.rand(np.prod(s)))
>>> T = ttb.tensor.from_function(sprandint, (2,2,2))
>>> print(T)
tensor of shape (2, 2, 2) with order F
data[:, :, 0] =
[[0.33540785 0.43814143]
[0. 0. ]]
data[:, :, 1] =
[[0. 0.6453551]
[0.5788586 0. ]]

Find the nonzero entries in the tensor::

>>> subs, vals = T.find()
>>> print(subs)
[[0 0 0]
[0 1 0]
[1 0 1]
[0 1 1]]
>>> print(vals)
[[0.33540785]
[0.43814143]
[0.5788586 ]
[0.6453551 ]]

"""
idx = np.nonzero(np.ravel(self.data, order=self.order))[0]
subs = tt_ind2sub(self.shape, idx)
Expand All @@ -571,17 +607,29 @@ def to_sptensor(self) -> ttb.sptensor:

Examples
--------
>>> T = ttb.tensor(np.array([[0, 2], [3, 0]]))
>>> print(T)
tensor of shape (2, 2) with order F
data[:, :] =
[[0 2]
[3 0]]
>>> S = T.to_sptensor()
>>> print(S)
sparse tensor of shape (2, 2) with 2 nonzeros and order F
[1, 0] = 3
[0, 1] = 2
Construct a 2x2x2 tensor with some nonzero entries::

>>> np.random.seed(3) # reproducibility
>>> sprandint = lambda s: np.random.randint(0, 4, size=np.prod(s)) / 4;
>>> T = ttb.tensor.from_function(sprandint, (2,2,2))
>>> print(T)
tensor of shape (2, 2, 2) with order F
data[:, :, 0] =
[[0.5 0.25]
[0. 0.75]]
data[:, :, 1] =
[[0. 0. ]
[0. 0.25]]

Convert to a sparse tensor::

>>> S = T.to_sptensor()
>>> print(S)
sparse tensor of shape (2, 2, 2) with 4 nonzeros and order F
[0, 0, 0] = 0.5
[0, 1, 0] = 0.25
[1, 1, 0] = 0.75
[1, 1, 1] = 0.25
"""
subs, vals = self.find()
return ttb.sptensor(subs, vals, self.shape, copy=False)
Expand Down Expand Up @@ -760,11 +808,11 @@ def isequal(self, other: Union[tensor, ttb.sptensor]) -> bool:

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