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Jul 13, 2022
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Fixing issues with np.reshape; requires order='F' to align with Matla… #39

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29 changes: 11 additions & 18 deletions pyttb/tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -335,10 +335,8 @@ def innerprod(self, other):
if isinstance(other, ttb.tensor):
if self.shape != other.shape:
assert False, 'Inner product must be between tensors of the same size'
x = np.reshape(self.data, (self.data.size,))
y = np.reshape(other.data, (other.data.size,))
#x = np.reshape(self.data, (1, self.data.size))
#y = np.reshape(other.data, (other.data.size, 1))
x = np.reshape(self.data, (self.data.size,), order='F')
y = np.reshape(other.data, (other.data.size,), order='F')
return x.dot(y)
elif isinstance(other, (ttb.ktensor, ttb.sptensor, ttb.ttensor)):
# Reverse arguments and call specializer code
Expand Down Expand Up @@ -770,20 +768,16 @@ def squeeze(self):
def symmetrize(self, grps=None, version=None):
"""
Symmetrize a tensor in the specified modes

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

Parameters
----------
grps
version

Returns
-------

"""
n = self.ndims
sz = np.array(self.shape)
Expand Down Expand Up @@ -962,7 +956,7 @@ def ttsv(self, vector, dims=None, version = None):
elif dims > 0:
assert False, "Invalid modes in ttsv"

if version is not None: # Calculate the old way
if version == 1: # Calculate the old way
P = self.ndims
X = np.array([vector for i in range(P)])
if dims == 0:
Expand All @@ -972,9 +966,7 @@ def ttsv(self, vector, dims=None, version = None):
else:
return self.ttv(X, -np.arange(1, -dims+1))

else: # Calculate the new way
if dims != 0:
assert False, "New version only support vector times all modes"
elif version == 2 or version is None: # Calculate the new way
d = self.ndims
sz = self.shape[0] # Sizes of all modes must be the same

Expand All @@ -983,17 +975,18 @@ def ttsv(self, vector, dims=None, version = None):

y = self.data
for i in range(drem, 0, -1):
yy = np.reshape(y, (sz**(dnew + i -1), sz))
yy = np.reshape(y, (sz**(dnew + i -1), sz), order='F')
y = yy.dot(vector)

# Convert to matrix if 2-way or convert back to tensor if result is >= 3-way
# TODO: currently this only support scalar return so these are ignored in coverage
if dnew == 2: # pragma: no cover
return np.reshape(y, [sz, sz])
elif dnew > 2: # pragma: no cover
return ttb.tensor.from_data(np.reshape(y, sz*np.ones(dnew)))
if dnew == 2:
return np.reshape(y, [sz, sz], order='F')
elif dnew > 2:
return ttb.tensor.from_data(np.reshape(y, sz*np.ones(dnew, dtype=np.int), order='F'))
else:
return y
else:
assert False, "Invalid value for version; should be None, 1, or 2"

def __setitem__(self, key, value):
"""
Expand Down
85 changes: 69 additions & 16 deletions tests/test_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -981,7 +981,7 @@ def test_tensor_squeeze(sample_tensor_2way):
def test_tensor_ttv(sample_tensor_2way, sample_tensor_3way, sample_tensor_4way):
(params2, tensorInstance2) = sample_tensor_2way
(params3, tensorInstance3) = sample_tensor_3way
(params3, tensorInstance4) = sample_tensor_4way
(params4, tensorInstance4) = sample_tensor_4way

# Wrong shape vector
with pytest.raises(AssertionError) as excinfo:
Expand Down Expand Up @@ -1028,31 +1028,64 @@ def test_tensor_ttv(sample_tensor_2way, sample_tensor_3way, sample_tensor_4way):
assert tensorInstance4.ttv(np.array([np.array([1, 1, 1]), np.array([1, 1, 1]), np.array([1, 1, 1]), np.array([1, 1, 1])])) == 3321

@pytest.mark.indevelopment
def test_tensor_ttsv(sample_tensor_2way):
(params, tensorInstance) = sample_tensor_2way
tensorInstance = ttb.tensor.from_data(np.ones((4, 4, 4)))
vector = np.array([1, 1, 1, 1])
def test_tensor_ttsv(sample_tensor_4way):

# 3-way
tensorInstance3 = ttb.tensor.from_data(np.ones((4, 4, 4)))
vector3 = np.array([4, 3, 2, 1])
assert tensorInstance3.ttsv(vector3, version=1) == 1000
assert (tensorInstance3.ttsv(vector3, dims=-1, version=1) == 100 * np.ones((4,))).all()
assert (tensorInstance3.ttsv(vector3, dims=-2, version=1) == 10 * np.ones((4,4))).all()

# Invalid dims
with pytest.raises(AssertionError) as excinfo:
tensorInstance.ttsv(vector, dims = 1)
tensorInstance3.ttsv(vector3, dims = 1)
assert "Invalid modes in ttsv" in str(excinfo)

# 4-way tensor
(params4, tensorInstance4) = sample_tensor_4way
T4ttsv = tensorInstance4.ttsv(np.array([1,2,3]), -3, version=1)
data4_3 = np.array([[[222, 276, 330],
[240, 294, 348],
[258, 312, 366]],
[[228, 282, 336],
[246, 300, 354],
[264, 318, 372]],
[[234, 288, 342],
[252, 306, 360],
[270, 324, 378]]])
assert (T4ttsv.data == data4_3).all()

assert tensorInstance.ttsv(vector, version=1) == 64
assert (tensorInstance.ttsv(vector, dims=-1, version=1) == np.array([16, 16, 16, 16])).all()
# 5-way dense tensor
shape = (3,3,3,3,3)
T5 = ttb.tensor.from_data(np.arange(1,np.prod(shape)+1), shape)
T5ttsv = T5.ttsv(np.array([1,2,3]), -3, version=1)
data5_3 = np.array([[[5220, 5544, 5868],
[5328, 5652, 5976],
[5436, 5760, 6084]],
[[5256, 5580, 5904],
[5364, 5688, 6012],
[5472, 5796, 6120]],
[[5292, 5616, 5940],
[5400, 5724, 6048],
[5508, 5832, 6156]]])
assert (T5ttsv.data == data5_3).all()

# Test new algorithm, version=2

tensorInstance = ttb.tensor.from_data(np.ones((4, 4, 4, 4)))
assert tensorInstance.ttsv(vector, dims=-3, version=1).isequal(tensorInstance.ttv(vector, 0))
# 3-way
assert tensorInstance3.ttsv(vector3) == 1000
assert (tensorInstance3.ttsv(vector3, dims=-1) == 100 * np.ones((4,))).all()
assert (tensorInstance3.ttsv(vector3, dims=-2) == 10 * np.ones((4,4))).all()

# Test new algorithm
# 4-way tensor
T4ttsv2 = tensorInstance4.ttsv(np.array([1,2,3]), -3)
assert (T4ttsv2.data == data4_3).all()

# Only works for all modes of equal length
# Incorrect version requested
with pytest.raises(AssertionError) as excinfo:
tensorInstance.ttsv(vector, dims=-1)
assert "New version only support vector times all modes" in str(excinfo)
tensorInstance = ttb.tensor.from_data(np.ones((4, 4, 4)))
assert tensorInstance.ttsv(vector) == 64
tensorInstance4.ttsv(np.array([1,2,3]), -3, version=3)
assert "Invalid value for version; should be None, 1, or 2" in str(excinfo)

@pytest.mark.indevelopment
def test_tensor_issymmetric(sample_tensor_2way):
Expand Down Expand Up @@ -1083,19 +1116,39 @@ def test_tensor_symmetrize(sample_tensor_2way):

# Test new default version

# 2-way
symmetricData = np.array([[[0.5, 0, 0.5, 0], [0, 0, 0, 0], [0.5, 0, 0, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 2.5, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[0.5, 0, 0, 0], [0, 0, 0, 0], [0, 0, 3.5, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]])
symmetricTensor = ttb.tensor.from_data(symmetricData)
assert symmetricTensor.symmetrize().isequal(symmetricTensor)

# 3-way
symmetricData = np.zeros((4, 4, 4))
symmetricData[1, 2, 1] = 1
symmetricTensor = ttb.tensor.from_data(symmetricData)
print(f'\nsymmetricTensor:\n{symmetricTensor}')
assert symmetricTensor.issymmetric() is False
print(f'\nsymmetricTensor.symmetrize():\n{symmetricTensor.symmetrize()}')
assert (symmetricTensor.symmetrize()).issymmetric()

# 3-way
shape = (2,2,2)
T3 = ttb.tensor.from_data(np.arange(1,np.prod(shape)+1), shape)
T3sym = T3.symmetrize()
print(f'\nT3sym:')
print(T3sym)
data3 = np.array([[[1, 3+1/3],
[3+1/3, 5+2/3]],
[[3+1/3, 5+2/3],
[5+2/3, 8 ]]])
assert (T3sym.data == data3).all()

#T3syms_2_1_3 = T3.symmetrize(grps=[[1], [0,2]])
#print(f'\nT3syms_2_1_3:')
#print(T3syms_2_1_3)

with pytest.raises(AssertionError) as excinfo:
symmetricTensor.symmetrize(grps=np.array([[0, 1], [1, 2]]))
assert "Cannot have overlapping symmetries" in str(excinfo)
Expand Down