def example():
shapeA = (300, 400, 50)
shapeB = (300, 600)
shapeC = (400, 600, 50)
a = Tensor(labels=['a3', 'b4', 'c5'], data=np.ones(shapeA))
b = Tensor(labels=['a3', 'd6'], data=np.ones(shapeB))
c = Tensor(labels=['e4', 'd6', 'c5'], data=np.ones(shapeC))
# create tensors with labels
makeLink('a3', 'a3', a, b)
makeLink('c5', 'c5', a, c)
makeLink('d6', 'd6', b, c)
# make links via labels
# note that labels can also be made between the "Leg" objects to avoid reused leg names, but here for simplicity from leg names
# now we have a tensor network, we can generate the optimal sequence of this tensor list
optimalSeq = generateOptimalSequence([a, b, c])
print('optimal contraction sequence = {}'.format(optimalSeq))
# if we do not have any knowledge in prior, we can contract the tensor list like
res = contractTensorList([a, b, c])
print(res)
# if you already have a good sequence to use
res, cost = contractAndCostWithSequence([a, b, c], seq=optimalSeq)
print(res)
print('contraction cost = {}'.format(cost))
# if you want to save time / space by contract in place(note that after this you cannot contract them again, since their bonds between have been broken):
res = contractWithSequence([a, b, c], seq=optimalSeq, inplace=True)
print(res)
print('')
def simplestExample():
shapeA = (300, 4, 5)
shapeB = (300, 6)
shapeC = (4, 6, 5)
a = Tensor(labels=['a300', 'b4', 'c5'], data=xplib.xp.ones(shapeA))
b = Tensor(labels=['a300', 'd6'], data=xplib.xp.ones(shapeB))
c = Tensor(labels=['e4', 'd6', 'c5'], data=xplib.xp.ones(shapeC))
# create tensors with labels
makeLink('a300', 'a300', a, b)
makeLink('c5', 'c5', a, c)
makeLink('d6', 'd6', b, c)
# make links via labels
# note that labels can also be made between the "Leg" objects to avoid reused leg names, but here for simplicity from leg names
# now we have a tensor network, we can generate the optimal sequence of this tensor list
optimalSeq = generateOptimalSequence([a, b, c])
print('optimal contraction sequence = {}'.format(optimalSeq))
# if we do not have any knowledge in prior, we can contract the tensor list like
res = contractTensorList([a, b, c])
print(res)
# if you already have a good sequence to use
res = contractWithSequence([a, b, c], seq=optimalSeq)
print(res)
# if you want to save time / space by contract in place(note that after this you cannot contract them again, since their bonds between have been broken):
res = contractWithSequence([a, b, c], seq=optimalSeq, inplace=True)
print(res)
print('')
# for reusable inplace contraction(which is our goal), refer to the use of CTL.tensornetwork.tensornetwork.FiniteTensorNetwork
return res
def test_outerProduct(self):
a = Tensor(shape=(2, ), labels=['a'])
b = Tensor(shape=(2, ), labels=['b'])
op = contractTwoTensors(a, b, outProductWarning=False)
self.assertTrue(funcs.compareLists(op.labels, ['a', 'b']))
a = Tensor(shape=(2, 2, 2), labels=['a', 'b', 'c'])
b = Tensor(shape=(2, ), labels=['x'])
c = Tensor(shape=(2, ), labels=['y'])
makeLink('a', 'x', a, b)
makeLink('b', 'y', a, c)
prod = contractTensorList([a, b, c], outProductWarning=False)
self.assertTrue(funcs.compareLists(prod.labels, ['c']))
dataA = np.random.random_sample((2, 2))
dataB = np.random.random_sample((3, 3))
a = Tensor(shape=(2, 2), labels=['a1', 'a2'], data=dataA)
b = Tensor(shape=(3, 3), labels=['b1', 'b2'], data=dataB)
prod = contractTensorList([a, b], outProductWarning=False)
prod.reArrange(['a1', 'a2', 'b1', 'b2'])
res = np.zeros((2, 2, 3, 3))
for i in range(2):
for j in range(2):
for k in range(3):
for l in range(3):
res[(i, j, k, l)] = dataA[(i, j)] * dataB[(k, l)]
# print(res, prod.a)
self.assertTrue(funcs.floatArrayEqual(res, prod.a))
a = Tensor(shape=(2, 2), labels=['a1', 'a2'], data=dataA)
b = DiagonalTensor(shape=(3, 3),
labels=['b1', 'b2'],
data=np.diag(dataB))
prod = contractTensorList([a, b], outProductWarning=False)
prodData = prod.toTensor(['a1', 'a2', 'b1', 'b2'])
# prod.reArrange(['a1', 'a2', 'b1', 'b2'])
res = np.zeros((2, 2, 3, 3))
for i in range(2):
for j in range(2):
for k in range(3):
# for l in range(3):
res[(i, j, k, k)] = dataA[(i, j)] * dataB[(k, k)]
# print(res, prod.a)
self.assertTrue(funcs.floatArrayEqual(res, prodData))
dataA = np.random.random_sample((2, 2))
dataB = np.random.random_sample(3)
a = Tensor(shape=(2, 2), labels=['a1', 'a2'], data=dataA)
b = DiagonalTensor(shape=(3, 3, 3),
labels=['b1', 'b2', 'b3'],
data=dataB)
prod = contractTensorList([a, b], outProductWarning=False)
prodData = prod.toTensor(['a1', 'a2', 'b1', 'b2', 'b3'])
# prod.reArrange(['a1', 'a2', 'b1', 'b2'])
res = np.zeros((2, 2, 3, 3, 3))
for i in range(2):
for j in range(2):
for k in range(3):
# for l in range(3):
res[(i, j, k, k, k)] = dataA[(i, j)] * dataB[k]
# print(res, prod.a)
self.assertTrue(funcs.floatArrayEqual(res, prodData))