def test_FreeBondFTN(self):
shapeA = (300, 4, 5)
shapeB = (300, 6)
shapeC = (4, 6, 5)
a = Tensor(shape=shapeA,
labels=['a300', 'b4', 'c5'],
data=np.ones(shapeA))
b = Tensor(shape=shapeB, labels=['a300', 'd6'], data=np.ones(shapeB))
c = Tensor(shape=shapeC,
labels=['e4', 'd6', 'c5'],
data=np.ones(shapeC))
tensorDict = TensorDict()
tensorDict.setTensor('a', a)
tensorDict.setTensor('b', b)
tensorDict.setTensor('c', c)
FTN = FiniteTensorNetwork(['a', 'b'], realCost=True)
FTN.addTensor('c')
FTN.addLink('a', 'a300', 'b', 'a300')
FTN.addLink('a', 'c5', 'c', 'c5')
FTN.addLink('b', 'd6', 'c', 'd6')
result = FTN.contract(tensorDict, removeTensorTag=False)
self.assertTrue(funcs.compareLists(result.labels, ['a-b4', 'c-e4']))
self.assertEqual(int(result.a[0][1]), 9000)
result = FTN.contract(tensorDict, removeTensorTag=True)
self.assertTrue(funcs.compareLists(result.labels, ['b4', 'e4']))
self.assertEqual(int(result.a[0][1]), 9000)
FTN.unlock()
FTN.addPostNameChange('c', 'e4', 'e4FromC')
FTN.addPreNameChange('a', 'b4', 'b4FromA')
FTN.addPreNameChange('a', 'a300', 'a3')
FTN.removePreNameChange('a', 'a300', 'a3')
FTN.addPostNameChange('a', 'd6', 'foo')
FTN.removePostNameChange('a', 'd6', 'foo')
result = FTN.contract(tensorDict, removeTensorTag=True)
# print(result.labels)
self.assertTrue(
funcs.compareLists(result.labels, ['b4FromA', 'e4FromC']))
self.assertEqual(int(result.a[0][1]), 9000)
FTN.unlock()
FTN.removePostNameChange('c', 'e4', 'e4FromC')
FTN.addPreNameChange('c', 'e4', 'e4FromC')
FTN.addPostOutProduct([('a', 'b4FromA'), ('c', 'e4FromC')], 'out')
result = FTN.contract(tensorDict, removeTensorTag=True)
self.assertListEqual(result.labels, ['out'])
self.assertEqual(result.shape[0], 16)
def test_FTN(self):
shapeA = (300, 4, 5)
shapeB = (300, 6)
shapeC = (4, 6, 5)
a = Tensor(shape=shapeA,
labels=['a300', 'b4', 'c5'],
data=np.ones(shapeA))
b = Tensor(shape=shapeB, labels=['a300', 'd6'], data=np.ones(shapeB))
c = Tensor(shape=shapeC,
labels=['b4', 'd6', 'c5'],
data=np.ones(shapeC))
tensorDict = TensorDict()
tensorDict.setTensor('a', a)
tensorDict.setTensor('b', b)
tensorDict.setTensor('c', c)
FTN = FiniteTensorNetwork(['a', 'b'], realCost=True)
FTN.addTensor('c')
FTN.addLink('a', 'a300', 'b', 'a300')
FTN.addLink('a', 'b4', 'c', 'b4')
FTN.addLink('a', 'c5', 'c', 'c5')
FTN.addLink('b', 'd6', 'c', 'd6')
result = FTN.contract(tensorDict)
self.assertEqual(int(result.a), 36000)
self.assertListEqual(FTN.optimalSeq, [(0, 1), (2, 0)])
result2 = FTN.contract(tensorDict)
self.assertEqual(int(result2.a), 36000)
self.assertListEqual(FTN.optimalSeq, [(0, 1), (2, 0)])
newShapeA = (3, 4, 5)
newShapeB = (3, 6)
newA = Tensor(shape=newShapeA,
labels=['a300', 'b4', 'c5'],
data=np.ones(newShapeA))
newB = Tensor(shape=newShapeB,
labels=['a300', 'd6'],
data=np.ones(newShapeB))
tensorDict.setTensor('a', newA)
tensorDict.setTensor('b', newB)
result3 = FTN.contract(tensorDict)
self.assertEqual(int(result3.a), 360)
self.assertListEqual(FTN.optimalSeq, [(0, 2), (1, 0)])
self.assertEqual(FTN.bondDims['a-a300'], 3)
self.assertEqual(FTN.tensorCount, 3)
def HOTRGGiltTN():
ATensorNames = ["aul", "aur", "adl", "adr"]
QTensorNames = [
'qbll', 'qbrl', 'qall', 'qarl', 'qblr', 'qbrr', 'qalr', 'qarr', 'qbu',
'qbd', 'qau', 'qad'
] # i(circle), o(line)
wTensorNames = ['wll', 'wlr', 'wrl', 'wrr'] # u, d, o
vTensorNames = ['vu', 'vd'] # l, r, o
tensorNames = ATensorNames + QTensorNames + wTensorNames + vTensorNames
FTN = FiniteTensorNetwork(tensorNames=tensorNames)
# FTN.addLink('aul', 'adl', 'd', 'u')
# FTN.addLink('aur', 'adr', 'd', 'u')
# up / down bonds
FTN.addLink('vu', 'l', 'qbu', 'o')
FTN.addLink('vu', 'r', 'qau', 'o')
FTN.addLink('vd', 'l', 'qbd', 'o')
FTN.addLink('vd', 'r', 'qad', 'o')
# left / right bonds
FTN.addLink('wll', 'u', 'qbll', 'o')
FTN.addLink('wll', 'd', 'qall', 'o')
FTN.addLink('wrr', 'u', 'qbrr', 'o')
FTN.addLink('wrr', 'd', 'qarr', 'o')
# A left bonds
FTN.addLink('aul', 'u', 'qbu', 'i')
FTN.addLink('adl', 'd', 'qbd', 'i')
FTN.addLink('aul', 'd', 'adl', 'u')
FTN.addLink('aul', 'l', 'qbll', 'i')
FTN.addLink('adl', 'l', 'qall', 'i')
FTN.addLink('aul', 'r', 'qbrl', 'i')
FTN.addLink('adl', 'r', 'qarl', 'i')
# A right bonds
FTN.addLink('aur', 'u', 'qau', 'i')
FTN.addLink('adr', 'd', 'qad', 'i')
FTN.addLink('aur', 'd', 'adr', 'u')
FTN.addLink('aur', 'l', 'qblr', 'i')
FTN.addLink('adr', 'l', 'qalr', 'i')
FTN.addLink('aur', 'r', 'qbrr', 'i')
FTN.addLink('adr', 'r', 'qarr', 'i')
# center w bonds
FTN.addLink('wlr', 'u', 'qbrl', 'o')
FTN.addLink('wlr', 'd', 'qarl', 'o')
FTN.addLink('wrl', 'u', 'qblr', 'o')
FTN.addLink('wrl', 'd', 'qalr', 'o')
FTN.addLink('wlr', 'o', 'wrl', 'o')
tensorDict = TensorDict()
chi = 10
for ATensorName in ATensorNames:
tensorDict.setTensor(
ATensorName,
Tensor(data=np.random.randn(chi, chi, chi, chi),
labels=['u', 'l', 'd', 'r']))
for QTensorName in QTensorNames:
tensorDict.setTensor(
QTensorName,
Tensor(data=np.random.randn(chi, chi), labels=['i', 'o']))
for wTensorName in wTensorNames:
tensorDict.setTensor(
wTensorName,
Tensor(data=np.random.randn(chi, chi, chi), labels=['u', 'd',
'o']))
for vTensorName in vTensorNames:
tensorDict.setTensor(
vTensorName,
Tensor(data=np.random.randn(chi, chi, chi), labels=['l', 'r',
'o']))
res = FTN.contract(tensorDict)
print(res)
