def applyHToM(HL, HR, H, M, k):
k1 = k
k2 = k + 1
# Add HL.opSum x h.identity(k1) x h.identity(k2) x I(Right)
# and I(Left) x h.identity(k1) x h.identity(k2) x HR.opSum
Hv = bops.multiContraction(HL.opSum, M, '0', '0')
Hv = bops.addNodes(Hv, bops.multiContraction(M, HR.opSum, '3', '0'))
# Add I(Left) x h.single(k1) x h.identity(k2) x I(Right)
# And I(Left) x h.identity(k1) x h.single(k2) x I(Right)
Hv = bops.addNodes(Hv, \
tn.transpose(bops.multiContraction(M, H.singles[k1], '1', '0'), [0, 3, 1, 2]))
Hv = bops.addNodes(Hv, \
tn.transpose(bops.multiContraction(M, H.singles[k2], '2', '0'), [0, 1, 3, 2]))
# Add HL.openOp x h.r2l(k1) x h.identity(k2) x I(Right)
# And I(Left) x h.identity(k1) x h.l2r(k2) x HR.openOp
HK1R2L = bops.permute(bops.multiContraction(M, H.r2l[k1], '1', '0'),
[0, 4, 3, 1, 2])
Hv = bops.addNodes(Hv, \
bops.multiContraction(HL.openOp, HK1R2L, '02', '01'))
HK2L2R = bops.permute(bops.multiContraction(M, H.l2r[k2], '2', '0'),
[0, 1, 3, 4, 2])
Hv = bops.addNodes(Hv, \
bops.multiContraction(HK2L2R, HR.openOp, '43', '02'))
# Add I(Left) x h.l2r(k1) x h.r2l(k2) x I(Right)
HK1K2 = bops.multiContraction(M, H.l2r[k1], '1', '0')
HK1K2 = bops.multiContraction(HK1K2, H.r2l[k2], '14', '02')
HK1K2 = bops.permute(HK1K2, [0, 2, 3, 1])
Hv = bops.addNodes(Hv, HK1K2)
return Hv
def test_transpose(dtype, num_charges):
np.random.seed(10)
a = tn.Node(get_random((6, 7, 8, 9, 10),
num_charges=num_charges,
dtype=dtype),
backend='symmetric')
order = [a[n] for n in reversed(range(5))]
transpa = tn.transpose(a, [4, 3, 2, 1, 0])
a.reorder_edges(order)
np.testing.assert_allclose(a.tensor.data, transpa.tensor.data)
def permute(node: tn.Node, permutation):
if node is None:
return None
axisNames = []
for i in range(len(permutation)):
axisNames.append(node.edges[permutation[i]].name)
result = tn.transpose(node, permutation, axis_names=axisNames)
result.add_axis_names(axisNames)
for i in range(len(axisNames)):
result.get_edge(i).set_name(axisNames[i])
result.set_name(node.name)
return result
def test_transpose_vs_backend(backend, dtype):
"""
Tests that transpose yields the same result as the backend equivalent.
"""
shape = (3, 2, 4)
permutation = (1, 2, 0)
tensor, array = testing_utils.safe_randn(shape, backend, dtype)
if tensor is not None:
backend_obj = backends.backend_factory.get_backend(backend)
test = backend_obj.convert_to_tensor(array)
test = backend_obj.transpose(test, perm=permutation)
tensor_test = tensornetwork.transpose(tensor, perm=permutation)
np.testing.assert_allclose(test, tensor_test.array)
def test_transpose_of_node_without_backend_raises_error():
node = np.random.rand(3, 3, 3)
with pytest.raises(AttributeError):
tn.transpose(node, permutation=[])
def test_transpose(backend):
a = tn.Node(np.random.rand(1, 2, 3, 4, 5), backend=backend)
order = [a[n] for n in reversed(range(5))]
transpa = tn.transpose(a, [4, 3, 2, 1, 0])
a.reorder_edges(order)
np.testing.assert_allclose(a.tensor, transpa.tensor)
