def get_chargearray(num_charges, dtype=np.float64):
D = np.random.randint(8, 12)
charge = BaseCharge(np.random.randint(-5, 6, (num_charges, D)),
charge_types=[U1Charge] * num_charges)
flow = False
index = Index(charge, flow)
return ChargeArray.random(indices=[index], dtype=dtype)
def test_broadcast_right_multiplication_raises():
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
num_charges = 1
Ds = [10, 30, 24]
R = len(Ds)
indices = [
Index(
BaseCharge(np.random.randint(-5, 6, (num_charges, Ds[n])),
charge_types=[U1Charge] * num_charges), False)
for n in range(R)
]
tensor1 = backend.randn(indices)
tensor2 = ChargeArray.random(indices=indices)
with pytest.raises(ValueError):
backend.broadcast_right_multiplication(tensor1, tensor2)
def test_broadcast_left_multiplication(dtype, num_charges):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
Ds = [10, 30, 24]
R = len(Ds)
indices = [
Index(
BaseCharge(np.random.randint(-5, 6, (num_charges, Ds[n])),
charge_types=[U1Charge] * num_charges), False)
for n in range(R)
]
tensor1 = ChargeArray.random(indices=[indices[0]], dtype=dtype)
tensor2 = backend.randn(indices, dtype=dtype)
t1dense = tensor1.todense()
t2dense = tensor2.todense()
out = backend.broadcast_left_multiplication(tensor1, tensor2)
dense = np.reshape(t1dense, (10, 1, 1)) * t2dense
np.testing.assert_allclose(out.todense(), dense)
def test_broadcast_right_multiplication(dtype, num_charges):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
Ds = [10, 30, 24]
R = len(Ds)
indices = [
Index(
BaseCharge(np.random.randint(-5, 6, (Ds[n], num_charges)),
charge_types=[U1Charge] * num_charges), False)
for n in range(R)
]
tensor1 = backend.randn(indices, dtype=dtype)
tensor2 = ChargeArray.random(indices=[indices[-1].copy().flip_flow()],
dtype=dtype)
t1dense = tensor1.todense()
t2dense = tensor2.todense()
out = backend.broadcast_right_multiplication(tensor1, tensor2)
dense = t1dense * t2dense
np.testing.assert_allclose(out.todense(), dense)