def get_square_matrix(shape, num_charges, dtype=np.float64):
charge = BaseCharge(
np.random.randint(-5, 5, (shape, num_charges)),
charge_types=[U1Charge] * num_charges)
flows = [True, False]
indices = [Index(charge, flows[n]) for n in range(2)]
return BlockSparseTensor.random(indices=indices, dtype=dtype)
def get_random(shape, num_charges, dtype=np.float64):
R = len(shape)
charges = [
BaseCharge(np.random.randint(-5, 5, (num_charges, shape[n])),
charge_types=[U1Charge] * num_charges) for n in range(R)
]
flows = list(np.full(R, fill_value=False, dtype=np.bool))
indices = [Index(charges[n], flows[n]) for n in range(R)]
return BlockSparseTensor.random(indices=indices, dtype=dtype)
def get_random_symmetric(shape, flows, num_charges, seed=10, dtype=np.float64):
assert np.all(np.asarray(shape) == shape[0])
np.random.seed(seed)
R = len(shape)
charge = BaseCharge(np.random.randint(-5, 5, (shape[0], num_charges)),
charge_types=[U1Charge] * num_charges)
indices = [Index(charge, flows[n]) for n in range(R)]
return BlockSparseTensor.random(indices=indices, dtype=dtype)
def test_sparse_shape(num_charges):
np.random.seed(10)
dtype = np.float64
shape = [10, 11, 12, 13]
R = len(shape)
charges = [
BaseCharge(np.random.randint(-5, 5, (shape[n], num_charges)),
charge_types=[U1Charge] * num_charges) for n in range(R)
]
flows = list(np.full(R, fill_value=False, dtype=np.bool))
indices = [Index(charges[n], flows[n]) for n in range(R)]
a = BlockSparseTensor.random(indices=indices, dtype=dtype)
node = tn.Node(a, backend='symmetric')
for s1, s2 in zip(node.sparse_shape, indices):
assert s1 == s2