name_list_A = [f"A.{i}" for i in range(rank_A)]
name_list_B = [f"B.{i}" for i in range(rank_B)]
for i in range(rank_fuse):
name_list_A[fuse_leg_A[i]] = f"{i}"
name_list_B[fuse_leg_B[i]] = f"{i}"
dim_A = np.random.randint(1, max_random, rank_A).tolist()
dim_B = np.random.randint(1, max_random, rank_B).tolist()
for i in range(rank_total):
dim_A[total_leg_A[i]] = dim_B[total_leg_B[i]] = np.random.randint(
2, max_random)
A = Tensor(name_list_A, dim_A).test()
B = Tensor(name_list_B, dim_B).test()
C = A.contract(
B, {(f"A.{contract_leg_A[i]}", f"B.{contract_leg_B[i]}")
for i in range(rank_contract)})
# print(repr(A))
# print(repr(B))
# print(repr(C))
a = A.block[{}]
b = B.block[{}]
index_A = [chr(ord('a') + i) for i in range(rank_A)]
index_B = [chr(ord('A') + i) for i in range(rank_B)]
index_C = []
for i in range(rank_total):
index_A[total_leg_A[i]] = index_B[total_leg_B[i]]
# fuse的顺序是按照name自身顺序来的
for i in fuse_leg_A:
index_C.append(index_A[i])
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
import numpy as np
from TAT.Tensor import DNo as Tensor
max_random = 8
for _ in range(100):
rank_A = np.random.randint(2, max_random)
rank_B = np.random.randint(2, max_random)
rank_contract = np.random.randint(1, np.min([rank_A, rank_B]))
# print(rank_A, rank_B, rank_contract)
contract_name_A = np.random.choice(range(rank_A), rank_contract, False)
contract_name_B = np.random.choice(range(rank_B), rank_contract, False)
dim_A = np.random.randint(1, max_random, size=rank_A)
dim_B = np.random.randint(1, max_random, size=rank_B)
dim_contract = np.random.randint(1, max_random, size=rank_contract)
dim_A = [j if i not in contract_name_A else dim_contract[contract_name_A.tolist().index(i)] for i, j in enumerate(dim_A)]
dim_B = [j if i not in contract_name_B else dim_contract[contract_name_B.tolist().index(i)] for i, j in enumerate(dim_B)]
A = Tensor([f"A.{i}" for i in range(rank_A)], dim_A).randn()
B = Tensor([f"B.{i}" for i in range(rank_B)], dim_B).randn()
v_t = A.contract(B, {(f"A.{i}", f"B.{j}") for i, j in zip(contract_name_A, contract_name_B)}).block[{}]
v_n = np.tensordot(A.block[{}], B.block[{}], [contract_name_A, contract_name_B])
v_d = v_t - v_n
print(np.max(np.abs(v_d)))