def model_from_pyqubo_model():
print("\n=== from pyqubo compiled model ===")
model = sawatabi.model.LogicalModel(mtype="qubo")
x = model.variables("x", shape=(4, 2))
y = model.variables("y", shape=(4, 2))
sum_x = sum(x[i, 0] for i in range(4))
sum_y = sum(y[i, 0] for i in range(4))
hamiltonian = Placeholder("A") * (sum_x - sum_y)**2 + 10.0
pyqubo_model = hamiltonian.compile()
model.from_pyqubo(pyqubo_model)
print("\nInteractions are set with pyqubo expression.")
_print_utf8(model)
print("\nCheck also the physical model after placeholder resolves.")
physical = model.to_physical({"A": 2.0})
_print_utf8(physical)
def define_model(self):
'''
pyqubo.Modelインスタンス作成。モデル作成結果はself.modelに格納
H1(距離 目的関数)、H2,H3（都市、順序のワンホット制約)
parameters
-----
なし
returns
-----
なし
'''
city_num = self.size
#変数定義
x = Array.create('x', shape=(city_num, city_num), vartype="BINARY")
#距離のペナルティ
H1 = 0
#i番目に都市j、i+1番目に都市kを訪れる
for i, j in product(range(city_num), range(city_num)):
for k in range(city_num):
H1 += self.dist(j, k) * x[i][j] * x[(i + 1) % city_num][k]
#ある時刻には訪れる都市は1つだけ
H2 = 0
for i in range(city_num):
H2 += Constraint((Sum(0, city_num, lambda j: x[i][j]) - 1)**2,
label=f"Constraint one hot for time{i}")
#ある都市に訪れるのは1度だけ
H3 = 0
for i in range(city_num):
H3 += Constraint((Sum(0, city_num, lambda j: x[j][i]) - 1)**2,
label=f"Constraint one hot for city {i}")
H = Placeholder("H1") * H1 + H2 + H3
#モデル作成
self.model = H.compile()
#エージェントの利用可能資源量を超えない
H2 = Sum(
0, n, lambda i: Constraint(
Sum(0, m, lambda j: x[
(i, j)] * a[i, j] + y[i] - b[i])**2, f"Agent Resource {i}"))
#H2 = Sum(0,n,lambda i: Sum(0,m,lambda j: x[(i,j)]*a[i,j] - b[i])**2)
#全ての仕事をエージェントに割り振る
H3 = Sum(0, m,
lambda j: Constraint(Sum(0, n, lambda i: x[i][j] - 1)**2, f"Job{j}"))
H = Placeholder("balancer1") * H1 + Placeholder(
"balancer2") * H2 + Placeholder("balancer3") * H3
model = H.compile()
#パラメータ探索
study = optuna.create_study(storage='sqlite:///example.db',
study_name=f"m{m}n{n}_DW2000Q",
load_if_exists=True)
study.optimize(objective, n_trials=200)
b1 = study.best_params["b1"]
b2 = study.best_params["b2"]
b3 = study.best_params["b3"]
def_dict = {
"balancer1": b1,
"balancer2": b2,
"balancer3": b3,