result = []
alpha = alpha_root - 0.1 * idx
# st = st_root + 2*idx
tmin = alpha**(m)
ep = []
tools = Tool(data)
start = datetime.datetime.now().timestamp()
a, b, c = tools.Stimulated_Annealing(t0, tmin, alpha, st)
end = datetime.datetime.now().timestamp()
time.append(end - start)
for i in range(10):
np.random.shuffle(data)
tools = Tool(data)
a, b, c, e = tools.Stimulated_Annealing_Epoch(
t0, tmin, alpha, st, epoch)
ep.append(e)
if b < bestD:
bestR = np.copy(a)
bestD = b
dict = copy.deepcopy(c)
ep = np.asarray(ep)
resultSum.append(np.sum(ep, axis=0).tolist())
resultSD.append(np.std(ep, axis=0).tolist())
result = copy.deepcopy(resultSum)
result.extend(resultSD)
# print(result)
with open("Result\Summary\AS" + name + "_" + str(alpha) + "alpha_" +
str(m) + "iterate_" + str(st) + "swaptime.csv",
"w",
