def ga_opt(self, max_=False):
if not max_:
ga = GA(func=self.func, n_dim=len(self.lb), size_pop=50, max_iter=int(self.repeaT), lb=self.lb, ub=self.ub,
constraint_eq=self.eq_ls, constraint_ueq=self.ueq_ls, precision=1e-5)
self.best_x, self.best_y = ga.run()
elif max_:
ga = GA(func=-self.func, n_dim=len(self.lb), size_pop=50, max_iter=int(self.repeaT), lb=self.lb, ub=self.ub,
constraint_eq=self.eq_ls, constraint_ueq=self.ueq_ls, precision=1e-5)
self.best_x, self.best_y = ga.run()
self.best_y = -self.best_y
def fit_ga(self, y0, E, I, R, n):
self.parameter = [E, I, R, y0]
self.y0 = y0
if n == 4:
ga = GA(func=self.loss_function_ga,
n_dim=4,
size_pop=50,
max_iter=300,
lb=[0, 0, 0, 0],
ub=[1, 1, 1, 1])
elif n == 2:
ga = GA(func=self.loss_function_ga_2,
n_dim=2,
size_pop=50,
max_iter=300,
lb=[0, 0],
ub=[1, 1]) # 遗传算法求解
best_x, best_y = ga.run()
if n == 4:
alpha, beta1, beta2, gamma2 = best_x[0], best_x[1], best_x[
2], best_x[3]
return alpha, beta1, beta2, gamma2
elif n == 2:
alpha, gamma2 = best_x[0], best_x[1]
return alpha, gamma2
def trainab(model_name, dataset_name, iter_num):
# init T,R,SP
T = getSGGRes(model_name, dataset_name, "train", tasktype)
R = getGT(model_name, dataset_name, "train", tasktype)
SP, N = getPfromR(R, dataset_name)
print("T info: ", len(T), len(T[0]), T[0][0])
print("R info: ", len(R), len(R[0]), R[0][0])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dd = 1e23
# start iteration
for i in range(iter_num):
ga = GA(func=fitness, n_dim=2, size_pop=10, max_iter=50, \
lb=[0.0, 0.0], ub=[dd, dd], precision=1e-7)
#ga = GA(func=fitness, n_dim=2, size_pop=10, max_iter=4, \
# lb=[0.0, 0.0], ub=[dd, dd], precision=1e-7)
ga.to(device=device) #GPU
best_x, best_y = ga.run() # best_x={alpha,beta},best_y=Q-R
alpha, beta = best_x
print('iteration ', i, ': best alpha and beta: ', alpha, beta)
print('best_y:', best_y)
if best_y == 0:
print('best {alpha,beta}:', best_x, 'best_y', best_y)
end_cnt = fitness(best_x)
print("detect if zeros: ", end_cnt)
if end_cnt == 0:
break
else:
R = ReSet(alpha, beta)
SP, _ = getPfromR(R, dataset_name)
return best_x
def fit2(self):
# ga_beta = GA(func=self.loss_function2_beta, n_dim=2, size_pop=50, max_iter=1000, lb=[0, 0], ub=[1, 1], precision=1e-7)
# ga_gamma = GA(func=self.loss_function2_gamma, n_dim=2, size_pop=50, max_iter=1000, lb=[0, 0], ub=[1, 1], precision=1e-7)
# best_x_beta, best_y = ga_beta.run()
# best_x_gamma, best_y = ga_gamma.run()
ga = GA(func=self.loss_function2, n_dim=2, size_pop=50, max_iter=3000, lb=[0, 0], ub=[1, 1], precision=1e-7)
best_x, best_y = ga.run()
# self.__beta = best_x_beta[0]
# self.__gamma = best_x_gamma[1]
self.__beta = best_x[0]
self.__gamma = best_x[1]
print('best_x:', best_x)
def reg_time(args):
#假设TEST_TIME为100
#总运行次数小于T1(比如200)时，运行次数就是运行次数
#总运行次数大于T1(比如200)时，计算运行次数乘以TEST_TIME/实际运行次数与T2的调和平均数
#这里T1代表了实际最大运算次数，T2代表了计算结果的最大重算次数
kind, f, p_min = args
print(".", end="")
res_ary = [1e+500]
if kind == "de":
de = DE(func=f,
n_dim=DIM,
lb=LB,
ub=UB,
max_iter=ITER // 2,
size_pop=POP)
time_de = 0
while res_ary[-1] > p_min + MIN_PRECISION:
if len(res_ary) > STOP_ITER_TIME and res_ary[
-STOP_ITER_TIME] - res_ary[-1] < CALC_PRECISION:
return (False, time_de - STOP_ITER_TIME)
_, temp_de = de.run()
res_ary.append(temp_de[0])
time_de += 1
return (True, time_de)
if kind == "ga":
ga = GA(func=f,
n_dim=DIM,
lb=LB,
ub=UB,
max_iter=ITER,
size_pop=POP,
precision=1e-200)
time_ga = 0
while res_ary[-1] > p_min + MIN_PRECISION:
if len(res_ary) > STOP_ITER_TIME and res_ary[
-STOP_ITER_TIME] - res_ary[-1] < CALC_PRECISION:
return (False, time_ga - STOP_ITER_TIME)
_, temp_ga = ga.run()
res_ary.append(temp_ga[0])
time_ga += 1
return (True, time_ga)
if kind == "pso":
pso = PSO(func=f, dim=DIM, pop=POP, max_iter=ITER, lb=LB, ub=UB)
time_pso = 0
while res_ary[-1] > p_min + MIN_PRECISION:
if len(res_ary) > STOP_ITER_TIME and res_ary[
-STOP_ITER_TIME] - res_ary[-1] < CALC_PRECISION:
return (False, time_pso - STOP_ITER_TIME)
pso.run()
res_ary.append(pso.gbest_y)
time_pso += 1
return (True, time_pso)
def trainabGA(model_name,dataset_name,iter_num):
# init T,R,SP
T = getSGGRes(model_name,dataset_name,"train",tasktype)
R = getGT(model_name,dataset_name,"train",tasktype)
SP,N = getPfromR(R,dataset_name)
print("T info: ",len(T),len(T[0]),T[0][0])
print("R info: ",len(R),len(R[0]),R[0][0])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# start iteration
low = [0.0,0.0]
high = [1e6,1e6]
for i in range(iter_num):
#ga = GA(func=fitness, n_dim=2, size_pop=4, max_iter=10, \
# lb=[0.0, 0.0], ub=[1.0, 1.0], precision=1e-7)
#ga = GA(func=fitness, n_dim=2, size_pop=6, max_iter=50, \
# lb=low, ub=high, precision=1e-7)
#ga = GA(func=fitness, n_dim=2, size_pop=50, max_iter=50, \
# lb=low, ub=high, precision=1e-7)
ga = GA(func=fitnessNDCG, n_dim=2, size_pop=100, max_iter=100, \
lb=low, ub=high, precision=1e-7)
ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3).\
register(operator_name='ranking', operator=ranking.ranking). \
register(operator_name='crossover', operator=crossover.crossover_2point). \
register(operator_name='mutation', operator=mutation.mutation)
ga.to(device=device) #GPU
start_time = time.time()
best_x, best_y = ga.run()# best_x={alpha,beta},best_y=Q-R
print("run time: ",time.time() - start_time)
alpha, beta = best_x
print('iteration ',i,': best alpha and beta: ', alpha,beta)
print('best_y:', best_y)
print("sum_ndcg: ",-best_y-0.5*(alpha**2+beta**2))
if best_y==0:
print('best {alpha,beta}:',best_x,'best_y',best_y)
end_cnt = fitnessNDCG(best_x)
print("detect if zeros: ",end_cnt)
if end_cnt==0:
break
else:
#R = ReSet(alpha,beta)
R = addsmallNDCG(alpha,beta,R)
SP,_ = getPfromR(R,dataset_name)
cnt_now=fitness(best_x)
print(i," iter :", cnt_now)
return best_x
class EvolutionarySearch:
def __init__(self,
env,
pop_size=100,
genotype_size=61,
lb=-1,
ub=1,
max_iter=100,
init=None):
self.env = env
global E
E = env
#The evolutionary search object
self.ga = GA(func=EvaluateGenome,
n_dim=genotype_size,
size_pop=pop_size,
max_iter=max_iter,
lb=lb,
ub=ub,
precision=1e-7)
if init is not None:
self.ga.Chrom = init
def calc_genotype(self, pheno_dict):
genotype_length = 0
for key in pheno_dict:
genotype_length += len(pheno_dict[key])
return genotype_length
def run(self, increments=10):
total_its = self.ga.max_iter // increments
for i in range(total_its):
best_x, best_y = self.ga.run(max_iter=increments)
gen = (i + 1) * increments
print(f'Generation: {gen}')
print('best_x:', best_x, '\n', 'best_y:', best_y)
# %% Plot the result
Y_history = pd.DataFrame(self.ga.all_history_Y)
fig, ax = plt.subplots(2, 1)
ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
Y_history.min(axis=1).cummin().plot(kind='line')
plt.show()
fig.savefig('./evo_run.pdf')
Y_history.to_pickle("Run_history.pkl")
return best_x
def GAmethod(max_iter=50, size_pop=500):
lb = lb
ub = ub
ga = GA(func=calculEffFunc,
n_dim=5,
size_pop=size_pop,
max_iter=max_iter,
lb=lb,
ub=ub,
precision=1e-7,
prob_mut=prob_mut) #只求解最小值
best_x, best_y = ga.run()
Y_history = ga.all_history_Y
return best_x, best_y, Y_history, ga.generation_best_Y
def findWay(tryNum): # 找从起点移动tryNum步距终点最近的路径
lBoundary = [] # 存自变量下界
uBoundary = [] # 存自变量上界
precisions = [] # 存自变量精度
for i in range(tryNum): # 0，1，2，3代表四个方向，从0到3，精度为1
lBoundary.append(0)
uBoundary.append(3)
precisions.append(1)
ga = GA(func=demo_func,
n_dim=tryNum,
size_pop=150,
max_iter=1000,
lb=lBoundary,
ub=uBoundary,
precision=precisions)
return ga.run()
def GA_CAV_included_best_UE(control_portion):
constraint_ueq = [
lambda x: sum(x) - 5 * control_portion,
]
# ga = SA(func=CAV_UE, x0=[0]*15, n_dim=15, size_pop=50, max_iter=200, lb=[0]*15, ub=[5]*15, precision=1e-7, constraint_eq=constraint_eq)
# ga = PSO(func=CAV_UE, n_dim=15, size_pop=50, max_iter=200, lb=[0]*15, ub=[5]*15, precision=1e-7, constraint_eq=constraint_eq)
set_run_mode(CAV_UE, 'multiprocessing')
ga = GA(func=CAV_UE,
n_dim=2,
size_pop=50,
max_iter=800,
lb=[0] * 2,
ub=[5 * control_portion] * 2,
precision=1e-5,
constraint_ueq=constraint_ueq)
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
return best_y
def GAmethod(max_iter=50):
start_time = datetime.now()
ga = GA(func=finallFunction,
n_dim=5,
size_pop=size_pop,
max_iter=max_iter,
lb=lb,
ub=ub,
prob_mut=prob_mut) #只求解最小值
end_time = datetime.now()
print('Itreation numbers is {}, weight is '.format(max_iter) +
str(weight) +
' using time is {}'.format((end_time - start_time).total_seconds()))
best_x, best_y = ga.run()
Y_history = ga.all_history_Y
tm = time.localtime(time.time())
tm_str = '{}-{}-{} {}:{}:{} '.format(tm[0], tm[1], tm[2], tm[3], tm[4],
tm[5])
with open(r'GArecordHistory_Y.txt', 'a') as f:
for yh in ga.all_history_Y:
f.write(str(yh)[1:-1] + '\n\n')
with open(r'GAbestPredictCircuit.txt', 'a') as f:
f.write(tm_str +
'GA algorithm iteration number is {} best Predict Circuit is '.
format(max_iter) + str(bestPredictCircuit)[1:-1] +
' using time is {}'.format((end_time -
start_time).total_seconds()) +
'\n')
with open(r'GAbestStructure.txt', 'a') as f:
f.write(
tm_str +
'GA algorithm iteration number is {}, weight is {} best Structure is '
.format(max_iter, weight) + str(best_x)[1:-1] + ' ' +
'Best result is ' +
str(bestPredictCircuit[0] - bestPredictCircuit[1] +
bestPredictCircuit[2] - bestPredictCircuit[3]) + '\n')
return best_x, best_y, Y_history, ga
def _calT():
def obj_func(p):
x1, x2 = p
return np.abs(r[kx, ky, 1] - x1 - x2 + r[kx - 1, ky, 0])
if r[kx, ky + 1, 1] == NaN:
constraint_ueq = [lambda x: np.abs(r[kx, ky, 1] - x[0]) - 0.1]
ga = GA(func=obj_func,
n_dim=2,
size_pop=popsize,
max_iter=maxiter,
lb=[0, -r[kx - 1, ky + 1, 2]],
ub=[r[kx, ky + 1, 2] + prec, r[kx - 1, ky + 1, 2] + prec],
precision=[prec, prec],
constraint_ueq=constraint_ueq)
else:
constraint_ueq = [
lambda x: np.abs(r[kx, ky, 0] - x[0]) - 0.1,
lambda x: np.abs(r[kx - 1, ky, 1] - x[1]) - 0.1,
]
constraint_eq = [
lambda x: r[kx, ky + 1, 2]**2 - r[kx, ky + 1, 1]**2 - x[0]**2
]
ga = GA(func=obj_func,
n_dim=2,
size_pop=popsize,
max_iter=maxiter,
lb=[0, -r[kx - 1, ky + 1, 2]],
ub=[r[kx, ky + 1, 2] + prec, r[kx - 1, ky + 1, 2] + prec],
precision=[prec, prec],
constraint_eq=constraint_eq,
constraint_ueq=constraint_ueq)
best_x, _ = ga.run()
r[kx, ky + 1, 0] = best_x[0]
r[kx - 1, ky + 1, 1] = best_x[1]
return
x1, x2 = p
x = np.square(x1) + np.square(x2)
return 0.5 + (np.square(np.sin(x)) - 0.5) / np.square(1 + 0.001 * x)
your_class = YourClass()
set_run_mode(your_class.obj_func2, 'vectorization')
set_run_mode(your_class.obj_func3, 'parallel')
ga1 = GA(func=your_class.obj_func1, n_dim=2, size_pop=10, max_iter=5, lb=[-1, -1], ub=[1, 1], precision=1e-7)
ga2 = GA(func=your_class.obj_func2, n_dim=2, size_pop=10, max_iter=5, lb=[-1, -1], ub=[1, 1], precision=1e-7)
ga3 = GA(func=your_class.obj_func3, n_dim=2, size_pop=10, max_iter=5, lb=[-1, -1], ub=[1, 1], precision=1e-7)
start_time = datetime.datetime.now()
best_x, best_y = ga1.run()
print('common mode, time costs: ', (datetime.datetime.now() - start_time).total_seconds())
start_time = datetime.datetime.now()
best_x, best_y = ga2.run()
print('vector mode, time costs: ', (datetime.datetime.now() - start_time).total_seconds())
start_time = datetime.datetime.now()
best_x, best_y = ga3.run()
print('parallel mode, time costs: ', (datetime.datetime.now() - start_time).total_seconds())
# %%
set_run_mode(your_class.obj_func4_2, 'cached')
ga4_1 = GA(func=your_class.obj_func4_1, n_dim=2, size_pop=6, max_iter=10, lb=[-2, -2], ub=[2, 2], precision=1)
ga4_2 = GA(func=your_class.obj_func4_2, n_dim=2, size_pop=6, max_iter=10, lb=[-2, -2], ub=[2, 2], precision=1)
ga = GA(func=func, n_dim=2, size_pop=50, max_iter=1, precision=1e-5,lb=[-3.5,4.1], ub=[12.1,5.8])
b1=np.array([-3.5,4.1])
b2=np.array([12.1,5.8])
ga1 = GA(func=func, n_dim=2, size_pop=50, max_iter=1, precision=1e-5,lb=[-3.5,4.1], ub=[12.1,5.8])
fk1=[]
fk2=[]
fk3=[]
fk4=[]
coll_t1=[]
coll_t2=[]
for i in range(60):
best_x, best_y = ga.run(1)
best_11, best_12 = ga1.run(1)
print(best_y)
print(best_12)
t1=ga.X
t2=ga.Y
if i<11:
coll_t1.append(t1)
coll_t2.append(t2)
else:
best_model=prn.train_LM(t2.T,t1.T,best_model,verbose=False,k_max=10,E_stop=1e-8)
if i==10:
t1=np.array(coll_t1).reshape(-1,2).T
t2=np.array(coll_t2).reshape(-1,1).T
np.savetxt('t1',t1)
# 计算预测结果
def f_fun(x, a, b, c, d):
return a * x**3 + b * x**2 + c * x + d
# 计算loss
def obj_fun(p):
a, b, c, d = p
# 计算残差 loss
loss = np.square(f_fun(x_true, a, b, c, d) - y_true).sum()
return loss
# 使用 scikit-opt 做最优化
ga = GA(func=obj_fun,
n_dim=4,
size_pop=100,
max_iter=500,
lb=[-2] * 4,
ub=[2] * 4)
best_params, loss = ga.run()
print('best_x:', best_params, '\n', 'best_y:', loss)
# 画出拟合效果图
# 得到预测值
y_predict = f_fun(x_true, *best_params)
fig, ax = plt.subplots()
ax.plot(x_true, y_true, 'o')
ax.plot(x_true, y_predict, '-')
plt.show()
def P_min(f):
other_mins = []
de = DE(func=f, n_dim=DIM, lb=LB, ub=UB, max_iter=ITER // 2, size_pop=POP)
ga = GA(func=f,
n_dim=DIM,
lb=LB,
ub=UB,
max_iter=ITER,
size_pop=POP,
precision=1e-200)
pso = PSO(func=f, dim=DIM, pop=POP, max_iter=ITER, lb=LB, ub=UB)
_, e_de = de.run()
e_de = e_de[0]
_, e_ga = ga.run()
e_ga = e_ga[0]
pso.run()
e_pso = pso.gbest_y
time_de, time_ga, time_pso = 0, 0, 0
cnt = 0
while True:
de_x, temp_de = de.run()
temp_de = temp_de[0]
time_de = time_de + 1 if e_de - temp_de < CALC_PRECISION else 0
e_de = temp_de
ga_x, temp_ga = ga.run()
temp_ga = temp_ga[0]
time_ga = time_ga + 1 if e_ga - temp_ga < CALC_PRECISION else 0
e_ga = temp_ga
pso.run()
time_pso = time_pso + 1 if e_pso - pso.gbest_y < CALC_PRECISION else 0
e_pso = pso.gbest_y
res = sorted([[e_de, time_de, de_x], [e_ga, time_ga, ga_x],
[e_pso, time_pso, pso.gbest_x]],
key=lambda x: x[0])
if res[1][0] - res[0][0] < CALC_PRECISION and res[0][
1] > STOP_ITER_TIME:
return (res[0][0], res[0][2])
if res[0][1] > STOP_ITER_TIME and res[1][1] > STOP_ITER_TIME and res[
2][1] > STOP_ITER_TIME:
other_mins.append((res[0][0], res[0][2]))
if len(other_mins) == 10:
return min(other_mins, key=lambda x: x[0])
res = []
de = DE(func=f,
n_dim=DIM,
lb=LB,
ub=UB,
max_iter=ITER // 2,
size_pop=POP)
ga = GA(func=f,
n_dim=DIM,
lb=LB,
ub=UB,
max_iter=ITER,
size_pop=POP,
precision=1e-200)
pso = PSO(func=f, dim=DIM, pop=POP, max_iter=ITER, lb=LB, ub=UB)
de_x, e_de = de.run()
e_de = e_de[0]
ga_x, e_ga = ga.run()
e_ga = e_ga[0]
pso.run()
e_pso = pso.gbest_y
time_de, time_ga, time_pso = 0, 0, 0
costly_function()
x1, x2 = p
x = np.square(x1) + np.square(x2)
return 0.5 + (np.square(np.sin(x)) - 0.5) / np.square(1 + 0.001 * x)
for mode in ('common', 'multithreading', 'multiprocessing'):
set_run_mode(obj_func, mode)
ga = GA(func=obj_func,
n_dim=2,
size_pop=10,
max_iter=5,
lb=[-1, -1],
ub=[1, 1],
precision=1e-7)
start_time = datetime.datetime.now()
best_x, best_y = ga.run()
print(
'on {task_type} task,use {mode} mode, costs {time_costs}s'.format(
task_type=task_type,
mode=mode,
time_costs=(datetime.datetime.now() -
start_time).total_seconds()))
# to use the vectorization mode, the function itself should support the mode.
mode = 'vectorization'
def obj_func2(p):
costly_function()
x1, x2 = p[:, 0], p[:, 1]
x = np.square(x1) + np.square(x2)
return 0.5 + (np.square(np.sin(x)) - 0.5) / np.square(1 + 0.001 * x)
lambda x: np.sum(x[:, 0]),
lambda x: np.sum(x[:, -1]) - 1,
lambda x: np.sum(x[-1, :]),
]
constraint_ueq = [
lambda x: np.sum(x, axis=1) - 1,
lambda x: np.sum(x, axis=0) - 1,
]
#
# demo_func = lambda x: -(x[0] * 4000 + x[1]* 3000)##目标函数是求最小值
# #约束条件小于等于0是满足
# constraint_ueq=[
# lambda x:2*x[0]+x[1]-10,
# lambda x:x[0]+x[1]-8,
# lambda x:x[1]-7,
# # lambda x:-x[0],
# # lambda x:-x[1],
# ]
ga = GA(func=demo_func,
n_dim=(10, 10),
constraint_ueq=constraint_ueq,
max_iter=500,
lb=[0] * 100,
ub=[1] * 100,
precision=[1] * 100)
res = ga.run()
# print('best_x:', best_x, '\n', 'best_y:', best_y)
print(res)
lb = [-10] * n_dim
ub = [10] * n_dim
# lb = [0] * n_dim
# ub = [10] * n_dim
# %% sko GA optimizer
np.random.seed(SEED)
skoga = GA(func=lambda x: -func(x)[0] if func == ode_loss_func else -func(x),
n_dim=n_dim,
size_pop=n_pop,
max_iter=n_iter,
prob_mut=0.05,
lb=lb,
ub=ub,
precision=1e-5)
sko_best_x, sko_best_y = skoga.run()
Y_history = pd.DataFrame(skoga.all_history_Y)
# %% Self defined GA optimizer
np.random.seed(SEED)
ga_opt = GAOPT(
func=func,
n_dim=n_dim,
n_iter=n_iter,
n_pop=n_pop,
pcr=0.9,
pm=0.01,
lb=lb,
ub=ub,
precision=1e-5,
sel="Tournament",
def gaRun(): # 调用遗传算法库得到解
ga = GA(func=demo_func, n_dim=clickNum, size_pop=size_pop, max_iter=max_iter,
lb=[0]*clickNum, ub=[pNum-1]*clickNum, precision=[1]*clickNum)
return ga.run()
from sko.GA import GA
demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
ga = GA(func=demo_func, n_dim=3, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2])
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
import pandas as pd
import matplotlib.pyplot as plt
Y_history = ga.all_history_Y
Y_history = pd.DataFrame(Y_history)
fig, ax = plt.subplots(3, 1)
ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
plt_mean = Y_history.mean(axis=1)
plt_max = Y_history.min(axis=1)
ax[1].plot(plt_mean.index, plt_mean, label='mean')
ax[1].plot(plt_max.index, plt_max, label='min')
ax[1].set_title('mean and all Y of every generation')
ax[1].legend()
ax[2].plot(plt_max.index, plt_max.cummin())
ax[2].set_title('best fitness of every generation')
plt.show()
def _calE():
# ==============
def obj_func1(p):
x1, = p
assert r[zeroP[0] + border, zeroP[1] - border,
0] != NaN, "obj_func1 is NaN"
assert r[zeroP[0] + border + 1, zeroP[1] - border,
1] != NaN, "obj_func1 is NaN"
assert r[zeroP[0] + border, zeroP[1] - border - 1,
1] != NaN, "obj_func1 is NaN"
return np.abs(r[zeroP[0] + border, zeroP[1] - border, 0] -
r[zeroP[0] + border + 1, zeroP[1] - border, 1] - x1 +
r[zeroP[0] + border, zeroP[1] - border - 1, 1])
def obj_func2(p):
x1, x2, x3 = p
assert r[zeroP[0] + border, zeroP[1] - border,
1] != NaN, "obj_func2 is NaN"
return np.abs(-r[zeroP[0] + border, zeroP[1] - border, 1] + x1 +
x2 - x3)
constraint_eq1 = [
lambda x: r[zeroP[0] + border, zeroP[1] - border - 1, 2]**2 - r[
zeroP[0] + border, zeroP[1] - border - 1, 1]**2 - x[0]**2,
lambda x: r[zeroP[0] + border + 1, zeroP[1] - border - 1, 2]**2 -
r[zeroP[0] + border + 1, zeroP[1] - border - 1, 0]**2 - x[1]**2,
lambda x: r[zeroP[0] + border + 1, zeroP[1] - border, 2]**2 - r[
zeroP[0] + border + 1, zeroP[1] - border, 1]**2 - x[2]**2
]
ga = GA(func=obj_func1,
n_dim=1,
size_pop=popsize,
max_iter=maxiter,
lb=0,
ub=r[zeroP[0] + border + 1, zeroP[1] - border - 1, 2] + prec,
precision=prec)
best_x, _ = ga.run()
r[zeroP[0] + border + 1, zeroP[1] - border - 1, 0] = best_x[0]
ga = GA(func=obj_func2,
n_dim=3,
size_pop=popsize,
max_iter=maxiter,
lb=[0, -r[zeroP[0] + border + 1, zeroP[1] - border - 1, 2], 0],
ub=[
r[zeroP[0] + border, zeroP[1] - border - 1, 2] + prec,
r[zeroP[0] + border + 1, zeroP[1] - border - 1, 2] + prec,
r[zeroP[0] + border + 1, zeroP[1] - border, 2] + prec
],
precision=[prec, prec, prec],
constraint_eq=constraint_eq1)
best_x, _ = ga.run()
r[zeroP[0] + border, zeroP[1] - border - 1, 0] = best_x[0]
r[zeroP[0] + border + 1, zeroP[1] - border - 1, 1] = best_x[1]
r[zeroP[0] + border + 1, zeroP[1] - border, 0] = best_x[2]
# ==============
def obj_func3(p):
x1, = p
assert r[zeroP[0] + border, zeroP[1] + border,
1] != NaN, "obj_func3 is NaN"
assert r[zeroP[0] + border, zeroP[1] + border + 1,
0] != NaN, "obj_func3 is NaN"
assert r[zeroP[0] + border + 1, zeroP[1] + border,
0] != NaN, "obj_func3 is NaN"
return np.abs(r[zeroP[0] + border, zeroP[1] + border, 1] +
r[zeroP[0] + border, zeroP[1] + border + 1, 0] - x1 -
r[zeroP[0] + border + 1, zeroP[1] + border, 0])
def obj_func4(p):
x1, x2, x3 = p
assert r[zeroP[0] + border, zeroP[1] + border,
0] != NaN, "obj_func4 is NaN"
return np.abs(r[zeroP[0] + border, zeroP[1] + border, 0] + x1 -
x2 - x3)
constraint_eq2 = [
lambda x: r[zeroP[0] + border + 1, zeroP[1] + border, 2]**2 - r[
zeroP[0] + border + 1, zeroP[1] + border, 0]**2 - x[0]**2,
lambda x: r[zeroP[0] + border + 1, zeroP[1] + border + 1, 2]**2 -
r[zeroP[0] + border + 1, zeroP[1] + border + 1, 1]**2 - x[1]**2,
lambda x: r[zeroP[0] + border, zeroP[1] + border + 1, 2]**2 - r[
zeroP[0] + border, zeroP[1] + border + 1, 0]**2 - x[2]**2
]
ga = GA(func=obj_func3,
n_dim=1,
size_pop=popsize,
max_iter=maxiter,
lb=-r[zeroP[0] + border + 1, zeroP[1] + border + 1, 2],
ub=r[zeroP[0] + border + 1, zeroP[1] + border + 1, 2] + prec,
precision=prec)
best_x, _ = ga.run()
r[zeroP[0] + border + 1, zeroP[1] + border + 1, 1] = best_x[0]
ga = GA(func=obj_func4,
n_dim=3,
size_pop=popsize,
max_iter=maxiter,
lb=[
-r[zeroP[0] + border + 1, zeroP[1] + border, 2], 0,
-r[zeroP[0] + border, zeroP[1] + border + 1, 2]
],
ub=[
r[zeroP[0] + border + 1, zeroP[1] + border, 2] + prec,
r[zeroP[0] + border + 1, zeroP[1] + border + 1, 2] + prec,
r[zeroP[0] + border, zeroP[1] + border + 1, 2] + prec
],
precision=[prec, prec, prec],
constraint_eq=constraint_eq2)
best_x, _ = ga.run()
r[zeroP[0] + border + 1, zeroP[1] + border, 1] = best_x[0]
r[zeroP[0] + border + 1, zeroP[1] + border + 1, 0] = best_x[1]
r[zeroP[0] + border, zeroP[1] + border + 1, 1] = best_x[2]
# ==============
def obj_func5(p):
x1, = p
assert r[zeroP[0] - border, zeroP[1] + border,
0] != NaN, "obj_func5 is NaN"
assert r[zeroP[0] - border - 1, zeroP[1] + border,
1] != NaN, "obj_func5 is NaN"
assert r[zeroP[0] - border, zeroP[1] + border + 1,
1] != NaN, "obj_func5 is NaN"
return np.abs(-r[zeroP[0] - border, zeroP[1] + border, 0] +
r[zeroP[0] - border - 1, zeroP[1] + border, 1] + x1 -
r[zeroP[0] - border, zeroP[1] + border + 1, 1])
def obj_func6(p):
x1, x2, x3 = p
assert r[zeroP[0] - border, zeroP[1] + border,
1] != NaN, "obj_func6 is NaN"
return np.abs(r[zeroP[0] - border, zeroP[1] + border, 1] - x1 -
x2 + x3)
constraint_eq3 = [
lambda x: r[zeroP[0] - border, zeroP[1] + border + 1, 2]**2 - r[
zeroP[0] - border, zeroP[1] + border + 1, 1]**2 - x[0]**2,
lambda x: r[zeroP[0] - border - 1, zeroP[1] + border + 1, 2]**2 -
r[zeroP[0] - border - 1, zeroP[1] + border + 1, 0]**2 - x[1]**2,
lambda x: r[zeroP[0] - border - 1, zeroP[1] + border, 2]**2 - r[
zeroP[0] - border - 1, zeroP[1] + border, 1]**2 - x[2]**2
]
ga = GA(func=obj_func5,
n_dim=1,
size_pop=popsize,
max_iter=maxiter,
lb=0,
ub=r[zeroP[0] + border - 1, zeroP[1] + border + 1, 2] + prec,
precision=prec)
best_x, _ = ga.run()
r[zeroP[0] - border - 1, zeroP[1] + border + 1, 0] = best_x[0]
ga = GA(func=obj_func6,
n_dim=3,
size_pop=popsize,
max_iter=maxiter,
lb=[0, -r[zeroP[0] - border - 1, zeroP[1] + border + 1, 2], 0],
ub=[
r[zeroP[0] - border, zeroP[1] + border + 1, 2] + prec,
r[zeroP[0] - border - 1, zeroP[1] + border + 1, 2] + prec,
r[zeroP[0] - border - 1, zeroP[1] + border, 2] + prec
],
precision=[prec, prec, prec],
constraint_eq=constraint_eq3)
best_x, _ = ga.run()
r[zeroP[0] - border, zeroP[1] + border + 1, 0] = best_x[0]
r[zeroP[0] - border - 1, zeroP[1] + border + 1, 1] = best_x[1]
r[zeroP[0] - border - 1, zeroP[1] + border, 0] = best_x[2]
# ==============
def obj_func7(p):
x1, = p
assert r[zeroP[0] - border, zeroP[1] - border,
1] != NaN, "obj_func7 1 is NaN"
assert r[zeroP[0] - border, zeroP[1] - border - 1,
0] != NaN, "obj_func7 2 is NaN"
assert r[zeroP[0] - border - 1, zeroP[1] - border,
0] != NaN, "obj_func7 3 is NaN"
return np.abs(-r[zeroP[0] - border, zeroP[1] - border, 1] -
r[zeroP[0] - border, zeroP[1] - border - 1, 0] + x1 +
r[zeroP[0] - border - 1, zeroP[1] - border, 0])
def obj_func8(p):
x1, x2, x3 = p
assert r[zeroP[0] - border, zeroP[1] - border,
0] != NaN, "obj_func8 is NaN"
return np.abs(-r[zeroP[0] - border, zeroP[1] - border, 0] - x1 +
x2 + x3)
constraint_eq4 = [
lambda x: r[zeroP[0] - border - 1, zeroP[1] - border, 2]**2 - r[
zeroP[0] - border - 1, zeroP[1] - border, 0]**2 - x[0]**2,
lambda x: r[zeroP[0] - border - 1, zeroP[1] - border - 1, 2]**2 -
r[zeroP[0] - border - 1, zeroP[1] - border - 1, 1]**2 - x[1]**2,
lambda x: r[zeroP[0] - border, zeroP[1] - border - 1, 2]**2 - r[
zeroP[0] - border, zeroP[1] - border - 1, 0]**2 - x[2]**2
]
ga = GA(func=obj_func7,
n_dim=1,
size_pop=popsize,
max_iter=maxiter,
lb=-r[zeroP[0] - border - 1, zeroP[1] - border - 1, 2],
ub=r[zeroP[0] - border - 1, zeroP[1] - border - 1, 2] + prec,
precision=prec)
best_x, _ = ga.run()
r[zeroP[0] - border - 1, zeroP[1] - border - 1, 1] = best_x[0]
ga = GA(func=obj_func8,
n_dim=3,
size_pop=popsize,
max_iter=maxiter,
lb=[
-r[zeroP[0] - border - 1, zeroP[1] - border, 2], 0,
-r[zeroP[0] - border, zeroP[1] - border - 1, 2]
],
ub=[
r[zeroP[0] - border - 1, zeroP[1] - border, 2] + prec,
r[zeroP[0] - border - 1, zeroP[1] - border - 1, 2] + prec,
r[zeroP[0] - border, zeroP[1] - border - 1, 2] + prec
],
precision=[prec, prec, prec],
constraint_eq=constraint_eq4)
best_x, _ = ga.run()
r[zeroP[0] - border - 1, zeroP[1] - border, 1] = best_x[0]
r[zeroP[0] - border - 1, zeroP[1] - border - 1, 0] = best_x[1]
r[zeroP[0] - border, zeroP[1] - border - 1, 1] = best_x[2]
return