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
return step3
return costly_function
for task_type in ('io_costly', 'cpu_costly'):
costly_function = generate_costly_function(task_type=task_type)
def obj_func(p):
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()))
mask_out_path = os.path.join(r'/tmp/pycharm_project_836/img_out', mask_out_path)
cv2.imwrite(mask_out_path, mask_out)
# iou
act_path = 'rbg_1.jpg'
iou, pa = eval(mask_out_path, act_path)
return -iou # 最优化取最小值！！取负数！！
if __name__ == '__main__':
# iou=find_path_func(20,0.3,0.2,0.2)
# print(iou)
set_run_mode(find_path_func, 'multiprocessing')
print("pao1")
pso = PSO(func=find_path_func, n_dim=4, pop=15, max_iter=30, lb=[0.1, 0.1, 0.1, 0.1], ub=[0.28, 0.8, 0.8, 0.8],
w=0.8, c1=0.5, c2=0.5)
print("pao2")
pso.record_mode = True
print("pao3")
#5
print("pao4")
pso.run(5)
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)
plt.plot(pso.gbest_y_hist)
plt.savefig('5.png')
record_dict = pso.record_value
f = open('5.txt', 'w')
def obj_func4_1(self, p):
time.sleep(0.1) # say that this function is very complicated and cost 0.1 seconds to run
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)
def obj_func4_2(self, p):
time.sleep(0.1) # say that this function is very complicated and cost 0.1 seconds to run
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()
print('common mode, time costs: ',
(datetime.datetime.now() - start_time).total_seconds())
# %% type2:矢量化
def obj_func2(p):
time.sleep(
0.1
) # say that this function is very complicated and cost 1 seconds to run
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)
set_run_mode(obj_func2, 'vectorization')
ga2 = GA(func=obj_func2,
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 = ga2.run()
print('vector mode, time costs: ',
(datetime.datetime.now() - start_time).total_seconds())
# %% type3：parallel
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)
def obj_func4_2(self, p):
time.sleep(
0.1
) # say that this function is very complicated and cost 0.1 seconds to run
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, 'multithreading')
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],