我定义的计算目标函数的函数为,def quadratic(bd_X, bd_Y, x3, x4): x1 = 0.25*(((DX*(bd_X-1))**2 + (DY*(bd_Y-1))**2)**0.5+ ((DX*(51-bd_X))**2 + (DY*(bd_Y-1))**2)**0.5 + ((DX*(bd_X-1))**2 + (DY*(51-bd_Y))**2)**0.5 + ((DX*(51-bd_X))**2 + (DY*(51-bd_Y))**2)**0.5) x2 = (((bd_X-mbjx)**2 + (bd_Y-mbjy)**2 )**0.5)*DX x5 = train_optimize2[4] x6 = train_optimize2[5] x7 = train_optimize2[6] x8 = train_optimize2[7] x9 = train_optimize2[8] x10 = train_optimize2[9] x11 = train_optimize2[10] x12 = train_optimize2[11] x13 = train_optimize2[12] x14 = train_optimize2[13] x15 = train_optimize2[14] x16 = train_optimize2[15] x17 = train_optimize2[16] x18 = train_optimize2[17] x19 = train_optimize2[18] with open('regressor_model.pkl', 'rb') as f: model = pickle.load(f) x_train = np.array([[x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14,x15,x16,x17,x18,x19]]) y_predict = model.predict(x_train) Y = y_predict return -Y
时间: 2024-03-17 10:47:27 浏览: 101
quadraticprograming.rar_quadratic_quadraticprogrami_quadraticpro
好的,根据您给出的代码,可以看出目标函数的计算涉及到一些外部的变量和模型,这里简单介绍一下如何将其与 CMA-ES 优化器结合起来。
首先,我们需要将 `quadratic()` 函数转换为只接受一个参数(即解向量),并返回一个标量值(即目标函数值)的形式。其次,由于 `quadratic()` 函数的计算需要使用一些外部的变量和模型,我们可以使用 Python 的函数闭包(closure)特性来包装 `quadratic()` 函数,以便在闭包内访问这些外部变量和模型。
下面是一个示例代码,其中假设您已经定义好了 `DX`、`DY`、`train_optimize2`、`mbjx` 和 `mbjy` 等变量:
```python
import numpy as np
import cma
import pickle
# 定义目标函数
def make_objective(DX, DY, train_optimize2, mbjx, mbjy):
def objective(x):
bd_X, bd_Y, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19 = x
x1 = 0.25*(((DX*(bd_X-1))**2 + (DY*(bd_Y-1))**2)**0.5+ ((DX*(51-bd_X))**2 + (DY*(bd_Y-1))**2)**0.5 + ((DX*(bd_X-1))**2 + (DY*(51-bd_Y))**2)**0.5 + ((DX*(51-bd_X))**2 + (DY*(51-bd_Y))**2)**0.5)
x2 = (((bd_X-mbjx)**2 + (bd_Y-mbjy)**2 )**0.5)*DX
with open('regressor_model.pkl', 'rb') as f:
model = pickle.load(f)
x_train = np.array([[x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19]])
y_predict = model.predict(x_train)
Y = y_predict
return -Y
return objective
# 定义变量边界
bounds = [[1, 51], [1, 51], [-5, 5], [-5, 5], -np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]
# 初始化 CMA-ES 优化器
objective = make_objective(DX, DY, train_optimize2, mbjx, mbjy)
x0 = np.mean(bounds, axis=1)
sigma0 = 0.5
optimizer = cma.CMAEvolutionStrategy(x0, sigma0, {'bounds': bounds})
# 运行优化器
while not optimizer.stop():
solutions = optimizer.ask() # 获取一组解向量
fitness_list = [objective(x) for x in solutions] # 计算目标函数值
optimizer.tell(solutions, fitness_list) # 更新优化器内部状态
best_solution = solutions[np.argmin(fitness_list)] # 获取当前最优解
best_fitness = np.min(fitness_list) # 获取当前最优解的目标函数值
print(f'Best solution: {best_solution}, Best fitness: {best_fitness}')
```
在上面的代码中,我们定义了一个 `make_objective()` 函数,用于创建一个闭包,其中包含 `quadratic()` 函数及其依赖的外部变量和模型。然后,我们使用 `make_objective()` 函数创建一个目标函数 `objective`,并将其传递给 CMA-ES 优化器进行优化。在每一轮迭代中,我们打印当前最优解和最优解的目标函数值,以便进行跟踪和分析。
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