请在上述提供的几段代码的基础上，继续写代码，求出最优解和最优解向量

好的，接下来我给出完整代码来求解最优解和最优解向量：

import numpy as np
import pickle
from cma import CMAEvolutionStrategy as CMA

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

def main():
    # X坐标束条件
    Min_pump_zcjj = 26
    Max_pump_zcjj = 51

    # Y坐标约束条件
    Min_pump_bdljd = 26
    Max_pump_bdljd = 51

    # 靶点位置约束条件
    Min_pump_bdwz = 0
    Max_pump_bdwz = 1

    # 射孔厚度约束条件
    Min_pump_skhd = 1
    Max_pump_skhd = 5

    bounds = np.array([[Min_pump_zcjj, Max_pump_zcjj], [Min_pump_bdljd, Max_pump_bdljd],[Min_pump_bdwz, Max_pump_bdwz],[Min_pump_skhd, Max_pump_skhd]])
    target = -(train_optimize2[19])

    # 定义你的优化时间限制，输入是一个数值，表示你想要优化的最长时间（秒）
    timeout = 30

    optimizer = CMA(mean=np.mean(bounds, axis=1), sigma=1, bounds=bounds, seed=0)

    while True:
        solutions = optimizer.ask()
        fitness_list = []
        for s in solutions:
            fitness_list.append(quadratic(*s))
        optimizer.tell(solutions, fitness_list)
        best_index = np.argmin(fitness_list)
        best_fit = fitness_list[best_index]
        best_sol = solutions[best_index]
        if -best_fit < target or optimizer.countiter >= timeout:
            break

    return best_sol, -best_fit

if __name__ == '__main__':
    train_optimize2 = [26.0, 26.0, 0.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, -0.735]
    DX = 0.5
    DY = 0.5
    mbjx = 30.5
    mbjy = 30.5
    best_sol, best_fit = main()
    print("最优解向量为：", best_sol)
    print("最优解为：", best_fit)

在这段代码中，首先我们导入了必要的库，然后定义了目标函数quadratic()和优化函数main()，其中目标函数的参数和实现与之前一致，而main()函数则是将目标函数与优化算法结合起来，求出最优解和最优解向量。在main()函数中，我们首先定义了X坐标、Y坐标、靶点位置和射孔厚度的约束条件，并将它们组成一个二维数组bounds；然后定义了目标函数的值target和优化时间限制timeout；最后创建了一个CMA对象optimizer，并在while循环中进行迭代，直到找到满足目标函数要求或达到时间限制为止。在每次迭代中，我们通过调用ask()方法获取一组新的解，并调用目标函数计算它们的适应度，然后将解和适应度传递给tell()方法，让优化算法进行更新。在每次迭代结束后，我们通过调用np.argmin()方法找到当前最优解的索引，然后保存最优解和最优解向量，并检查是否满足终止条件。最后，我们在if语句中返回最优解向量和最优解的负数值。最后，在代码的最后一行，我们输出最优解向量和最优解的负数值。