我不知道如何从optimization.minimize函数中发送数组

Question

我正在构建一个用于科学计算的Python应用程序。该应用程序是模型预测控制器（MPC），我正在使用scipy.optimize.minimize函数作为优化算法。

solution_guess = minimize(objectiveFunction,
                                  U_guess,
                                  arg_guess,
                                  callback= None,
                                  method = "SLSQP")

其中目标函数是一个自制函数，其中将执行我的系统的仿真。它看起来像这样：

def objectiveFunction(x,*arg):
    U_test = x
    dt_test = arg[0]
    setpoint_test = arg[1]
    pred_horizion_length_test = arg[2]
    initStateValue_test = arg[3]
    # Defining Model Arrays
    NS_pred_horizion_test = int(pred_horizion_length_test/dt_test)+1
    pred_horizion_array_test = np.linspace(0, pred_horizion_length_test, NS_pred_horizion_test)
    SP_array_test = np.zeros(NS_pred_horizion_test) + setpoint_test
    Y_array_test = SP_array_test * 0

    # Defining parameters for the testing model
    timeDelay_test = 50
    initDelayValue_test = 0
    K_test = 4
    Tc1_test = 30
    Tc2_test = 60

    # Defining Model Object
    obj_model_test = model.secDegModel(dt = dt_test,
                                      K = K_test,
                                      Tc1 = Tc1_test,
                                      Tc2 = Tc2_test,
                                      timeDelay = timeDelay_test,
                                      initStateValue = initStateValue_test,
                                      initDelayValue = initDelayValue_test
                                      )


    ###########################################
    #|||||||||||||||||||||||||||||||||||||||||#
    #     Testing Values for U on Model       #
    #|||||||||||||||||||||||||||||||||||||||||#
    ###########################################


    # Running simulation of "real" model function
    for k in range(NS_pred_horizion_test):
        Y_array_test[k] = obj_model_test.run(u_k = U_test) 

    error = np.sum(abs(SP_array_test-Y_array_test))
    return error

我不为之烦恼的是如何取回Y_array_test数组，以便每次优化完成后都可以绘制它。我尝试使用全局变量，但没有使它起作用，也没有想到使用全局变量的良好编码方式。有谁知道解决我的问题的好方法吗？也许使用回调函数？ （如果要使用回调，则我不完全了解此方法的工作方式或如何以不错的方式实现它）

Answer 1

你为什么不只做以下事情？

如下修改您的objectiveFunction

from numpy import savez

def objectiveFunction(x,*arg):
.
.
.
.
.

# Running simulation of "real" model function
for k in range(NS_pred_horizion_test):
    Y_array_test[k] = obj_model_test.run(u_k = U_test) 
# Just save Y_array_test in a file
# Add some call_no if you don't want to overwrite
# then filename would be 'Y_array_test_' + str(call_no) 
# You could increment this call_no, every time by 
# call_no = call_no + 1
savez(file_name, Y_array_test)
# Then you could plot it outside using matplotlib
error = np.sum(abs(SP_array_test-Y_array_test))
return error

Answer 2

根据您对my first answer的评论并重用my other answer中的一些代码（这本身是通过修改@Scott（Scott Sievert）answer并使用他的drawnow Github package编写的]

提示：

我没有安装drawnow Github package。相反，我只是将drawdow.py复制到了我的文件夹中。 （这是因为我找不到通过conda安装它的任何方法。我不想使用PyPi）

如下修改您的代码

from numpy.random import random_sample
from numpy import arange, zeros

from drawnow import drawnow
from matplotlib import use
from matplotlib.pyplot import figure, axes, ion
from matplotlib import rcParams
from matplotlib.pyplot import style
from matplotlib.pyplot import cla, close
use("TkAgg")
pgf_with_rc_fonts = {"pgf.texsystem": "pdflatex"}
rcParams.update(pgf_with_rc_fonts)
style.use('seaborn-whitegrid')

scott_fig = figure()  # call here instead!
ion()
# figure()  # call here instead!
# ion()    # enable interactivity



solution_guess = minimize(objectiveFunction,
                          U_guess,
                          arg_guess,
                          callback= None,
                          method = "SLSQP")





def objectiveFunction(x,*arg):
    .
    .
    .
    .
    .
    def draw_fig():
        # can be arbitrarily complex; just to draw a figure
        # figure() # don't call!
        scott_ax = axes()
        scott_ax.plot(x, y, '-g', label='label')
        # Need to add some pause element here
        # otherwise you won't be able to see the figure as  
        # it will change too fast
        # cla()
        # close(scott_fig)
        # show() # don't call!

    # Running simulation of "real" model function
    for k in range(NS_pred_horizion_test):
        Y_array_test[k] = obj_model_test.run(u_k = U_test)

    # Just plot Y_array_test now that you have updated it


    drawnow(draw_fig)
    # Then you could plot it outside using matplotlib
    error = np.sum(abs(SP_array_test-Y_array_test))
    return error