我不知道如何从优化中发送数组

Question

我正在构建一个用于智能计算的Python应用程序。 该应用程序是一个模型谓词控制器(MPC(，我正在使用scipy.optimize.minimin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，其中执行了我的系统的模拟。 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

我挣扎的是如何取回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，但我没有让它工作，也不认为使用全局变量的良好编码方式。有没有人知道解决我问题的好方法？也许使用回调函数？(如果回调是要走的路，我不完全了解此方法的工作原理或如何以良好的方式实现它(

Answer 1

你为什么不做以下事情呢？

修改你的目标函数，如下所示

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

基于您对我的第一个答案的评论并重用我另一个答案中的一些代码(这本身是通过修改@Scott(Scott Sievert(答案并使用他的drawnow Github包编写的(

备注：

我没有安装drawnow Github软件包。相反，我只是 drawnow.py 复制到我的文件夹中。(这是因为我没有找到任何通过 conda 安装它的方法。我不想使用PyPi(

按如下方式修改代码

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