[英]GEKKO TypeError in python
suppose:认为:
# ww is a numpy array
ww.shape
>>>(10, 1)
# C is a numpy array
C.shape
>>>(5, 10)
i want to solve a optimization problem in python with specific objective function.我想用特定的目标 function 解决 python 中的优化问题。
Here is the code that i wrote for that purpose:这是我为此目的编写的代码:
from gekko import GEKKO
m = GEKKO()
x1 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x2 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x3 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x4 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x5 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x = [x1, x2, x3, x4, x5]
# My subjective function
m.Equation(x1 + x2 + x3 + x4 + x5 == 1)
# My specific Objective Function
## Remember that I specified about ww and C arrays right upside of these Codes
def Objective(x):
i = 0
j = 0
C_b = np.zeros((1,C.shape[1])) # so C_b.shape would be (1, 10)
for i in range(C.shape[1]):
for j in range(5):
C_b[0][i] += math.log10(x[j] * C[j,i])
return -sum((C_b * ww)[0])
m.Obj(Objective(x))
m.solve(disp=False)
print(x1.value, x2.value, x3.value, x4.value, x5.value)
Output: Output:
TypeError: must be real number, not GK_Operators
Picture of Error:错误图片:
i guess this error is cause of specific objective function!我猜这个错误是特定目标函数的原因! because with simple objective functions like:
因为具有简单的目标函数,例如:
m.Obj(x1 + x2)
I don't get error.我没有收到错误。 so I guess the error comes from specific objective function.
所以我猜错误来自特定的目标函数。
How can I fix this error?我该如何解决这个错误? where is the problem?
问题出在哪里?
This should work for you.这应该适合你。
from gekko import GEKKO
import numpy as np
nd = 5; md = 10
ww = np.random.rand(md)
C = np.random.rand(nd,md)
m = GEKKO()
x = m.Array(m.Var,nd,value=1/nd,lb=0,ub=1)
m.Equation(sum(x)==1)
for i in range(C.shape[1]):
for j in range(C.shape[0]):
m.Maximize(ww[i]*(m.log10(x[j]*C[j,i])))
m.solve(disp=True)
for i,xi in enumerate(x):
print(i+1,xi.value)
The solution is always 1/nd
that is also the same as the initial guess.解始终是
1/nd
,这也与初始猜测相同。 You can check that the solver converges to this optimal solution (not just stops at the initial guess) by setting the initial guess to something like 1
.您可以通过将初始猜测设置为类似
1
来检查求解器是否收敛到此最优解(不仅仅是在初始猜测处停止)。
The Error Fixed by changing the shape of ww
.通过更改
ww
的形状修复了错误。
before fixing problem:在解决问题之前:
ww.shape
>>>(10, 1)
fixed The problem with:修复了以下问题:
ww.shape
>>>(10, )
Now proposed algorithm worked without any kind of error or problem.现在提出的算法没有任何错误或问题。 That mean it was cause of shape of
ww
, it fixed after I changed the shape of ww to (10, ) instead (10. 1) .这意味着它是
ww
形状的原因,在我将 ww 的形状更改为 (10, ) 而不是 (10. 1) 后它已修复。
now Suppose:现在假设:
# ww is a numpy array
ww.shape
>>>(10, )
# C is a numpy array
C.shape
>>>(5, 10)
Corrected & Proposed Algorithm:更正和建议的算法:
from gekko import GEKKO
import numpy as np
nd = 5
m = GEKKO()
x = m.Array(m.Var,nd,value=1/nd,lb=0,ub=1)
m.Equation(sum(x)==1)
i = 0
j = 0
for i in range(C.shape[1]):
for j in range(C.shape[0]):
m.Maximize(ww[i]*(m.log10(x[j] *C[j,i])))
m.solve(disp=True)
for i,xi in enumerate(x):
print(i+1,xi.value)
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.