参数在Python中使用最小化优化

Question

我编写了以下两个函数来校准模型:

主要功能是:

def function_Price(para,y,t,T,tau,N,C):  
# y= price array
# C = Auto and cross correlation array
# a= paramters need to be calibrated
a=para[0:]
temp=0
for j in range(N):
price_j = a[j]*C[j]*P[t:T-tau,j]
temp=temp+price_j
Price=temp
return Price 

目标函数为

def GError_function_Price(para,y,k,t,T,tau,N,C):
# k is the price need to be fitted
return sum((function_Price(para,y,t,T,tau,N,C)-k[t+tau:T]) ** 2)

现在，我调用这两个函数来对模型进行优化:

import numpy as np
from scipy.optimize import minimize

# Prices (example)
y = np.array([[1,2,3,4,5,4], [4,5,6,7,8,9], [6,7,8,7,8,6], [13,14,15,11,12,19]])
# Correaltion (example)
Corr= np.array([[1,2,3,4,5,4], [4,5,6,7,8,9], [6,7,8,7,8,6], [13,14,15,11,12,19],[1,2,3,4,5,4],[6,7,8,7,8,6]])
# Define 
tau=1
Size = y.shape
N = Size[1]
T = Size[0]
t=0
# initial Values
para=np.zeros(N) 
# Bounds
B = np.zeros(shape=(N,2)) 
for n in range(N):
B[n][0]= float('-inf')
B[n][1]= float('inf')
# Calibration 
A = np.zeros(shape=(N,N))  
for i in range (N):
k=y[:,i] #fitted one
C=Corr[i,:]
parag=minimize(GError_function_Price,para,args=(y,Y,t,T,tau,N,C),method='SLSQP',bounds=B)
A[i,:]=parag.x

一旦我运行模型，它应该产生一个N × N的优化参数值数组。但是，除了第一列，其余的都是零。不对劲。

你能帮我解决这个问题吗?

我知道如何在Matlab中实现它.

下面是Matlab代码:主要功能>

function gerr=GError_function_Price(para,P,Y,t,T,tau,N,C)
gerr=sum((function_Price(para,P,t,T,tau,N,C)-Y(t+tau:T)).^2);
end

现在，我以以下方式调用这两个函数:

P = [1,2,3,4,5,4;4,5,6,7,8,9;6,7,8,7,8,6;13,14,15,11,12,19];
AutoAndCrossCorr= [1,2,3,4,5,4;4,5,6,7,8,9;6,7,8,7,8,6;13,14,15,11,12,19;1,2,3,4,5,4;6,7,8,7,8,6];
tau=1;
Size = size(P);
N =6;
T =4;
t=1;   
for i=1:N
Y=P(:,i); % fitted one
C=AutoAndCrossCorr(i,:);
para=zeros(1,N);
lb= repmat(-inf,N,1);
ub= repmat(inf ,N,1);
parag=fminsearchbnd(@(para)abs(GError_function_Price(para,P,Y,t,T,tau,N,C)),para,lb,ub);
a(i,:)=parag;
end

Answer 1

问题似乎是您将函数调用的结果传递给最小化，而不是函数本身。实参通过args形参传递。所以不是:

minimize(GError_function_Price(para,y,k,t,T,tau,N,C),para,method='SLSQP',bounds=B)

minimize(GError_function_Price,para,args=(y,k,t,T,tau,N,C),method='SLSQP',bounds=B)