我的目标是找到使 L 最大化的向量 x 和 y 的值。K_i^out 和 K_i^in 是图中节点 i 的入度和出度G
,它们基本上是从 0 到 100 的整数。我读到该minimize
函数对此很有用,因此我编写了以下代码:
import networkx as nx
import scipy as sp
import numpy as np
from scipy.optimize import minimize
def f(z, n):
frst_term = 0 # first term in B(43)
scnd_term = 0 # second term in B(43)
for i in range(n): # n is the amount of nodes in Graph G.
frst_term += -G.out_degree(i)*np.log(z[i]) + -G.in_degree(i)*np.log(z[n+i])
#description of first term where z[i] is x_i and z[n+i] = y_i
for j in range(n):
if i == j:
None
else:
scnd_term += np.log(1+z[i]*z[n+j]) #z[i] = x_i z[n+j] = y_j
lik = (frst_term - scnd_term) #the total function
return(lik)
w = 2*n*[0.5] #my first guess
max_val = minimize(f, w, args=(n))
print(max_val)
从这里我得到运行时警告
invalid value encountered in log
和
invalid value encountered in reduce
return umr_maximum(a, axis, None, out, keepdims)
x 和 y 值应该都是正数,最大值在 0 到 10 之间,但大多在 0 到 1 之间。总结一下:你们中的任何人对如何改进此代码或解决此问题的任何其他方法有什么建议吗?