Question

我对这段代码有疑问，我真的不明白为什么它不能达到以下最内部的第二个周期：

def remove_random_links(A,E):
    rs = random.sample(A.edges, E)
    A.remove_edges_from(rs)

N = 30
L = [0,60,30,40,30,30,20,40]
rep = 15
d = []
g = []

for j in range(rep):
    R = nx.grid_graph(dim=[N,N])  
    _deg = []
    _gap = [] 
    for i in L:
        remove_random_links(R,i)
        A = nx.adj_matrix(R)
        L = nx.laplacian_matrix(R) 
        eigwd, eigv = LA.eigh(L.todense())
        eigw = np.sort(eigwd)
        eigw0 = eigw[0]
        eigw1 = eigw[1]
        eigw2 = eigw[2]
        eigw3 = eigw[3]
        gap = eigw2 - eigw1
        deg = eigw3 - eigw2
        _deg.append(deg)
        _gap.append(gap)
        print("meow")    
    d.append(_deg)
    g.append(_gap)
    print("M")

mean_deg = [np.mean(d[i]) for i in range(rep)]    
mean_gap = [np.mean(g[i]) for i in range(rep)]
std_deg = [np.std(d[i]) for i in range(rep)]
std_gap = [np.std(g[i]) for i in range(rep)]

它先执行第一个循环（j中的那个），然后进入第一个i循环，完成它，然后由于功能remove_random_links（）的错误而无法执行第二个，这给了我以下错误：

文件“ /Users/Sofia/Desktop/delta.py”，第23行，在         remove_random_links（R，i）


文件“ /Users/Sofia/Desktop/delta.py”，第9行，在remove_random_links中       rs = random.sample（A.edges，E）

示例中的文件“ /anaconda3/lib/python3.6/random.py”，第317行       如果不是0 <= k <= n：

文件   “ /anaconda3/lib/python3.6/site-packages/scipy/sparse/compressed.py”，   第322行，位于 ge       “将稀疏矩阵与标量进行比较”

文件   “ /anaconda3/lib/python3.6/site-packages/scipy/sparse/compressed.py”，   _inequality中的第272行       引发NotImplementedError（“> =和<=不适用于0。”）

NotImplementedError：> =和<=不适用于0。

Answer 1

这是因为您两次在同一个变量L中使用过一次，

L = [0,60,30,40,30,30,20,40]

然后在此处进行迭代时重新分配它

for i in L:
        remove_random_links(R,i)
        A = nx.adj_matrix(R)
        L = nx.laplacian_matrix(R)   #<--------change variable name

使用不同的变量名，这应该可以解决问题，例如，尝试一下

import random
from numpy import linalg as LA
def remove_random_links(A,E):
    print A.edges, E
    rs = random.sample(A.edges, E)
    A.remove_edges_from(rs)

N = 30
L_edges = [0,60,30,40,30,30,20,40]    #<--- changed it to L_edges
rep = 15
d = []
g = []

for j in range(rep):
    R = nx.grid_graph(dim=[N,N])  
    _deg = []
    _gap = [] 
    for i in L_edges:     #<--- changed it to L_edges
        remove_random_links(R,i)
        A = nx.adj_matrix(R)
        L = nx.laplacian_matrix(R) 
        eigwd, eigv = LA.eigh(L.todense())
        eigw = np.sort(eigwd)
        eigw0 = eigw[0]
        eigw1 = eigw[1]
        eigw2 = eigw[2]
        eigw3 = eigw[3]
        gap = eigw2 - eigw1
        deg = eigw3 - eigw2
        _deg.append(deg)
        _gap.append(gap)
        print("meow")    
    d.append(_deg)
    g.append(_gap)
    print("M")

mean_deg = [np.mean(d[i]) for i in range(rep)]    
mean_gap = [np.mean(g[i]) for i in range(rep)]
std_deg = [np.std(d[i]) for i in range(rep)]
std_gap = [np.std(g[i]) for i in range(rep)]

random.sample错误：引发NotImplementedError（“> =和<=不适用于0。”）NotImplementedError：> =和<=不适用于0

1 个答案: