[英]Edge classification using DFS
我使用networkx創建了一個圖形,我想在每個邊緣添加一個“類”屬性,以了解是向前,向后,交叉還是樹。 下一步,我嘗試在python中實現以下偽代碼,但是當我打印圖形邊緣時,輸出為每個邊緣都具有空的'class'屬性。
DFS (V, E)
1. for each vertex u in V[G]
2. do color[u] ← WHITE
3. π[u] ← NIL
4. time ← 0
5. for each vertex u in V[G]
6. do if color[u] ← WHITE
7. then DFS-Visit(u) ▷ build a new DFS-tree from u
DFS-Visit(u) ▷ with edge classification. G must be a directed graph
1. color[u] ← GRAY
2. time ← time + 1
3. d[u] ← time
4. for each vertex v adjacent to u
5. do if color[v] ← BLACK
6. then if d[u] < d[v]
7. then Classify (u, v) as a forward edge
8. else Classify (u, v) as a cross edge
9. if color[v] ← GRAY
10. then Classify (u, v) as a back edge
11. if color[v] ← WHITE
12. then π[v] ← u
13. Classify (u, v) as a tree edge
14. DFS-Visit(v)
15. color[u] ← BLACK
16. time ← time + 1
17. f[u] ← time
這是我的代碼:
def dfs_visit(u):
color[u] = 'gray'
global time
time = time + 1
d[u] = time
for v in G.neighbors(u):
if color[v] == 'black':
if d[u] < d[v]:
G.edges[u,v]['class'] = 'forward'
else:
G.edges[u,v]['class'] ='cross'
if color[v] == 'gray':
G.edges[u, v]['class'] = 'back'
if color[v] == 'white':
p[v] = u
G.edges[u, v]['class'] = 'tree'
dfs_visit(v)
color[u] = 'black'
time = time + 1
f[u] = time
for u in G.nodes():
color[u] = 'white'
time = 0
for u in G.nodes():
if color[u] == 'white':
dfs_visit(u)
print(G.edges(data=True))
如果我打印所有圖形邊緣,則將獲得以下輸出:
[(1, 2, {'class': ''}), (1, 6, {'class': ''}), (2, 3, {'class': ''}), (6, 3, {'class': ''}), (3, 4, {'class': ''}), (3, 1, {'class': 'cross'}), (3, 7, {'class': ''}), (4, 5, {'class': ''}), (4, 7, {'class': ''})]
每個邊的class屬性為空。 我該如何解決?
可能是縮進問題,請嘗試以下操作:
def dfs_visit(u):
color[u] = 'gray'
global time
time = time + 1
d[u] = time
for v in G.neighbors(u):
if color[v] == 'black':
if d[u] < d[v]:
G.edges[u,v]['class'] = 'forward'
else:
G.edges[u,v]['class'] ='cross'
if color[v] == 'gray':
G.edges[u, v]['class'] = 'back'
if color[v] == 'white':
p[v] = u
G.edges[u, v]['class'] = 'tree'
dfs_visit(v)
color[u] = 'black'
time = time + 1
f[u] = time
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.