Python中的Hopcroft–Karp算法
我正在尝试使用networkx作为图形表示形式在Python中实现Hopcroft
Karp算法。
目前我对此:
#Algorithms for bipartite graphsimport networkx as nx
import collections
class HopcroftKarp(object):
INFINITY = -1
def __init__(self, G):
self.G = G
def match(self):
self.N1, self.N2 = self.partition()
self.pair = {}
self.dist = {}
self.q = collections.deque()
#init
for v in self.G:
self.pair[v] = None
self.dist[v] = HopcroftKarp.INFINITY
matching = 0
while self.bfs():
for v in self.N1:
if self.pair[v] and self.dfs(v):
matching = matching + 1
return matching
def dfs(self, v):
if v != None:
for u in self.G.neighbors_iter(v):
if self.dist[ self.pair[u] ] == self.dist[v] + 1 and self.dfs(self.pair[u]):
self.pair[u] = v
self.pair[v] = u
return True
self.dist[v] = HopcroftKarp.INFINITY
return False
return True
def bfs(self):
for v in self.N1:
if self.pair[v] == None:
self.dist[v] = 0
self.q.append(v)
else:
self.dist[v] = HopcroftKarp.INFINITY
self.dist[None] = HopcroftKarp.INFINITY
while len(self.q) > 0:
v = self.q.pop()
if v != None:
for u in self.G.neighbors_iter(v):
if self.dist[ self.pair[u] ] == HopcroftKarp.INFINITY:
self.dist[ self.pair[u] ] = self.dist[v] + 1
self.q.append(self.pair[u])
return self.dist[None] != HopcroftKarp.INFINITY
def partition(self):
return nx.bipartite_sets(self.G)
该算法取自http://en.wikipedia.org/wiki/Hopcroft%E2%80%93Karp_algorithm,
但是它不起作用。我使用以下测试代码
G = nx.Graph([(1,"a"), (1,"c"),
(2,"a"), (2,"b"),
(3,"a"), (3,"c"),
(4,"d"), (4,"e"),(4,"f"),(4,"g"),
(5,"b"), (5,"c"),
(6,"c"), (6,"d")
])
matching = HopcroftKarp(G).match()
print matching
不幸的是,这不起作用,我陷入了一个无休止的循环:(。有人可以发现错误,我没有主意,我必须承认我还没有完全理解算法,所以它主要是伪算法的实现。维基百科上的代码
回答:
线
if self.pair[v] and self.dfs(v):
应该
if self.pair[v] is None and self.dfs(v):
按照Wikipedia页面上的伪代码。我看到的唯一另一个问题是,您将双端队列用作堆栈,并且希望将其用作队列。为了解决这个问题,您只需要向左弹出而不是弹出(向右弹出)即可。所以线
v = self.q.pop()
应该
v = self.q.popleft()
希望其他所有东西都可以。我只是在检查您的Python代码是否与Wikipedia上的伪代码相同,因此希望该伪代码是正确的。
以上是 Python中的Hopcroft–Karp算法 的全部内容, 来源链接: utcz.com/qa/400691.html