我想修改Johnson算法的networkx实现(https://networkx.github.io/documentation/networkx-1.10/_modules/networkx/algorithms/cycles.html#simple_cycles),以查找图中的所有基本周期(也在下面复制)并将其打印在文本文件中。我想做这个修改,因为我使用大图,我得到一个内存错误,因为保存周期的列表是巨大的。
def simple_cycles(G):
def _unblock(thisnode,blocked,B):
stack=set([thisnode])
while stack:
node=stack.pop()
if node in blocked:
blocked.remove(node)
stack.update(B[node])
B[node].clear()
# Johnson's algorithm requires some ordering of the nodes.
# We assign the arbitrary ordering given by the strongly connected comps
# There is no need to track the ordering as each node removed as processed.
subG = type(G)(G.edges_iter()) # save the actual graph so we can mutate it here
# We only take the edges because we do not want to
# copy edge and node attributes here.
sccs = list(nx.strongly_connected_components(subG))
while sccs:
scc=sccs.pop()
# order of scc determines ordering of nodes
startnode = scc.pop()
# Processing node runs "circuit" routine from recursive version
path=[startnode]
blocked = set() # vertex: blocked from search?
closed = set() # nodes involved in a cycle
blocked.add(startnode)
B=defaultdict(set) # graph portions that yield no elementary circuit
stack=[ (startnode,list(subG[startnode])) ] # subG gives component nbrs
while stack:
thisnode,nbrs = stack[-1]
if nbrs:
nextnode = nbrs.pop()
# print thisnode,nbrs,":",nextnode,blocked,B,path,stack,startnode
# f=raw_input("pause")
if nextnode == startnode:
yield path[:]
closed.update(path)
# print "Found a cycle",path,closed
elif nextnode not in blocked:
path.append(nextnode)
stack.append( (nextnode,list(subG[nextnode])) )
closed.discard(nextnode)
blocked.add(nextnode)
continue
# done with nextnode... look for more neighbors
if not nbrs: # no more nbrs
if thisnode in closed:
_unblock(thisnode,blocked,B)
else:
for nbr in subG[thisnode]:
if thisnode not in B[nbr]:
B[nbr].add(thisnode)
stack.pop()
# assert path[-1]==thisnode
path.pop()
# done processing this node
subG.remove_node(startnode)
H=subG.subgraph(scc) # make smaller to avoid work in SCC routine
sccs.extend(list(nx.strongly_connected_components(H)))
当然,我也接受一个与上述实施不同但在相似时间内运行的建议。此外,我的项目使用networkx,因此可以随意使用该库中的任何其他功能
答案 0 :(得分:1)
networkx.simple_cycles()函数已经是一个生成器。你可以迭代循环并将它们打印到像这样的文件
In [1]: import networkx as nx
In [2]: G = nx.cycle_graph(5).to_directed()
In [3]: with open('foo','w') as f:
...: for c in nx.simple_cycles(G):
...: print(c, file=f)
...:
In [4]: cat foo
[0, 4]
[0, 4, 3, 2, 1]
[0, 1, 2, 3, 4]
[0, 1]
[1, 2]
[2, 3]
[3, 4]