networkx 包具有可以在线性时间内为您生成所需电路的功能...
有可能,如所讨论的那样,构建nx.MultiDiGraph()速度较慢且效率不高,或者仅将外部包用于一个功能是相当过度的。如果是这样,还有另一种方法。
计划:首先我们会找到一些从start_nodeto 的方式start_node,然后我们将插入所有尚未访问的循环。
from itertools import chain
from collections import defaultdict, deque
from typing import Tuple, List, Iterable, Iterator, DefaultDict, Deque
def retrieve_closed_path(arcs: List[Tuple[int, int]], start_node: int = 1) -> Iterator[int]:
if not arcs:
return iter([])
# for each node `u` carries queue of its
# neighbours to be visited from node `u`
d: DefaultDict[int, Deque[int]] = defaultdict(deque)
for u, v in arcs:
# deque pop and append complexity is O(1)
d[u].append(v)
def _dfs(node) -> Iterator[int]:
out: Iterator[int] = iter([])
# guarantee, that all queues
# will be emptied at the end
while d[node]:
# chain returns an iterator and helps to
# avoid unnecessary memory reallocations
out = chain([node], _dfs(d[node].pop()), out)
# if we return in this loop from recursive call, then
# `out` already carries some (node, ...) and we need
# only to insert all other loops which start at `node`
return out
return chain(_dfs(start_node), [start_node])
def path_to_string(path: Iterable[int]) -> str:
return '->'.join(str(x) for x in path)
例子:
E = [(1, 2), (2, 1)]
p = retrieve_closed_path(E, 1)
print(path_to_string(p))
>> 1->2->1
E = [(1, 2), (1, 5), (2, 3), (3, 1), (5, 6), (6, 1)]
p = retrieve_closed_path(E, 1)
print(path_to_string(p))
>> 1->5->6->1->2->3->1
E = [(1, 2), (2, 3), (3, 4), (4, 2), (2, 1)]
p = retrieve_closed_path(E, 1)
print(path_to_string(p))
>> 1->2->3->4->2->1
E = [(5, 1), (1, 5), (5, 2), (2, 5), (5, 1), (1, 4), (4, 5)]
p = retrieve_closed_path(E, 1)
print(path_to_string())
>> 1->4->5->1->5->2->5->1