def _minimum_spanning_tree_kruskal_adjacency_matrix(graph):
mst = Graph(*[getattr(graph, str(v)) for v in graph.vertices])
sort_key = lambda item: item[1].value
dsf = DisjointSetForest()
for v in graph.vertices:
dsf.make_set(v)
for _, edge in sorted(graph.edge_weights.items(), key=sort_key):
u, v = edge.source.name, edge.target.name
if dsf.find_root(u) is not dsf.find_root(v):
mst.add_edge(u, v, edge.value)
dsf.union(u, v)
return mst
def _minimum_spanning_tree_parallel_prim_adjacency_list(graph, num_threads):
q = [
PriorityQueue(implementation='binomial_heap')
for _ in range(num_threads)
]
e = [dict() for _ in range(num_threads)]
v2q = dict()
mst = Graph(implementation='adjacency_list')
itr = iter(graph.vertices)
for i in range(len(graph.vertices)):
v2q[next(itr)] = i % len(q)
q[0].push(next(iter(graph.vertices)), 0)
while True:
_vs = [None for _ in range(num_threads)]
with ThreadPoolExecutor(max_workers=num_threads) as Executor:
for i in range(num_threads):
Executor.submit(_find_min, q[i], _vs, i).result()
v = None
for i in range(num_threads):
if _comp(_vs[i], v, lambda u, v: u.key < v.key):
v = _vs[i]
if v is None:
break
v = v.data
idx = v2q[v]
q[idx].pop()
if not hasattr(mst, v):
mst.add_vertex(graph.__getattribute__(v))
if e[idx].get(v, None) is not None:
edge = e[idx][v]
mst.add_vertex(edge.target)
mst.add_edge(edge.source.name, edge.target.name, edge.value)
mst.add_edge(edge.target.name, edge.source.name, edge.value)
for w_node in graph.neighbors(v):
w = w_node.name
vw = graph.edge_weights[v + '_' + w]
j = v2q[w]
q[j].push(w, vw.value)
if e[j].get(w, None) is None or \
e[j][w].value > vw.value:
e[j][w] = vw
return mst
def _minimum_spanning_tree_prim_adjacency_list(graph):
q = PriorityQueue(implementation='binomial_heap')
e = {}
mst = Graph(implementation='adjacency_list')
q.push(next(iter(graph.vertices)), 0)
while not q.is_empty:
v = q.pop()
if not hasattr(mst, v):
mst.add_vertex(graph.__getattribute__(v))
if e.get(v, None) is not None:
edge = e[v]
mst.add_vertex(edge.target)
mst.add_edge(edge.source.name, edge.target.name, edge.value)
mst.add_edge(edge.target.name, edge.source.name, edge.value)
for w_node in graph.neighbors(v):
w = w_node.name
vw = graph.edge_weights[v + '_' + w]
q.push(w, vw.value)
if e.get(w, None) is None or e[w].value > vw.value:
e[w] = vw
return mst