def test_keys_only(self):
graph = {'a': {1, 3}, 'c': {1, 3}, 'd': {3, 6}, 'h': {8}}
hk = HopcroftKarp(graph)
max_matching = hk.maximum_matching(keys_only=True)
self.assertTrue(
max_matching == {
'a': 1,
'c': 3,
'd': 6,
'h': 8
} or max_matching == {
'a': 3,
'c': 1,
'd': 6,
'h': 8
}, 'maximum matching is incorrect')
def pick(problems,solved2):
chosen=[]
if len(problems) == 0:
return chosen
graph = {}
co=0
for typ,l,r in problems:
allp = getall(typ,l,r,solved2)
if len(allp) == 0:
continue
random.shuffle(allp)
graph[co] = allp
co=co+1
if len(graph) == 0:
return []
hk = HopcroftKarp(graph)
max_matching = hk.maximum_matching()
for i in range(0,co):
chosen.append(max_matching[i])
random.shuffle(chosen)
return chosen
def __init__(self, graph):
self.graph = graph
self.left = set(graph.keys())
self.right = set()
for value in graph.values():
self.right.update(value)
for vertex in self.left:
for neighbour in graph[vertex]:
if neighbour not in graph:
graph[neighbour] = set()
graph[neighbour].add(vertex)
else:
graph[neighbour].add(vertex)
hk = HopcroftKarp(graph)
self.matching = hk.maximum_matching()
self.E_w = set()
self.E_0 = set()
self.E_1 = set()
def test_mainExample(self):
graph = {'a': {1, 3}, 'c': {1, 3}, 'd': {3, 6}, 'h': {8}}
hk = HopcroftKarp(graph)
max_matching = hk.maximum_matching()
self.assertTrue(
max_matching == {
1: 'a',
'a': 1,
3: 'c',
'c': 3,
6: 'd',
'd': 6,
8: 'h',
'h': 8
} or max_matching == {
3: 'a',
'a': 3,
1: 'c',
'c': 1,
6: 'd',
'd': 6,
8: 'h',
'h': 8
}, 'maximum matching is incorrect')
for neighbour in self.graph[vertex]:
if (vertex in left_star and neighbour in right_star) or \
(vertex in left_plus and neighbour in right_plus):
self.E_w.add((vertex, neighbour))
graph_prime = {}
for vertex in left_unlabelled:
graph_prime[vertex] = set([neighbour for neighbour in self.graph[vertex] if neighbour in right_unlabelled])
for vertex in right_unlabelled:
graph_prime[vertex] = set([neighbour for neighbour in self.graph[vertex] if neighbour in left_unlabelled])
self.edge_part_perfect(graph_prime, left_unlabelled, right_unlabelled)
return self.E_1, self.E_w, self.E_0
# g = {'a': {1, 3}, 'b': {1, 2, 4}, 'c': {1, 3}, 'd': {3, 5, 6}, 'e': {5}, 'f': {4, 7},
# 'g': {5}, 1: {'a', 'b', 'c'}, 2: {'b'}, 3: {'a', 'b', 'c'}, 4: {'b', 'f', 'e'},
# 5: {'d', 'e', 'g'}, 6: {'d'}, 7: {'f'}}
# l = {'a', 'b', 'c', 'd', 'e', 'f', 'g'}
# r = set(range(1, 8))
g = {'S1': {'a', 'b', 'c'}, 'S2': {'a', 'b', 'c'}, 'S3': {'a', 'b', 'c'}}
C = CostaImproved(g)
m = HopcroftKarp(g)
maximum_matching = m.maximum_matching()
E1, Ew, E0 = C.edge_partitioning()
#print('Bipartite graph: ' + str(m))
print('Maximum Matching:' + str(maximum_matching))
print('one-persistent edges: ' + str(E1))
print('weakly-persistent edges: ' + str(Ew))
print('zero-persistent edges: ' + str(E0))
