def random_graph(n, p, directed=False):
"""
Create a random graph
:param n: number of nodes
:param p: probability of edge creation (0 to 100)
:param directed: set to True to generate a directed graph. Default is False (undirected graph)
:return: graph
"""
if not directed:
g = Graph()
for i in range(n):
g.add_vertex(i)
for u, v in combinations(g.vertices, 2):
# print(u, v)
if randrange(1, 101) <= p:
g.add_edge(u, v)
return g
else:
dg = Digraph()
for i in range(n):
dg.add_vertex(i)
for u, v in permutations(dg.vertices, 2):
# print(u, v)
if randrange(1, 101) <= p:
dg.add_edge(u, v)
return dg
def transpose(g):
gt = Digraph()
for u in g.vertices:
gt.add_vertex(u)
for u, v in g.edges:
gt.add_edge(v, u, g.weight(u, v))
return gt
for u in list0:
for v in g.neighbors(u):
in_degrees[v] -= 1
if in_degrees[v] == 0:
list0.append(v)
return len(list0) == len(g.vertices)
if __name__ == '__main__':
dress_order = [['shirt', 'tie'], ['tie', 'jacket'], ['belt', 'jacket'],
['shirt', 'belt'], ['undershorts', 'pants'],
['pants', 'shoes'], ['socks', 'shoes']]
g = Digraph()
g.add_edges_from(dress_order)
g.add_vertex('jacket')
g.add_vertex('watch')
g.add_vertex('shoes')
print(g)
print(is_directed_acyclic_graph(g))
print(is_directed_acyclic_dfs(g))
# print(is_directed_acyclic_bfs(g))
# g1 = Digraph()
# g1.add_vertex(1)
# g1.add_vertex(2)
# print(is_directed_acyclic_bfs(g1))
# g2 = Digraph()
# g2.add_edge(1, 2)
# g2.add_edge(1, 3)
# def topological_sort3(g):
# in_degrees = defaultdict(int)
#
# for u in g.vertices:
# in_degrees[u] += 0
# for v in g.neighbors(u):
# in_degrees[v] += 1
#
# list0 = [k for k, v in in_degrees.items() if v == 0] # vertices with in-degree = 0
# result = list0.copy()
# for u in list0:
# for v in g.neighbors(u):
# in_degrees[v] -= 1
# if in_degrees[v] == 0:
# result.append(v)
#
# return result
if __name__ == '__main__':
dress_order = [['shirt', 'tie'], ['tie', 'jacket'], ['belt', 'jacket'],
['shirt', 'belt'], ['undershorts', 'pants'],
['undershorts', 'shoes'], ['pants', 'shoes'],
['pants', 'belt'], ['socks', 'shoes']]
g = Digraph()
g.add_edges_from(dress_order)
g.add_vertex('watch')
print(g)
print(topological_sort(g))
print(topological_sort2(g))