def test_add_edge(self):
graph = Graph("my graph")
graph.add_vertex("A")
graph.add_vertex("B")
graph.add_edge("A", "B")
self.assertEqual(1, len(graph.edges()))
graph.add_edge("X", "Y")
self.assertEqual(2, len(graph.edges()))
self.assertEqual(4, len(graph.vertices()))
def test_edges(self):
json_graph = {"label": "my graph", "graph": {"A": ["B"], "B": []}}
graph = Graph(input_graph=json.dumps(json_graph))
self.assertEqual(json_graph['label'], graph.get_label())
self.assertEqual(False, graph.is_directed())
self.assertEqual(json_graph['graph'], graph.get_graph())
self.assertEqual([Edge('A', 'B')], graph.edges())
def density(graph: Graph) -> float:
"""
The graph density is defined as the ratio of the number of edges of a given
graph, and the total number of edges, the graph could have.
A dense graph is a graph G = (V, E) in which |E| = Θ(|V|^2)
:param graph:
:return:
"""
V = len(graph.vertices())
E = len(graph.edges())
return 2.0 * (E / (V**2 - V))
def is_complete(graph: Graph) -> bool:
"""
Checks that each vertex has V(V-1) / 2 edges and that each vertex is
connected to V - 1 others.
runtime: O(n^2)
:param graph:
:return: true or false
"""
V = len(graph.vertices())
max_edges = (V**2 - V)
if not graph.is_directed():
max_edges //= 2
E = len(graph.edges())
if E != max_edges:
return False
for vertex in graph:
if len(graph[vertex]) != V - 1:
return False
return True
def test_set_from_adjacency_matrix(self):
array_graph = np.array([[0, 1], [1, 0]], dtype=object)
graph = Graph(input_array=array_graph)
self.assertEqual(2, len(graph.vertices()))
self.assertEqual(1, len(graph.edges()))
