def test_clustering_coefficient(self):
"""tests the clustering coefficent"""
v = Vertex('v')
w = Vertex('w')
x = Vertex('x')
e = Arc(v,w)
e2 = Arc(x, w)
e3 = Arc(x, v)
dg = DirectedGraph([v, w, x],[e, e2, e3])
self.assertAlmostEqual(dg.clustering_coefficient(),0.5)
def ErdosRenyiClustering():
"""
Creates an Erdos-Renyi graph of 1000 vertices and iterates through
p, checking the clustering coefficient each time. Uses pylot to
graph all that stuff, to see if there's anything interesting
"""
import matplotlib.pyplot as pyplot
vs = [Vertex(str(v)) for v in range(100)]
cs = []
ps = []
p = 0.01
for i in range(100):
drg = DirectedGraph(vs)
drg.add_random_arcs(p)
ps.append(p)
cs.append(drg.clustering_coefficient())
p += 0.01
pyplot.plot(ps,cs)
pyplot.xlabel('p')
pyplot.ylabel('clustering coefficient')
pyplot.show()
