def test_simrank_disregard_nb(self):
# test graph G
G = self.G
sim = simrank(G, remove_neighbors=False, remove_self=False)
nt.assert_equal(len(sim), 3)
for i in range(3):
nt.assert_in(i, sim)
for i in self.G.simrank.keys():
nt.assert_equal(len(self.G.simrank[i]), len(sim[i]))
for j in self.G.simrank[i].keys():
nt.assert_almost_equal(sim[i][j],
self.G.simrank[i][j],
places=4)
# test graph H
H = self.H
sim = simrank(H, remove_neighbors=False, remove_self=False)
nt.assert_equal(len(sim), 4)
for i in range(4):
nt.assert_in(i, sim)
for i in self.H.simrank.keys():
nt.assert_equal(len(self.H.simrank[i]), len(sim[i]))
for j in self.H.simrank[i].keys():
nt.assert_almost_equal(sim[i][j],
self.H.simrank[i][j],
places=4)
def test_graph_with_orphan(self):
I = self.I
sim = simrank(I, remove_neighbors=False, remove_self=False)
nt.assert_equal(len(sim), 4)
for i in range(4):
nt.assert_in(i, sim)
for i in self.I.simrank.keys():
nt.assert_equal(len(self.I.simrank[i]), len(sim[i]))
for j in self.I.simrank[i].keys():
nt.assert_almost_equal(sim[i][j], self.I.simrank[i][j], places=4)
def test_simrank_disregard_nb(self):
# test graph G
G = self.G
sim = simrank(G, remove_neighbors=False, remove_self=False)
nt.assert_equal(len(sim), 3)
for i in range(3):
nt.assert_in(i, sim)
for i in self.G.simrank.keys():
nt.assert_equal(len(self.G.simrank[i]), len(sim[i]))
for j in self.G.simrank[i].keys():
nt.assert_almost_equal(sim[i][j], self.G.simrank[i][j], places=4)
# test graph H
H = self.H
sim = simrank(H, remove_neighbors=False, remove_self=False)
nt.assert_equal(len(sim), 4)
for i in range(4):
nt.assert_in(i, sim)
for i in self.H.simrank.keys():
nt.assert_equal(len(self.H.simrank[i]), len(sim[i]))
for j in self.H.simrank[i].keys():
nt.assert_almost_equal(sim[i][j], self.H.simrank[i][j], places=4)
def test_graph_with_orphan(self):
I = self.I
sim = simrank(I, remove_neighbors=False, remove_self=False)
nt.assert_equal(len(sim), 4)
for i in range(4):
nt.assert_in(i, sim)
for i in self.I.simrank.keys():
nt.assert_equal(len(self.I.simrank[i]), len(sim[i]))
for j in self.I.simrank[i].keys():
nt.assert_almost_equal(sim[i][j],
self.I.simrank[i][j],
places=4)
if __name__ == "__main__":
myedge = readfile(sys.argv[1])
K = nx.Graph()
for i in range(len(myedge)):
K.add_edge(myedge[i][0], myedge[i][1])
# Alld = readattr('degrees.txt')
# H1 = K.subgraph(Alld)
# Bookd = readattr('degreesBook.txt')
# H2 = K.subgraph(Bookd)
DVDd = readattr('degreesDVD.txt')
DVDd = dict(sorted(DVDd.items(), key=operator.itemgetter(1))[:])
H3 = K.subgraph(DVDd)
# Musicd = readattr('degreesMusic.txt')
# H4 = K.subgraph(Musicd)
s3 = similarity.simrank(H3)
print(s3)
# mat = nx.to_numpy_matrix(H9)
# print(len(mat))
writefile('DVDsim5.csv', s3)
