/usr/lib/python2.7/dist-packages/networkx/algorithms/bipartite/tests/test_cluster.py is in python-networkx 1.8.1-0ubuntu3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import networkx as nx
from nose.tools import *
from networkx.algorithms.bipartite.cluster import cc_dot,cc_min,cc_max
import networkx.algorithms.bipartite as bipartite
def test_pairwise_bipartite_cc_functions():
# Test functions for different kinds of bipartite clustering coefficients
# between pairs of nodes using 3 example graphs from figure 5 p. 40
# Latapy et al (2008)
G1 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7)])
G2 = nx.Graph([(0,2),(0,3),(0,4),(1,3),(1,4),(1,5)])
G3 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7),(1,8),(1,9)])
result = {0:[1/3.0, 2/3.0, 2/5.0],
1:[1/2.0, 2/3.0, 2/3.0],
2:[2/8.0, 2/5.0, 2/5.0]}
for i, G in enumerate([G1, G2, G3]):
assert(bipartite.is_bipartite(G))
assert(cc_dot(set(G[0]), set(G[1])) == result[i][0])
assert(cc_min(set(G[0]), set(G[1])) == result[i][1])
assert(cc_max(set(G[0]), set(G[1])) == result[i][2])
def test_star_graph():
G=nx.star_graph(3)
# all modes are the same
answer={0:0,1:1,2:1,3:1}
assert_equal(bipartite.clustering(G,mode='dot'),answer)
assert_equal(bipartite.clustering(G,mode='min'),answer)
assert_equal(bipartite.clustering(G,mode='max'),answer)
@raises(nx.NetworkXError)
def test_not_bipartite():
bipartite.clustering(nx.complete_graph(4))
@raises(nx.NetworkXError)
def test_bad_mode():
bipartite.clustering(nx.path_graph(4),mode='foo')
def test_path_graph():
G=nx.path_graph(4)
answer={0:0.5,1:0.5,2:0.5,3:0.5}
assert_equal(bipartite.clustering(G,mode='dot'),answer)
assert_equal(bipartite.clustering(G,mode='max'),answer)
answer={0:1,1:1,2:1,3:1}
assert_equal(bipartite.clustering(G,mode='min'),answer)
def test_average_path_graph():
G=nx.path_graph(4)
assert_equal(bipartite.average_clustering(G,mode='dot'),0.5)
assert_equal(bipartite.average_clustering(G,mode='max'),0.5)
assert_equal(bipartite.average_clustering(G,mode='min'),1)
def test_ra_clustering_davis():
G = nx.davis_southern_women_graph()
cc4 = round(bipartite.robins_alexander_clustering(G), 3)
assert_equal(cc4, 0.468)
def test_ra_clustering_square():
G = nx.path_graph(4)
G.add_edge(0, 3)
assert_equal(bipartite.robins_alexander_clustering(G), 1.0)
def test_ra_clustering_zero():
G = nx.Graph()
assert_equal(bipartite.robins_alexander_clustering(G), 0)
G.add_nodes_from(range(4))
assert_equal(bipartite.robins_alexander_clustering(G), 0)
G.add_edges_from([(0,1),(2,3),(3,4)])
assert_equal(bipartite.robins_alexander_clustering(G), 0)
G.add_edge(1,2)
assert_equal(bipartite.robins_alexander_clustering(G), 0)
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