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## iof-tools / networkxMiCe / networkx-master / networkx / algorithms / bipartite / tests / test_cluster.py @ 5cef0f13

 1 ```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) ```