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Viewing changes to networkx/algorithms/bipartite/tests/test_cluster.py

  • Committer: Package Import Robot
  • Author(s): Sandro Tosi
  • Date: 2012-08-11 12:41:30 UTC
  • mfrom: (1.4.1) (5.1.13 sid)
  • Revision ID: package-import@ubuntu.com-20120811124130-whr6uso7fncyg8bi
Tags: 1.7-1
* New upstream release
* debian/patches/changeset_*
  - removed, included upstream

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networkx/algorithms/bipartite/tests/test_cluster.py
 
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import networkx as nx
 
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from nose.tools import *
 
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from networkx.algorithms.bipartite.cluster import cc_dot,cc_min,cc_max
 
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import networkx.algorithms.bipartite as bipartite
 
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def test_pairwise_bipartite_cc_functions():
 
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    # Test functions for different kinds of bipartite clustering coefficients
 
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    # between pairs of nodes using 3 example graphs from figure 5 p. 40 
 
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    # Latapy et al (2008)
 
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    G1 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7)])
 
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    G2 = nx.Graph([(0,2),(0,3),(0,4),(1,3),(1,4),(1,5)])
 
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    G3 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7),(1,8),(1,9)])
 
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    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]}
 
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    for i, G in enumerate([G1, G2, G3]):
 
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        assert(bipartite.is_bipartite(G))
 
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        assert(cc_dot(set(G[0]), set(G[1])) == result[i][0])
 
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        assert(cc_min(set(G[0]), set(G[1])) == result[i][1])
 
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        assert(cc_max(set(G[0]), set(G[1])) == result[i][2])
 
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def test_star_graph():
 
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    G=nx.star_graph(3)
 
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    # all modes are the same
 
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    answer={0:0,1:1,2:1,3:1}
 
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    assert_equal(bipartite.clustering(G,mode='dot'),answer)
 
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    assert_equal(bipartite.clustering(G,mode='min'),answer)
 
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    assert_equal(bipartite.clustering(G,mode='max'),answer)
 
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@raises(nx.NetworkXError)
 
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def test_not_bipartite():
 
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    bipartite.clustering(nx.complete_graph(4))
 
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@raises(nx.NetworkXError)
 
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def test_bad_mode():
 
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    bipartite.clustering(nx.path_graph(4),mode='foo')
 
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def test_path_graph():
 
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    G=nx.path_graph(4)
 
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    answer={0:0.5,1:0.5,2:0.5,3:0.5}
 
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    assert_equal(bipartite.clustering(G,mode='dot'),answer)
 
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    assert_equal(bipartite.clustering(G,mode='max'),answer)
 
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    answer={0:1,1:1,2:1,3:1}
 
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    assert_equal(bipartite.clustering(G,mode='min'),answer)
 
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def test_average_path_graph():
 
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    G=nx.path_graph(4)
 
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    assert_equal(bipartite.average_clustering(G,mode='dot'),0.5)
 
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    assert_equal(bipartite.average_clustering(G,mode='max'),0.5)
 
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    assert_equal(bipartite.average_clustering(G,mode='min'),1)
 
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