## Mercurial > repos > shellac > sam_consensus_v3

### comparison env/lib/python3.9/site-packages/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py @ 0:4f3585e2f14b draft default tip

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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |

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-1:000000000000 | 0:4f3585e2f14b |
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1 import networkx as nx | |

2 from networkx.algorithms.approximation import average_clustering | |

3 | |

4 # This approximation has to be be exact in regular graphs | |

5 # with no triangles or with all possible triangles. | |

6 | |

7 | |

8 def test_petersen(): | |

9 # Actual coefficient is 0 | |

10 G = nx.petersen_graph() | |

11 assert average_clustering(G, trials=int(len(G) / 2)) == nx.average_clustering(G) | |

12 | |

13 | |

14 def test_petersen_seed(): | |

15 # Actual coefficient is 0 | |

16 G = nx.petersen_graph() | |

17 assert average_clustering( | |

18 G, trials=int(len(G) / 2), seed=1 | |

19 ) == nx.average_clustering(G) | |

20 | |

21 | |

22 def test_tetrahedral(): | |

23 # Actual coefficient is 1 | |

24 G = nx.tetrahedral_graph() | |

25 assert average_clustering(G, trials=int(len(G) / 2)) == nx.average_clustering(G) | |

26 | |

27 | |

28 def test_dodecahedral(): | |

29 # Actual coefficient is 0 | |

30 G = nx.dodecahedral_graph() | |

31 assert average_clustering(G, trials=int(len(G) / 2)) == nx.average_clustering(G) | |

32 | |

33 | |

34 def test_empty(): | |

35 G = nx.empty_graph(5) | |

36 assert average_clustering(G, trials=int(len(G) / 2)) == 0 | |

37 | |

38 | |

39 def test_complete(): | |

40 G = nx.complete_graph(5) | |

41 assert average_clustering(G, trials=int(len(G) / 2)) == 1 | |

42 G = nx.complete_graph(7) | |

43 assert average_clustering(G, trials=int(len(G) / 2)) == 1 |