## Mercurial > repos > shellac > sam_consensus_v3

### view 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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import networkx as nx from networkx.algorithms.approximation import average_clustering # This approximation has to be be exact in regular graphs # with no triangles or with all possible triangles. def test_petersen(): # Actual coefficient is 0 G = nx.petersen_graph() assert average_clustering(G, trials=int(len(G) / 2)) == nx.average_clustering(G) def test_petersen_seed(): # Actual coefficient is 0 G = nx.petersen_graph() assert average_clustering( G, trials=int(len(G) / 2), seed=1 ) == nx.average_clustering(G) def test_tetrahedral(): # Actual coefficient is 1 G = nx.tetrahedral_graph() assert average_clustering(G, trials=int(len(G) / 2)) == nx.average_clustering(G) def test_dodecahedral(): # Actual coefficient is 0 G = nx.dodecahedral_graph() assert average_clustering(G, trials=int(len(G) / 2)) == nx.average_clustering(G) def test_empty(): G = nx.empty_graph(5) assert average_clustering(G, trials=int(len(G) / 2)) == 0 def test_complete(): G = nx.complete_graph(5) assert average_clustering(G, trials=int(len(G) / 2)) == 1 G = nx.complete_graph(7) assert average_clustering(G, trials=int(len(G) / 2)) == 1