Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,43 @@ +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