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import networkx as nx
from networkx.algorithms.approximation import average_clustering
# This approximation has to 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=len(G) // 2) == nx.average_clustering(G)
def test_petersen_seed():
# Actual coefficient is 0
G = nx.petersen_graph()
assert average_clustering(G, trials=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=len(G) // 2) == nx.average_clustering(G)
def test_dodecahedral():
# Actual coefficient is 0
G = nx.dodecahedral_graph()
assert average_clustering(G, trials=len(G) // 2) == nx.average_clustering(G)
def test_empty():
G = nx.empty_graph(5)
assert average_clustering(G, trials=len(G) // 2) == 0
def test_complete():
G = nx.complete_graph(5)
assert average_clustering(G, trials=len(G) // 2) == 1
G = nx.complete_graph(7)
assert average_clustering(G, trials=len(G) // 2) == 1
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