U A·fÙã@s4ddlmZmZdgZedƒedƒddd„ƒƒZdS) é)Únot_implemented_forÚpy_random_stateÚaverage_clusteringéZdirectedéèNc s|t|ƒ‰d}t|ƒ}‡‡fdd„t|ƒDƒD]F}t|||ƒ}t|ƒdkrNq,ˆ |d¡\}}|||kr,|d7}q,||S)uËEstimates the average clustering coefficient of G. The local clustering of each node in `G` is the fraction of triangles that actually exist over all possible triangles in its neighborhood. The average clustering coefficient of a graph `G` is the mean of local clusterings. This function finds an approximate average clustering coefficient for G by repeating `n` times (defined in `trials`) the following experiment: choose a node at random, choose two of its neighbors at random, and check if they are connected. The approximate coefficient is the fraction of triangles found over the number of trials [1]_. Parameters ---------- G : NetworkX graph trials : integer Number of trials to perform (default 1000). seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. Returns ------- c : float Approximated average clustering coefficient. Examples -------- >>> from networkx.algorithms import approximation >>> G = nx.erdos_renyi_graph(10, 0.2, seed=10) >>> approximation.average_clustering(G, trials=1000, seed=10) 0.214 References ---------- .. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering coefficient and transitivity. Universität Karlsruhe, Fakultät für Informatik, 2004. https://doi.org/10.5445/IR/1000001239 rcsg|]}tˆ ¡ˆƒ‘qS©)ÚintÚrandom)Ú.0Úi©ÚnÚseedrú\/tmp/pip-unpacked-wheel-0wvcsx6h/networkx/algorithms/approximation/clustering_coefficient.pyÚ 9sz&average_clustering..ré)ÚlenÚlistÚrangeÚsample) ÚGZtrialsrZ trianglesZnodesr ZnbrsÚuÚvrr rrs0   )rN)Znetworkx.utilsrrÚ__all__rrrrrÚs