U Ç-e* ã@sDdZddlZddlZddlmZdgZedƒedƒd dd„ƒƒZdS) z% Computation of graph non-randomness éN)Únot_implemented_forÚnon_randomnessZdirectedZ multigraphÚweightc Csèddl}t |¡st d¡‚ttt |¡ƒƒdkr>> G = nx.karate_club_graph() >>> nr, nr_rd = nx.non_randomness(G, 2) >>> nr, nr_rd = nx.non_randomness(G, 2, 'weight') Notes ----- This computes Eq. (4.4) and (4.5) in Ref. [1]_. If a weight field is passed, this algorithm will use the eigenvalues of the weighted adjacency matrix to compute Eq. (4.4) and (4.5). References ---------- .. [1] Xiaowei Ying and Xintao Wu, On Randomness Measures for Social Networks, SIAM International Conference on Data Mining. 2009 rNzNon connected graph.z!Graph must not contain self-loops)réé)ÚnumpyÚnxZ is_connectedZNetworkXExceptionÚlenÚlistZselfloop_edgesZ NetworkXErrorÚtupleZ communityZlabel_propagation_communitiesZlinalgZeigvalsZto_numpy_arrayÚrealÚsumZnumber_of_nodesZnumber_of_edgesÚmathÚsqrt) ÚGÚkrÚnpZ eigenvaluesÚnrÚnÚmÚpZnr_rd©rúc/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/networkx/algorithms/non_randomness.pyr s>   2)Nr)Ú__doc__rZnetworkxrZnetworkx.utilsrÚ__all__rrrrrÚs