<s> """<STR_LIT>""" <EOL> import networkx as nx <EOL> from networkx . algorithms . centrality . flow_matrix import * <EOL> __author__ = """<STR_LIT>""" <EOL> __all__ = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> def current_flow_closeness_centrality ( G , weight = '<STR_LIT>' , <EOL> dtype = float , solver = '<STR_LIT>' ) : <EOL> """<STR_LIT>""" <EOL> from networkx . utils import reverse_cuthill_mckee_ordering <EOL> import numpy as np <EOL> import scipy <EOL> if G . is_directed ( ) : <EOL> raise nx . NetworkXError ( <EOL> "<STR_LIT>" ) <EOL> if not nx . is_connected ( G ) : <EOL> raise nx . NetworkXError ( "<STR_LIT>" ) <EOL> solvername = { "<STR_LIT>" : FullInverseLaplacian , <EOL> "<STR_LIT>" : SuperLUInverseLaplacian , <EOL> "<STR_LIT>" : CGInverseLaplacian } <EOL> n = G . number_of_nodes ( ) <EOL> ordering = list ( reverse_cuthill_mckee_ordering ( G ) ) <EOL> H = nx . relabel_nodes ( G , dict ( zip ( ordering , range ( n ) ) ) ) <EOL> betweenness = dict . fromkeys ( H , <NUM_LIT:0.0> ) <EOL> n = H . number_of_nodes ( ) <EOL> L = laplacian_sparse_matrix ( H , nodelist = range ( n ) , weight = weight , <EOL> dtype = dtype , format = '<STR_LIT>' ) <EOL> C2 = solvername [ solver ] ( L , width = <NUM_LIT:1> , dtype = dtype ) <EOL> for v in H : <EOL> col = C2 . get_row ( v ) <EOL> for w in H : <EOL> betweenness [ v ] += col [ v ] - <NUM_LIT:2> * col [ w ] <EOL> betweenness [ w ] += col [ v ] <EOL> for v in H : <EOL> betweenness [ v ] = <NUM_LIT:1.0> / ( betweenness [ v ] ) <EOL> return dict ( ( ordering [ k ] , float ( v ) ) for k , v in betweenness . items ( ) ) <EOL> information_centrality = current_flow_closeness_centrality <EOL> def setup_module ( module ) : <EOL> from nose import SkipTest <EOL> try : <EOL> import numpy <EOL> except : <EOL> raise SkipTest ( "<STR_LIT>" ) </s>
