def ClosenessCentralityExperiment(G, min_target, max_target, filename):
print nx.info(G)
print 'average closeness centrality: ', UF.average_closeness_centrality(G)
X_closeness_centrality = []
Y_nD = []
target = min_target
while target <= max_target:
copyG = G.copy()
new_G = SimulatedAnnealing(copyG, target, closeness_centrality_cost_function)
closeness_centrality = UF.average_closeness_centrality(new_G)
nD = SCT.controllability(new_G)
X_closeness_centrality.append(closeness_centrality)
Y_nD.append(nD)
print "target = ", target, " ClosenessC = ", closeness_centrality, 'nD = ', nD
target += 0.05
s = 'results/' + filename
with open(s, "w") as f:
for i in range(len(Y_nD)):
print >> f, "%f %f"%(X_closeness_centrality[i], Y_nD[i])
return (X_closeness_centrality, Y_nD)
def closeness_centrality_cost_function(G, target):
return math.fabs(UF.average_closeness_centrality(G) - target)
