def main():
G = gg.genGraph_import_graphML('../annotated_topo/Internetmci.graphml',
3000)
# rnd_sl= gg.randomize_service_load(G, seed = 0)
#
# rnd_sl.next()
#
# rnd_cap = gg.randomize_cap (G, 6000, seed =1)
# rnd_cap.next()
#
# rt = FTCP_placement (G, 0.9999, 0.9999, 0.9999, 0.9999)
#
# for nd, nb in G.edges(data = False):
# print G[nd][nb]['capacity']
#
# controllers, Rmin = rt.run()
controllers = [0, 1, 2]
distance_matrix = nx.all_pairs_shortest_path_length(G)
AR = Anealing_Clustering_Routing_Problem(G, distance_matrix)
start_time = time.time()
success, cluster, effort = AR.anneal_clustering(controllers)
end_time = time.time()
print "time consumed:", end_time - start_time
print " clustering results "
print success
print cluster
print effort
directory = directory_r +str(cap)+'/'
if not os.path.exists(directory):
os.makedirs(directory)
directory_g = directory +graph_name +'/'
if not os.path.exists(directory_g):
os.makedirs(directory_g)
graph_path = topoPath + graph_name+'.graphml'
print graph_path
G= gg.genGraph_import_graphML(graph_path, cap)
for task_id in range(0, 1):
d = [deepcopy(G), directory_g, task_id, 1, cap]
data.append(d)
mp_handler(data)
# jobs = []
# for i in range(2):
# d = [deepcopy(G), directory, i, 25, 1000]
# p = multiprocessing.Process(target=mp_worker, args=(d,))
# #jobs.append(p)
# p.start()
if new_cost == float('inf'):
return best_clustering, best_controllers, best_rel, best_cost, best_effort
T = T * alpha
return best_clustering, best_controllers, best_rel, best_cost, best_effort
if __name__ == '__main__':
logging.config.fileConfig('logging.config')
logger = logging.getLogger("all_aneal_placement")
logger.info("Program started")
G = gg.genGraph_import_graphML('../annotated_topo/Internetmci.graphml',
3000)
rt = Anealing_Placement_Problem(G)
best_clustering, best_controllers, best_rel, best_cost, best_effort = rt.anneal_placement(
)
#distance_matrix = nx.all_pairs_shortest_path_length(G)
#AR = Anealing_Clustering_Routing_Problem (G, distance_matrix)
#success, cluster, effort = AR.anneal_clustering(controllers)
print " final results "
print "clustering {}".format(best_clustering)
print "controllers {}".format(best_controllers)