def myfunctionsuperkey(test, graph, simul, learn): # Commands for the fit of the nodes if test == -1: Graph_Plot.check_right_number_graphs(start_my_code.NUMBER_OF_NODES, start_my_code.NUMBER_OF_GRAPHS, start_my_code.INIT_SEED, 1483) if test == 1: Graph_Plot.calculatemystats(start_my_code.NUMBER_OF_NODES, start_my_code.NUMBER_OF_TIER_1, start_my_code.LENGTH, start_my_code.SIG_FACT, 500) Graph_Plot.findmyfactor(start_my_code.NUMBER_OF_NODES, start_my_code.NUMBER_OF_TIER_1, start_my_code.LENGTH, 500) # Create a new random graph if graph == 1: G = Graph_Plot.build_my_random_net(start_my_code.NUMBER_OF_NODES, start_my_code.NUMBER_OF_TIER_1, start_my_code.LENGTH, start_my_code.SIG_FACT, start_my_code.GAMMA, start_my_code.FACTOR, True) G.writemygraph(Graph_Plot.getnamenodes(start_my_code.INIT_SEED), Graph_Plot.getnameedges(start_my_code.INIT_SEED), start_my_code.INIT_SEED) Graph_Plot.plotpretty(G.graph, G.nodelayer, Graph_Plot.getmygraphname(start_my_code.INIT_SEED)) # Load an existing graph (Careful with existing disruptions!, txt file should be topological) if graph == 2: G = Graph_Plot.addmygraph(Graph_Plot.getnamenodes(start_my_code.INIT_SEED), Graph_Plot.getnameedges(start_my_code.INIT_SEED)) #Graph_Plot.plotpretty(G.graph, G.nodelayer, Graph_Plot.getmygraphname(start_my_code.INIT_SEED)) # Start a simulation if simul == 1: my_code_simul.StartMySimul(start_my_code.TIME_SIMUL, G, True, True, start_my_code.INIT_SEED_SIMUL) # Start a lot of simul if simul == 2: my_code_simul.startall(start_my_code.TIME_SIMUL, G, True, True, start_my_code.INIT_SEED_SIMUL, start_my_code.NUMBER_OF_SIMUL) # Compute statistics with a lot of simulations if test == 2: my_code_simul.analysemytrack("500trackdis1234.txt") my_code_simul.stats_lot(start_my_code.NUMBER_OF_SIMUL) # Generate a lot of graphs for the learning and check for duplicates if graph == 3: Graph_Plot.generatealotofgraphs(start_my_code.NUMBER_OF_GRAPHS, True, start_my_code.INIT_SEED, 0) # Get the babies if graph == 4: Graph_Plot.getmybabies(start_my_code.NUMBER_OF_GRAPHS, start_my_code.INIT_SEED) Graph_Plot.getallsiblings(start_my_code.NUMBER_OF_GRAPHS, start_my_code.INIT_SEED) # Start simulations for all the graphs in the directory graphdata. Stores all in. if simul == 3: left = start_my_code.NUMBER_OF_GRAPHS current_seed_simul3 = start_my_code.INIT_SEED while left > 0: for i in range(start_my_code.NUMBER_OF_SIMUL): print "current simulation", i test = my_code_simul.simulforallbabies(start_my_code.TIME_SIMUL, start_my_code.INIT_SEED_SIMUL + i, start_my_code.NUMBER_OF_NODES, 1, current_seed_simul3) if test == 0: current_seed_simul3 += 1 if test == 1: current_seed_simul3 +=1 left -= 1 if test == -10: print "Erreur de data graphs" break # Learning, preprocessing of data if learn == 1: #learning.check_right_number(start_my_code.NUMBER_OF_NODES, start_my_code.NUMBER_OF_GRAPHS, start_my_code.INIT_SEED, 1483) #learning.get_feature_one_family(start_my_code.NUMBER_OF_NODES, start_my_code.INIT_SEED, start_my_code.INIT_SEED_SIMUL, start_my_code.NUMBER_OF_TIER_1, start_my_code.NUMBER_OF_SIMUL) data = learning.get_feature_all_families(start_my_code.NUMBER_OF_NODES, start_my_code.INIT_SEED, start_my_code.INIT_SEED_SIMUL, start_my_code.NUMBER_OF_TIER_1, start_my_code.NUMBER_OF_SIMUL, start_my_code.NUMBER_OF_GRAPHS) learning.check_identical_rows(data[0]) learning.store_data_print_size(data[0], data[1], 'features', 'label') if learn == 2: learn_algo.main(1) if learn == 3: compare_features.main() print "done", test, graph, simul, learn """