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
"""
