def random_fixed_test2():
file_name = "Tests/test_rf_twitter.csv"
open(file_name, 'w+').write("Threshold Incentives\n")
iterations = 51
graph = graph_setup.create_graph(twitter)
graph_setup.edge_random_probability(graph)
for i in range (5,iterations,5):
g = graph_setup.set_fixed_threshold(graph, i)
tot_incentives = tpi_algorithm.tpi(g)
open(file_name, 'a+').write("%d %d\n" % (i, tot_incentives))
def random_random_test(dataset, path):
avg = 0
file_name = "Tests/"+dataset+"_tests/test_rr.csv"
open(file_name, 'w+').write("Iteration Incentives\n")
iterations = 50
for j in range(0, iterations):
graph = graph_setup.create_graph(path)
graph_setup.edge_random_probability(graph)
g = graph_setup.set_random_threshold(graph)
tot_incentives = tpi_algorithm.tpi(g)
avg += tot_incentives
open(file_name, 'a+').write("%d %d\n" % (j+1, tot_incentives))
open(file_name, 'a+').write("Media %d\n" % (avg/iterations))
def proportional_proportional_test(dataset, path):
avg = np.zeros(10)
file_name = "Tests/"+dataset+"_tests/test_pp.csv"
open(file_name, 'w+').write("Threshold AVG_Incentives\n")
iterations = 50
for j in range(0, iterations):
graph = graph_setup.create_graph(path)
graph_setup.edge_proportional_to_degree_probability(graph)
for i in range(0, 10):
g = graph_setup.set_degree_proportional_thresholds(graph, (i + 1) / 10)
tot_incentives = tpi_algorithm.tpi(g)
avg[i] += tot_incentives
for y in range(0, 10):
open(file_name, 'a+').write("%d %d\n" % (y + 1, avg[y] / iterations))