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))