def main(): N_nodes = 200 p_out = .1 monte_carlo = 100 results = [] for N_clusters in clusters_generator(): results.append([]) print N_clusters for p_in in np.linspace(0,1,20): AVG = [] for mc in range( monte_carlo ): #Random Initial state theta0 = np.random.normal( loc = 0, scale = .01, size = (1,N_nodes) )[0] #Random small phase angles dtheta0 = np.random.normal( loc = 0, scale = .01, size = (1,N_nodes) )[0] #Random small frequencies Y0 = np.append( theta0, dtheta0 ) Y0 = np.append( Y0, 1 ) #Random Initial state thetaf = np.random.normal( loc = 0, scale = .01, size = (1,N_nodes) )[0] #Random small phase angles dthetaf = np.random.normal( loc = 0, scale = .01, size = (1,N_nodes) )[0] #Random small frequencies Yf = np.append( thetaf, dthetaf ) Yf = np.append( Yf, 1 ) G = nx.planted_partition_graph(N_clusters, N_nodes/N_clusters, p_in, p_out) adja = np.array( nx.to_numpy_matrix( G ) ) L = control2.get_Laplacian( adja ) Psource = generate_random_power_distribution( N_nodes ) Pmax = generate_random_capacity_distribution( N_nodes, adja ) K = Pmax / float( I * Omega ) A = control2.build_transition_matrix( Psource, L, Omega, I, K, alpha, Dt ) A_powers = [ np.identity( A.shape[0] ), A ] for k in range( control_time + 1 )[2:]: A_powers.append( np.dot( A, A_powers[-1] ) ) storage_level = 100 * np.ones((1,N_nodes))[0] drivers = control2.rank_submodular_greedy_lazy( A_powers, adja, range( N_nodes ), control_time, storage_level, max_capacity, r, I, Omega, Y0, Yf, True ) AVG.append( len(drivers) ) results[-1].append( AVG ) with open('./results3.json', 'w') as f: json.dump(results,f)
def main(): N_nodes = 200 p_out = 0.05 monte_carlo = 100 results = [] p_values = [0.1, 0.3, 0.5, 0.7, 0.9] c_values = [2, 4, 5, 8, 10, 20, 25, 40, 50] val = 0 with progress.Bar(label="Progress: ", expected_size=len(p_values) * len(c_values) * monte_carlo) as bar: for p_in in [0.1, 0.3, 0.5, 0.7, 0.9]: results.append([]) for N_clusters in [2, 4, 5, 8, 10, 20, 25, 40, 50]: AVG = [] for mc in range(monte_carlo): # Random Initial state theta0 = np.random.normal(loc=0, scale=0.01, size=(1, N_nodes))[0] # Random small phase angles dtheta0 = np.random.normal(loc=0, scale=0.01, size=(1, N_nodes))[0] # Random small frequencies Y0 = np.append(theta0, dtheta0) Y0 = np.append(Y0, 1) # Random Initial state thetaf = np.random.normal(loc=0, scale=0.01, size=(1, N_nodes))[0] # Random small phase angles dthetaf = np.random.normal(loc=0, scale=0.01, size=(1, N_nodes))[0] # Random small frequencies Yf = np.append(thetaf, dthetaf) Yf = np.append(Yf, 1) G = nx.planted_partition_graph(N_clusters, N_nodes / N_clusters, p_in, p_out) adja = np.array(nx.to_numpy_matrix(G)) L = control2.get_Laplacian(adja) Psource = generate_random_power_distribution(N_nodes) Pmax = generate_random_capacity_distribution(N_nodes, adja) K = Pmax / float(I * Omega) A = control2.build_transition_matrix(Psource, L, Omega, I, K, alpha, Dt) A_powers = [np.identity(A.shape[0]), A] for k in range(control_time + 1)[2:]: A_powers.append(np.dot(A, A_powers[-1])) storage_level = 100 * np.ones((1, N_nodes))[0] drivers = control2.rank_submodular_greedy_lazy( A_powers, adja, range(N_nodes), control_time, storage_level, max_capacity, r, I, Omega, Y0, Yf, True, ) AVG.append(len(drivers)) val += 1 bar.show(val) results[-1].append(np.mean(AVG)) with open("./results.json", "w") as f: json.dump(results, f)