def generate_random_topology_BA(N_nodes, n):
"""Generate a Barabasi-Albert random graph with N_nodes nodes, and new link parameter n
Return the triplet (Graph, adjacancy matrix, Laplacian matrix)
"""
G = nx.barabasi_albert_graph(N_nodes, n)
adja = np.array(nx.to_numpy_matrix(G))
L = control2.get_Laplacian(adja)
return (G, adja, L)
def generate_random_topology_ER(N_nodes, p):
"""Generate a Erdos-Renyi random graph with N_nodes nodes, and link probability p
Return the triplet (Graph, adjacancy matrix, Laplacian matrix)
"""
G = nx.erdos_renyi_graph(N_nodes, p)
adja = np.array(nx.to_numpy_matrix(G))
L = control2.get_Laplacian(adja)
return (G, adja, L)
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)