def dissociate_network(self, last_network, now_network):
y_last_structure = last_network.structure
y_now_structure = now_network.structure
y_plus = y_last_structure.copy()
y_minus = y_last_structure.copy()
for row in range(self.node_num):
for col in range(self.node_num):
if y_now_structure[row, col] == 1:
y_plus[row, col] = 1
if y_now_structure[row, col] == 0:
y_minus[row, col] = 0
result = 0
if self.isDirected:
result = (DirectedNetwork(y_plus), DirectedNetwork(y_minus))
else:
result = (UndirectedNetwork(y_plus), UndirectedNetwork(y_minus))
return result
def make_block_diag_net(self, network_list):
num_block = len(network_list)
block_mat = network_list[0].structure
for i in range(1, num_block):
block_mat = block_diag(block_mat, network_list[i].structure)
if self.isDirected:
return DirectedNetwork(block_mat)
else:
return UndirectedNetwork(block_mat)
def propose_network(self, last_network):
proposed_structure = last_network.structure.copy()
edge_idx = self.choose_mutable_edge(
) #choose_edge or choose_mutable_edge
result_network = 0
if isinstance(last_network, UndirectedNetwork):
proposed_structure[
edge_idx[0],
edge_idx[1]] = 1 - proposed_structure[edge_idx[0], edge_idx[1]]
proposed_structure[edge_idx[1],
edge_idx[0]] = proposed_structure[edge_idx[0],
edge_idx[1]]
result_network = UndirectedNetwork(proposed_structure)
elif isinstance(last_network, DirectedNetwork):
proposed_structure[
edge_idx[0],
edge_idx[1]] = 1 - proposed_structure[edge_idx[0], edge_idx[1]]
result_network = DirectedNetwork(proposed_structure)
return result_network
with open("pyBSTERGM/" + filename + '.csv',
'w',
newline='',
encoding='utf-8') as f:
writer = csv.writer(f)
for vec_f, vec_d in zip(formation_netStat, dissolution_netStat):
csv_row = vec_f.tolist() + vec_d.tolist()
writer.writerow(csv_row)
if __name__ == "__main__":
import data_samplk, data_knecht_friendship, data_tailor
sociational_interactions = [
UndirectedNetwork(np.array(data_tailor.KAPFTS1)),
UndirectedNetwork(np.array(data_tailor.KAPFTS2))
]
friendship_sequence = [
DirectedNetwork(np.array(data_knecht_friendship.friendship_t1)),
DirectedNetwork(np.array(data_knecht_friendship.friendship_t2)),
DirectedNetwork(np.array(data_knecht_friendship.friendship_t3)),
DirectedNetwork(np.array(data_knecht_friendship.friendship_t4))
]
samplk_sequence = [
DirectedNetwork(np.array(data_samplk.samplk1)),
DirectedNetwork(np.array(data_samplk.samplk2)),
DirectedNetwork(np.array(data_samplk.samplk3))
]
def model_netStat_edgeonly(network):
def model_netStat(network: UndirectedNetwork):
model = []
#define model
model.append(network.statCal_edgeNum())
model.append(network.statCal_geoWeightedESP(0.3))
return np.array(model)
#define model
model.append(network.statCal_edgeNum())
model.append(network.statCal_geoWeightedESP(0.3))
return np.array(model)
test_structure = np.array([[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
test_initnet = UndirectedNetwork(test_structure)
test_constraint_structure = np.array([
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
])
test_constraint_net = UndirectedNetwork(test_constraint_structure)
#1