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
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):
model = []
model.append(network.statCal_edgeNum())
return np.array(model)
# if self.pid is not None:
# print("pid:",self.pid," iter:", iter,"/",iter, "time elapsed(second):", round(time.time()-start_time,1))
# else:
# print(iter,"/",iter, "time elapsed(second):", round(time.time()-start_time,1))
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):
model = []
model.append(network.statCal_edgeNum())
return np.array(model)
netstat_reader_inst_samplk_vig_conti_d = BSTERGM_latest_exchangeSampler_work()
netstat_reader_inst_samplk_vig_conti_d.read_from_csv("example_results_samplk/samplk_jointly_normPrior_vignettesEx_conti_2chain_dissolution_NetworkStat")
netstat_reader_inst_samplk_vig_conti_d.show_traceplot()
#============================================================================================
if gof:
import numpy as np
from network import UndirectedNetwork, DirectedNetwork
from BSTERGM_GOF import BSTERGM_GOF
import data_samplk
from model_settings import model_netStat_samplk_vignettesEx
samplk_sequence = [
DirectedNetwork(np.array(data_samplk.samplk1)),
DirectedNetwork(np.array(data_samplk.samplk2)),
DirectedNetwork(np.array(data_samplk.samplk3))
]
# #samplk: gof
# #time 0,1
gof_inst_samplk_vig_f = BSTERGM_GOF(model_netStat_samplk_vignettesEx, reader_inst_samplk_vig.MC_formation_samples,
is_formation=True, obs_network_seq=samplk_sequence, time_lag=1, additional_netstat_function=model_netStat_samplk_vignettesEx)
gof_inst_samplk_vig_f.gof_run(num_sim=300, exchange_iter=200)
gof_inst_samplk_vig_f.show_boxplot(compare=True)
gof_inst_samplk_vig_d = BSTERGM_GOF(model_netStat_samplk_vignettesEx, reader_inst_samplk_vig.MC_dissolution_samples,
is_formation=False, obs_network_seq=samplk_sequence, time_lag=1, additional_netstat_function=model_netStat_samplk_vignettesEx)
gof_inst_samplk_vig_d.gof_run(num_sim=300, exchange_iter=200)
gof_inst_samplk_vig_d.show_boxplot(compare=True)
netstat_reader_inst_friendship_KHEx_conti_d.read_from_csv("example_results_friendship_20210722/friendship_jointly_normPrior_KHEx_20210722_0chain_dissolution_NetworkStat")
netstat_reader_inst_friendship_KHEx_conti_d.show_traceplot()
#============================================================================================
if gof:
import numpy as np
from network import UndirectedNetwork, DirectedNetwork
from BSTERGM_GOF import BSTERGM_GOF
import data_knecht_friendship
from model_settings import model_netStat_friendship_KHEx
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))
]
#friendship: gof
#time 0,1
# # def __init__(self, model_fn, posterior_parameter_samples, is_formation, obs_network_seq, time_lag, additional_netstat_function=None , rng_seed=2021):
gof_inst_friendship_KHEx_f = BSTERGM_GOF(model_netStat_friendship_KHEx, reader_inst_friendship_KHEx.MC_formation_samples,
is_formation=True, obs_network_seq=friendship_sequence, time_lag=0, additional_netstat_function=model_netStat_friendship_KHEx)
gof_inst_friendship_KHEx_f.gof_run(num_sim=300, exchange_iter=200)
gof_inst_friendship_KHEx_f.show_boxplot(compare=True)
gof_inst_friendship_KHEx_f.save_boxplot("/example_results_friendship_20210722/friendship", compare=True)
gof_inst_friendship_KHEx_d = BSTERGM_GOF(model_netStat_friendship_KHEx, reader_inst_friendship_KHEx.MC_dissolution_samples,
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0]])
test_structure3 = np.array([[0, 0, 1, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1, 1, 0]])
test_initnet1 = DirectedNetwork(test_structure1)
test_initnet2 = DirectedNetwork(test_structure2)
test_initnet3 = DirectedNetwork(test_structure3)
test_obs_seq = [test_initnet1, test_initnet2, test_initnet3]
def model_netStat(network):
model = []
#define model
model.append(network.statCal_edgeNum())
model.append(network.statCal_geoWeightedESP(0.5))
return np.array(model)
def gof_additional_netStat(network):
gof_netstat = []
gof_netstat.append(network.statCal_edgeNum())
gof_netstat.append(network.statCal_geoWeightedESP(0.5))