result = []
for i in range(t):
# i = t - 1
g, rvs_table = kmf.grounded_graph(i + 1, data)
bp = HybridLBP(g, n=50, proposal_approximation='simple')
# bp = GaLBP(g)
print('number of vr', len(g.rvs))
num_evidence = 0
for rv in g.rvs:
if rv.value is not None:
num_evidence += 1
print('number of evidence', num_evidence)
start_time = time.time()
bp.run(6, c2f=0, log_enable=False)
print('time lapse', time.time() - start_time)
# for i in range(t):
temp = []
for idx, rv in enumerate(rvs_table[i]):
temp.append([idx, bp.map(rv)])
result.append(temp)
result = np.array(result)
np.save('Data/well_t_prediction', result)
for idx in range(result.shape[1]):
y = []
for i in range(t):
from VarInference import VarInference as VI
from LiftedVarInference import VarInference as LVI
from C2FVarInference import VarInference as C2FVI
from GaBP import GaBP
from HybridMaxWalkSAT import HybridMaxWalkSAT as HMWS
from HybridLBPLogVersion import HybridLBP
rel_g = generate_rel_graph()
rel_g.ground_graph()
data = {('recession', 'all'): 25}
g, rvs_dict = rel_g.add_evidence(data)
infer = HybridLBP(g, n=10, proposal_approximation='simple')
infer.run(10, c2f=0, log_enable=False)
# infer = HMWS(g)
# infer.run(max_tries=1, max_flips=10000, epsilon=0.0, noise_std=0.5)
# infer = LVI(g, num_mixtures=1, num_quadrature_points=3)
# infer.run(200, lr=0.2)
# infer = GaBP(g)
# infer.run(20)
map_res = dict()
for key, rv in rvs_dict.items():
map_res[rv] = infer.map(rv)
print(key, map_res[rv])