def fg_inference(compile_fg):
print('----------------------------------------------------------------')
print('Weight inference')
print('----------------------------------------------------------------')
weight, variable, factor, ftv, domain_mask, n_edges = compile_fg
fg = NumbSkull(
n_inference_epoch=1000,
n_learning_epoch=1000,
stepsize=0.01,
decay=0.95,
reg_param=1e-6,
regularization=2,
truncation=10,
quiet=(not False),
verbose=False,
learn_non_evidence=False, # need to test
sample_evidence=False,
burn_in=10,
nthreads=1)
fg.loadFactorGraph(weight, variable, factor, ftv, domain_mask, n_edges)
fg.inference(out=True)
for i in range(len(variable)):
if fg.factorGraphs[0].marginals[i] > 0.5:
variable[i]['initialValue'] = 1
else:
variable[i]['initialValue'] = 0
weight_value = fg.factorGraphs[0].weight_value[0]
return weight_value
def marginals(self,
V,
cardinality,
L,
L_offset,
deps=(),
init_acc=1.0,
init_deps=1.0,
init_class_prior=-1.0,
epochs=100,
step_size=None,
decay=0.99,
verbose=False,
burn_in=50,
timer=None):
if self.weights is None:
raise ValueError(
"Must fit model with train() before computing marginal probabilities."
)
y = None
weight, variable, factor, ftv, domain_mask, n_edges = self._compile(
V, cardinality, L, L_offset, y, deps, self.weights,
self.dep_weights)
fg = NumbSkull(n_inference_epoch=epochs,
n_learning_epoch=0,
stepsize=step_size,
decay=decay,
quiet=(not verbose),
verbose=verbose,
learn_non_evidence=True,
burn_in=burn_in,
sample_evidence=False)
fg.loadFactorGraph(weight, variable, factor, ftv, domain_mask, n_edges)
fg.inference(out=False)
marginals = fg.factorGraphs[0].marginals[:V.shape[0]]
return marginals
nthreads=1)
subgraph = weight, variable, factor, fmap, domain_mask, edges
ns_learing.loadFactorGraph(*subgraph)
# 因子图参数学习
ns_learing.learning()
# 因子图推理
# 参数学习完成后将weight的isfixed属性置为true
for index, w in enumerate(weight):
w["isFixed"] = True
w['initialValue'] = ns_learing.factorGraphs[0].weight[index][
'initialValue']
ns_inference = NumbSkull(
n_inference_epoch=1000,
n_learning_epoch=1000,
stepsize=0.001,
decay=0.95,
reg_param=1e-6,
regularization=2,
truncation=10,
quiet=(not False),
verbose=False,
learn_non_evidence=False, # need to test
sample_evidence=False,
burn_in=10,
nthreads=1)
ns_inference.loadFactorGraph(*subgraph)
# 因子图推理
ns_inference.inference()
#获取变量推理结果
print(ns_inference.factorGraphs[0].marginals)
