def reduce_model(model, evidence):
model = copy.deepcopy(model)
# continuous_factors = [factor for factor in model.factors if isinstance(factor, ContinuousFactor)]
for var, val in evidence.items():
for factor in model.factors:
if var in factor.scope(
): # and "F(" in var: # make sure that we only reduce at this stage for continuous values, let the inference algorithm deal with reducing for binary variables
try:
factor.reduce([(var, val)])
except ValueError as e:
print(factor)
raise e
new_model = FactorGraph()
additional_evidence = {}
for node in model.factors:
if isinstance(node, ContinuousFactor):
if len(node.scope()) == 1:
node = TabularCPD(str(node.scope()[0]), 2,
[[node.assignment(0),
node.assignment(1)]]).to_factor()
if len(node.scope()) == 0:
continue
# try:
# var = node.variable
# except:
# print(node.scope())
# for v in node.scope():
# if var != v:
# new_model.add_edge(str(v), str(var))
# if "same_reason" in var:
# additional_evidence[var] = 1
new_model.add_nodes_from([str(n) for n in node.scope()])
new_model.add_factors(node)
return new_model, additional_evidence
def reduce_model(self, evidence):
model = copy.deepcopy(self.model)
continuous_factors = [
factor for factor in model.factors
if isinstance(factor, ContinuousFactor)
]
for var, val in evidence.items():
for factor in continuous_factors:
if var in factor.scope(
) and "F(" in var: # make sure that we only reduce at this stage for continuous values, let the inference algorithm deal with reducing for binary variables
factor.reduce([(var, val)])
new_model = BayesianModel()
additional_evidence = {}
for node in model.factors:
if isinstance(node, ContinuousFactor):
if len(node.scope()) == 1:
node = TabularCPD(
str(node.scope()[0]), 2,
[[node.assignment(0),
node.assignment(1)]])
else:
node = to_CPD(node)
var = node.variable
for v in node.scope():
if var != v:
new_model.add_edge(str(v), str(var))
if "same_reason" in var:
additional_evidence[var] = 1
new_model.add_nodes_from([str(n) for n in node.scope()])
new_model.add_cpds(node)
return new_model, additional_evidence
