def _unpack_jacobian_out(self,
raw_jac: Any) -> Dict[Variable, VariableData]:
jac = {}
for v0, vjac in nested_filter(is_variable, self.factor_out, raw_jac):
jac[v0] = VariableData()
for v1, j in zip(self.args, vjac):
jac[v0][v1] = j
return jac
def _factor_value(self, raw_fval) -> FactorValue:
"""Converts the raw output of the factor into a `FactorValue`
where the values of the deterministic values are stored in a dict
attribute `FactorValue.deterministic_values`
"""
det_values = VariableData(
nested_filter(is_variable, self.factor_out, raw_fval))
fval = det_values.pop(FactorValue, 0.0)
return FactorValue(fval, det_values)
def calc_exact_update(self, mean_field) -> "MeanField":
if self._calc_exact_update:
from autofit.graphical.mean_field import MeanField
projection = self._calc_exact_update(
*self.resolve_args_and_out(mean_field))
return MeanField(
nested_filter(is_variable, self.args + (self.factor_out, ),
projection))
else:
raise NotImplementedError
def out_variables(self):
return set(v[0] for v in nested_filter(is_variable, self.factor_out))
def deterministic_variables(self) -> Set[Variable]:
deterministic_variables = set(
v for v, in nested_filter(is_variable, self.factor_out))
deterministic_variables.discard(FactorValue)
return frozenset(deterministic_variables)