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Currently, dependencies of stochastic next functions can only be discrete states. In principle, we want to allow for arbitrary arguments. There are a few things that need to be tackled. The current implementation (create_params.py) does:
dependencies=list(
inspect.signature(model["functions"][f"next_{var}"]).parameters,
)
_check_variables_are_all_discrete_states(
variables=dependencies,
variable_info=variable_info,
msg_suffix=(
f"The function next_{var} can only depend on discrete state variables."
),
)
What we probably should do:
We don't need the list of arguments here, but the root nodes in a dag that contains the next function and all auxiliary functions the user might have provided. Another workaround would be to build a next function that only depends on states and choices via dags and inspects its signature.
What remains to be done:
Extend the logic to continuous variables.
The text was updated successfully, but these errors were encountered:
Currently, dependencies of stochastic next functions can only be discrete states. In principle, we want to allow for arbitrary arguments. There are a few things that need to be tackled. The current implementation (
create_params.py
) does:What we probably should do:
We don't need the list of arguments here, but the root nodes in a dag that contains the next function and all auxiliary functions the user might have provided. Another workaround would be to build a next function that only depends on states and choices via dags and inspects its signature.
What remains to be done:
Extend the logic to continuous variables.
The text was updated successfully, but these errors were encountered: