reasoning under uncertainty probdbn py -(continued class factorrename(factor)def __init__(self,fac,renaming)""" renamed factor fac is factor renaming is dictionary of the form {new:oldwhere old and new var variableswhere the variables in fac appear exactly once in the renaming ""factor __init__(self,[ for ( ,oin renaming items(if in fac variables]self orig_fac fac self renaming renaming def get_value(self,assignment)return self orig_fac get_value({self renaming[var]:val for (var,valin assignment items(if var in self variables}the following class renames the variables of conditional probability distribution it is used for template models ( dynamic decision networks or relational modelsprobdbn py -(continued class cpdrename(factorrenamecpd)def __init__(selfcpdrenaming)renaming_inverse {old:new for (new,oldin renaming items()cpd __init__(self,renaming_inverse[cpd child],[renaming_inverse[pfor in cpd parents]self orig_fac cpd self renaming renaming probdbn py -(continued class dbn(displayable)"""the class of stationary dynamic belief networks name is the dbn name vars_now is list of current variables (each must have previous variabletransition_factors is list of factors for ( |parentswhere is current variable and parents is list of current or previous variables init_factors is list of factors for ( |parentswhere is current variable and parents can only include current variables the graph of transition factors init factors must be acyclic ""def __init__(selftitlevars_nowtransition_factors=noneinit_factors=none)self title title self vars_now vars_now version june