reasoning under uncertainty unrolling dbns probdbn py -(continued class bnfromdbn(beliefnetwork)"""belief network unrolled from dynamic belief network "" def __init__(self,dbn,horizon)"""dbn is the dynamic belief network being unrolled horizon> is the number of steps (so there will be horizon+ variables for each dbn variable ""self name var {var basename[dbnvariable(var basename,var domain,indexfor index in range(horizon+ )for var in dbn vars_nowself display( , "name var={self name var}"variables { for vs in self name var values(for in vsself display( , "variables={variables}"bnfactors {cpdrename(fac,{self name var[var basename][ ]:var for var in fac variables}for fac in dbn init_factorsbnfactors |{cpdrename(fac,dict_union({self name var[var basename][ ]:var for var in fac variables if var index=='prev'{self name var[var basename][ + ]:var for var in fac variables if var index=='now'})for fac in dbn transition_factors for in range(horizon)self display( , "bnfactors={bnfactors}"beliefnetwork __init__(selfdbn titlevariablesbnfactorshere are two examples note that we need to use bn name var[' '][ to get the variable ( at time probdbn py -(continued try #from probrc import probrc #bn bnfromdbn(dbn , construct belief network #drc probrc(bninitialize recursive conditioning # bn name var[' '][ #drc query( # ( #drc query(bn name var[' '][ ],{bn name var[' '][ ]:true,bn name var[' '][ ]:false}# ( | , dbn filtering if we only wanted to ask questions about the current statewe can save space by forgetting the history variables version june