% % This module defines PFL, the prolog factor language. % % :- module(pfl, [ factor/6, skolem/2, defined_in_factor/2, get_pfl_parameters/2, % given id return par factor parameter new_pfl_parameters/2, % given id set new parameters get_first_pvariable/2, % given id get firt pvar (useful in bayesian) get_factor_pvariable/2, % given id get any pvar op(550,yfx,@), op(550,yfx,::), op(1150,fx,bayes), op(1150,fx,markov)]). :- use_module(library(lists), [nth0/3, append/3, member/2]). :- dynamic factor/6, skolem_in/2, skolem/2, preprocess/3, evidence/2, id/1. :- reexport(library(clpbn), [clpbn_flag/2 as pfl_flag, set_clpbn_flag/2 as set_pfl_flag]). :- reexport(library(clpbn/horus), [set_solver/1]). :- ( % if clp(bn) has done loading, we're top-level predicate_property(set_pfl_flag(_,_), imported_from(clpbn)) -> % we're using factor language % set appropriate flag set_pfl_flag(use_factors,on) ; % we're within clp(bn), no need to do anything true ). user:term_expansion( bayes((Formula ; Phi ; Constraints)), pfl:factor(bayes,Id,FList,FV,Phi,Constraints)) :- !, term_variables(Formula, FreeVars), FV =.. [''|FreeVars], new_id(Id), process_args(Formula, Id, 0, _, FList, []). user:term_expansion( markov((Formula ; Phi ; Constraints)), pfl:factor(markov,Id,FList,FV,Phi,Constraints)) :- !, term_variables(Formula, FreeVars), FV =.. [''|FreeVars], new_id(Id), process_args(Formula, Id, 0, _, FList, []). user:term_expansion( Id@N, L ) :- atom(Id), number(N), !, N1 is N + 1, findall(G,generate_entity(1, N1, Id, G), L). user:term_expansion( Goal, [] ) :- preprocess(Goal, Sk,Var), !, (ground(Goal) -> true ; throw(error('non ground evidence',Goal))), % prolog_load_context(module, M), assert(pfl:evidence(Sk,Var)). Id@N :- generate_entity(0, N, Id, G), assert_static(user:G), fail. _Id@_N. defined_in_factor(Key, Factor) :- skolem_in(Key, Id), factor(bayes, Id, [Key|FList], FV, Phi, Constraints), !, Factor = factor(bayes, Id, [Key|FList], FV, Phi, Constraints). defined_in_factor(Key, Factor) :- skolem_in(Key, Id), factor(markov, Id, FList, FV, Phi, Constraints), member(Key, FList), Factor = factor(markov, Id, FList, FV, Phi, Constraints). generate_entity(N, N, _, _) :- !. generate_entity(I0, _N, Id, T) :- atomic_concat(p, I0, P), T =.. [Id, P]. generate_entity(I0, N, Id, T) :- I is I0+1, generate_entity(I, N, Id, T). id(0). new_id(Id) :- retract(id(Id0)), Id is Id0+1, assert(id(Id)). process_args((Arg1,Arg2), Id, I0, I ) --> !, process_args(Arg1, Id, I0, I1), process_args(Arg2, Id, I1, I). process_args(Arg1, Id, I0, I ) --> { I is I0+1 }, process_arg(Arg1, Id, I). process_arg(Sk::D, Id, _I) --> !, { new_skolem(Sk,D), assert(skolem_in(Sk, Id)) }, [Sk]. process_arg(Sk, Id, _I) --> !, { % if :: been used before for this skolem % just keep on using it, % otherwise, assume it is t,f ( \+ \+ skolem(Sk,_D) -> true ; new_skolem(Sk,[t,f]) ), assert(skolem_in(Sk, Id)) }, [Sk]. new_skolem(Sk,D) :- copy_term(Sk, Sk1), skolem(Sk1, D1), Sk1 =@= Sk, !, ( D1 = D -> true ; throw(pfl(permission_error(redefining_domain(Sk),D:D1)))). new_skolem(Sk,D) :- interface_predicate(Sk), assert(skolem(Sk, D)). interface_predicate(Sk) :- Sk =.. SKAs, append(SKAs, [Var], ESKAs), ESk =.. ESKAs, assert(preprocess(ESk, Sk, Var)), % transform from PFL to CLP(BN) call assert_static((user:ESk :- evidence(Sk,Ev) -> Ev = Var; var(Var) -> insert_atts(Var,Sk) ; add_evidence(Sk,Var) ) ). insert_atts(Var,Sk) :- clpbn:put_atts(Var,[key(Sk)]). add_evidence(Sk,Var) :- skolem(Sk,D), once(nth0(E,D,Var)), clpbn:put_atts(_V,[key(Sk),evidence(E)]). get_pfl_parameters(Id,Out) :- factor(_Type,Id,_FList,_FV,Phi,_Constraints), %writeln(factor(_Type,Id,_FList,_FV,_Phi,_Constraints)), ( is_list(Phi) -> Out = Phi ; call(user:Phi, Out) ). new_pfl_parameters(Id, NewPhi) :- retract(factor(Type,Id,FList,FV,_Phi,Constraints)), assert(factor(Type,Id,FList,FV,NewPhi,Constraints)), fail. new_pfl_parameters(_Id, _NewPhi). get_pfl_factor_sizes(Id, DSizes) :- factor(_Type, Id, FList, _FV, _Phi, _Constraints), get_sizes(FList, DSizes). get_sizes([], []). get_sizes(Key.FList, Sz.DSizes) :- skolem(Key, Domain), length(Domain, Sz), get_sizes(FList, DSizes). % only makes sense for bayesian networks get_first_pvariable(Id,Var) :- factor(_Type, Id,Var._FList,_FV,_Phi,_Constraints). % only makes sense for bayesian networks get_factor_pvariable(Id,Var) :- factor(_Type, Id,FList,_FV,_Phi,_Constraints), member(Var, FList).