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yap-6.3/packages/CLPBN/pfl.yap
Vítor Santos Costa c5f42cd7eb more pfl changes.
2012-04-12 17:24:39 +01:00

183 lines
4.3 KiB
Prolog

%
% 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/5, 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) -->
!,
{
assert(skolem_in(Sk, Id))
},
[Sk].
new_skolem(Sk,D) :-
copy_term(Sk, Sk1),
skolem(Sk1, D1),
Sk1 =@= Sk,
!,
D1 = D.
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)),
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).