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yap-6.3/packages/ProbLog/problog/lbdd.yap
Vitor Santos Costa e8d9e71a4e lbfgs
2019-03-17 23:01:48 +00:00

155 lines
4.4 KiB
Prolog

%========================================================================
%=
%=
%=
%========================================================================
/**
* @file problog/lbdd.yap
* support routines for BDD evaluation.
*
*/
%========================================================================
%= Updates all values of query_probability/2 and query_gradient/4
%= should be called always before these predicates are accessed
%= if the old values are still valid, nothing happens
%========================================================================
update_values :-
values_correct,
!.
update_values :-
\+ values_correct,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% delete old values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
retractall(query_probability_intern(_,_)),
retractall(query_gradient_intern(_,_,_,_)),
assertz(values_correct).
update_query_cleanup(QueryID) :-
(
(query_is_similar(QueryID,_) ; query_is_similar(_,QueryID))
->
% either this query is similar to another or vice versa,
% therefore we don't delete anything
true;
retractall(query_gradient_intern(QueryID,_,_,_))
).
update_query(QueryID,Symbol,What_To_Update) :-
(
query_is_similar(QueryID,_)
->
% we don't have to evaluate the BDD
format_learning(4,'#',[]);
(
problog_flag(sigmoid_slope,Slope),
((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'),
gradient(QueryID, Method, Slope),
format_learning(4,'~w',[Symbol])
)
).
maplist_to_hash([], H0, H0).
maplist_to_hash([I-V|MapList], H0, Hash) :-
rb_insert(H0, V, I, H1),
maplist_to_hash(MapList, H1, Hash).
prob2log(_X,Slope,FactID,V) :-
get_fact_probability(FactID, V0),
inv_sigmoid(V0, Slope, V).
log2prob(X,Slope,FactID,V) :-
V0 <== X[FactID],
sigmoid(V0, Slope, V).
bind_maplist([], _Slope, _X).
bind_maplist([Node-Pr|MapList], Slope, X) :-
Pr <== X[Node],
bind_maplist(MapList, Slope, X).
tree_to_grad([], _, Grad, Grad).
tree_to_grad([Node|Tree], H, Grad0, Grad) :-
node_to_gradient_node(Node, H, GNode),
tree_to_grad(Tree, H, [GNode|Grad0], Grad).
%get_prob(Node, Prob) :-
% query_probability(Node,Prob), !.
get_prob(Node, Prob) :-
get_fact_probability(Node,Prob).
gradient(QueryID, l, Slope) :-
probability( QueryID, Slope, Prob),
assert(query_probability_intern(QueryID,Prob)),
fail.
gradient(_QueryID, l, _).
/* query_probability(21,6.775948e-01). */
gradient(QueryID, g, Slope) :-
recorded(QueryID, BDD, _),
query_gradients(BDD,Slope,I,Grad),
% writeln(grad(QueryID:I:Grad)),
assert(query_gradient_intern(QueryID,I,p,Grad)),
fail.
gradient(QueryID, g, Slope) :-
gradient(QueryID, l, Slope).
query_probability( DBDD, Slope, X, Prob) :-
DBDD = bdd(Dir, Tree, MapList),
bind_maplist(MapList, Slope, X),
run_sp(Tree, Slope, 1.0, Prob0),
(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0).
query_gradients(bdd(Dir, Tree, MapList),Slope,X,I,Grad) :-
bind_maplist(MapList, Slope, X),
member(I-_, MapList),
run_grad(Tree, I, Slope, 0.0, Grad0),
( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :-
rb_lookup(X,Id,H),
(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
rb_lookup(X,Id,H),
(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
run_sp([], _, P0, P0).
run_sp(gnodep(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
EP = 1.0 / (1.0 + exp(-X * Slope) ),
P is EP*PL+ (1.0-EP)*PR,
run_sp(Tree, Slope, P, PF).
run_sp(gnoden(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
EP is 1.0 / (1.0 + exp(-X * Slope) ),
P is EP*PL + (1.0-EP)*(1.0 - PR),
run_sp(Tree, Slope, P, PF).
run_grad([], _I, _, G0, G0).
run_grad([gnodep(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
EP is 1.0/(1.0 + exp(-X * Slope)),
P is EP*PL+ (1.0-EP)*PR,
G0 is EP*GL + (1.0-EP)*GR,
% don' t forget the -X
( I == Id -> G is G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ),
run_grad(Tree, I, Slope, G, GF).
run_grad([gnoden(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
EP is 1.0 / (1.0 + exp(-X * Slope) ),
P is EP*PL + (1.0-EP)*(1.0 - PR),
G0 is EP*GL - (1.0 - EP) * GR,
( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ),
run_grad(Tree, I, Slope, G, GF).