bbdd

2019-03-15 12:38:09 +00:00
parent e3c399af48
commit 044329d115
12 changed files with 250 additions and 344 deletions
--- a/packages/ProbLog/problog/lbdd.yap
+++ b/packages/ProbLog/problog/lbdd.yap
@@ -0,0 +1,146 @@
+
+%========================================================================
+%=
+%=
+%=
+%========================================================================
+
+/**
+ * @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).
+
+bind_maplist([]).
+bind_maplist([Node-Theta|MapList]) :-
+	get_prob(Node, ProbFact),
+	inv_sigmoid(ProbFact, Theta),
+	bind_maplist(MapList).
+
+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, Prob) :-
+    DBDD = bdd(Dir, Tree, MapList),
+    bind_maplist(MapList),
+    run_sp(Tree, Slope, 1.0, Prob0),
+    (Dir == 1 -> Prob0 = Prob ;  Prob is 1.0-Prob0).
+
+    
+query_gradients(bdd(Dir, Tree, MapList),Slope,I,Grad) :-
+	bind_maplist(MapList),
+        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).
+
+