Problog testing: modules :(

packages/ProbLog/problog.yap

@@ -845,7 +845,7 @@ term_expansion_intern(Head :: Goal,Module,problog:ProbFact) :-
 
 % handles probabilistic facts
 term_expansion_intern(P :: Goal,Module,problog:ProbFact) :-
-	copy_term((P,Goal),(P_Copy,Goal_Copy)),
+    copy_term((P,Goal),(P_Copy,Goal_Copy)),
 	functor(Goal, Name, Arity),
 	atomic_concat([problog_,Name],ProblogName),
 	Goal =.. [Name|Args],

packages/ProbLog/problog/lbdd.yap

@@ -91,6 +91,7 @@ gradient(QueryID, g, Slope) :-
 query_probabilities( DBDD, Prob) :-
     DBDD = bdd(Dir, Tree, _MapList),
     findall(P, evalp(Tree,P), [Prob0]),
+   % nonvar(Prob0),
     (Dir == 1 -> Prob0 = Prob ;  Prob is 1.0-Prob0).
 
 evalp( Tree, Prob0) :-
@@ -104,7 +105,7 @@ query_gradients(bdd(Dir, Tree, MapList),I,IProb,Grad) :-
 
 evalp( pn(P, _-X, PL, PR), _,P ):-
     P is X*PL+ (1.0-X)*(1.0-PR).
-evalp( pp(P, _-X, PL, PR), _,P ):-
+evalp( pp(P, _-X, PL, PR), _,P ):-  
     P is X*PL+ (1.0-X)*PR.
 
 evalg( I, pp(P-G, J-X, L, R), _, G ):-

packages/ProbLog/problog_examples/learn_graph_lbdd.pl

@@ -14,20 +14,9 @@
 % will run 20 iterations of learning with default settings
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-:- use_module('../problog_lbfgs').
+:- use_module('../problog_learning_lbdd').
 
 
- :- if(true).
-
- :- use_module('kbgraph').
-
-
-%%%%
-% background knowledge
-%%%% 
-% definition of acyclic path using list of visited nodes
-
-:- else. 
 
 :- Query=path(X,Y), set_problog_flag(init_method,(Query,K,Bdd,problog:problog_exact_lbdd(Query,Bdd))).
 
@@ -48,7 +37,6 @@ edge(X,Y) :- dir_edge(X,Y).
 absent(_,[]).
 absent(X,[Y|Z]):-X \= Y, absent(X,Z).
 
-:- endif.
 
 %%%%
 % probabilistic facts 

packages/ProbLog/problog_lbfgs.yap

@@ -553,7 +553,7 @@ empty_bdd_directory.
 init_queries :-
     empty_bdd_directory,
 	format_learning(2,'Build BDDs for examples~n',[]),
-	forall(user:test_example(ID,Query,_Prob,_),init_one_query(ID,Query,test)),	
+	forall(user:test_example(ID,Query,_Prob,_),init_one_query(ID,Query,test)),
 	forall(user:example(ID,Query,_Prob,_),init_one_query(ID,Query,training)).
 
 bdd_input_file(Filename) :-
@@ -835,7 +835,7 @@ update_values :-
 	% delete old values
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 	retractall(query_probability_intern(_,_)),
-	retractall(query_gradient_intern(_,_,_,_)),	
+	retractall(query_gradient_intern(_,_,_,_)),
 
 
 	assertz(values_correct).
@@ -847,7 +847,7 @@ update_values :-
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % start calculate gradient
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :- 
+user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
     %Handle = user_error,
     N1 is N-1,
     forall(between(0,N1,I),(Grad[I]<==0.0)),
@@ -893,13 +893,11 @@ compute_gradient( Grad, X, Slope, LL) :-
 	BDD = bdd(_,_,MapList),
 	MapList = [_|_],
 	bind_maplist(MapList, Slope, X),
-%writeln(QueryID:MapList),
 	query_probabilities( BDD, BDDProb),
 	(isnan(BDDProb) -> writeln((nan::QueryID)), fail;true),
-writeln(BDDProb),
 	LL is (BDDProb-QueryProb)*(BDDProb-QueryProb),
     forall(
-	query_gradients(BDD,I,IProb,GradValue), 
+	query_gradients(BDD,I,IProb,GradValue),
 	gradient_pair(BDDProb, QueryProb, Grad, GradValue, I, IProb)
     ).
 
@@ -925,8 +923,9 @@ wrap( _X, _Grad, _GradCount).
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 user:progress(FX,_X,_G, _X_Norm,_G_Norm,_Step,_N,_CurrentIteration,_Ls,-1) :-
     FX < 0, !,
-    format('stopped on bad FX=~4f~n',[FX]).
+    format('Bad FX=~4f~n',[FX]).
 user:progress(FX,X,G,X_Norm,G_Norm,Step,_N, LBFGSIteration,Ls,0) :-
+writeln(fx=FX),
      problog_flag(sigmoid_slope,Slope),
      save_state(X, Slope, G),
     logger_set_variable(mse_trainingset, FX),
@@ -946,22 +945,6 @@ save_state(X,Slope,_Grad) :-
     	tunable_fact(FactID,_GroundTruth),
     	set_tunable(FactID,Slope,X),
     	fail.
-save_state(X, Slope, _) :-
-	    user:example(QueryID,_Query,_QueryProb),
-	    recorded(QueryID,BDD,_),
-	    BDD = bdd(_,_,MapList),
-	    bind_maplist(MapList, Slope, X),
-	    query_probabilities( BDD, BDDProb),
-	    assert( query_probability_intern(QueryID,BDDProb)),
-	fail.
-save_state(X, Slope, _) :-
-	    user:test_example(QueryID,_Query,_QueryProb),
-	    recorded(QueryID,BDD,_),
-	    BDD = bdd(_,_,MapList),
-	    bind_maplist(MapList, Slope, X),
-	    query_probabilities( BDD, BDDProb),
-	    assert( query_probability_intern(QueryID,BDDProb)),
-	fail.
 save_state(_X, _Slope, _).
 
 %========================================================================

packages/ProbLog/problog_learning.yap

@@ -710,7 +710,7 @@ update_values :-
 %=
 %========================================================================
 
-    listing(
+ update_query_cleanup(QueryID) :-
 	(
 	 (query_is_similar(QueryID,_) ; query_is_similar(_,QueryID))
 	->
@@ -893,7 +893,6 @@ ground_truth_difference :-
 %=
 %= -Float
 %========================================================================
-
 mse_trainingset_only_for_linesearch(MSE) :-
 	update_values,
 

packages/ProbLog/problog_learning_lbdd.yap

@@ -228,6 +228,7 @@
 :- use_module('problog/utils_lbdd').
 :- use_module('problog/utils').
 :- use_module('problog/tabling').
+:- use_module('problog/lbdd').
 
 % used to indicate the state of the system
 :- dynamic(values_correct/0).