bfgs

packages/ProbLog/problog/lbdd.yap

@@ -79,10 +79,7 @@ bind_maplist([Node-(Node-Pr)|MapList], Slope, X) :-
 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). */

packages/ProbLog/problog_examples/learn_graph_lbdd.pl

@@ -17,7 +17,7 @@
 :- use_module('../problog_lbfgs').
 
 
- :- if(true).
+ :- if(false).
 
  :- use_module('kbgraph').
 

packages/ProbLog/problog_lbfgs.yap

@@ -238,7 +238,7 @@
 :- dynamic(values_correct/0).
 :- dynamic(learning_initialized/0).
 :- dynamic(current_iteration/1).
-:- dynamic(solver_iteration/1).
+:- dynamic(solver_iterations/2).
 :- dynamic(example_count/1).
 :- dynamic(query_probability_intern/2).
 %:- dynamic(query_gradient_intern/4).
@@ -263,28 +263,15 @@ user:test_example(A,B,C,=) :-
 	user:test_example(A,B,C),
 	\+  user:problog_discard_example(B).
 
-solver_iteration(0).
+solver_iterations(0,0).
 
 %========================================================================
 %= store the facts with the learned probabilities to a file
 %========================================================================
 
-save_model(X):-
+save_model:-
-    problog_flag(sigmoid_slope,Slope),
+ 	current_iteration(Id),
-	current_iteration(Iteration),
+	create_factprobs_file_name(Id,Filename),			export_facts(Filename).
-	solver_iteration(LBFGSIteration),
-	Id is Iteration*100+LBFGSIteration,
-	create_factprobs_file_name(Id,Filename),
-	retractall( query_probability_intern(_,_)),
-	forall(
-	    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)))
-	),
-	export_facts(Filename).
 
 
 
@@ -900,19 +887,44 @@ 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]).
-user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N, LBFGSIteration,Ls,0) :-
+user:progress(FX,X,G,X_Norm,G_Norm,Step,_N, LBFGSIteration,Ls,0) :-
      problog_flag(sigmoid_slope,Slope),
-    forall(
+     save_state(X, Slope, G),
-    tunable_fact(FactID,_GroundTruth), 		set_tunable(FactID,Slope,X)),
     logger_set_variable(mse_trainingset, FX),
-    retractall(solver_iterations(_)),
+    (retract(solver_iterations(SI,_)) -> true ; SI = 0),
-    assert(solver_iterations(LBFGSIteration)),
+    (retract(current_iteration(TI)) -> true ; TI = 0),
-    save_model(X),
+    SI1 is SI+1,
+    TI1 is TI+1,
+   assert(current_iteration(TI1)),
+    assert(solver_iterations(SI1,LBFGSIteration)),
+    save_model,
     X0 <== X[0], sigmoid(X0,Slope,P0),
     X1 <== X[1], sigmoid(X1,Slope,P1),
     format('~d. Iteration : (x0,x1)=(~4f,~4f)  f(X)=~4f  |X|=~4f  |X\'|=~4f  Step=~4f  Ls=~4f~n',[LBFGSIteration,P0,P1,FX,X_Norm,G_Norm,Step,Ls]).
 
 
+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, _).
+
 %========================================================================
 %= initialize the logger module and set the flags for learning
 %= don't change anything here! use set_problog_flag/2 instead