new version of CLP(BN) with EM learning

CLPBN/learning/learn_utils.yap

@@ -2,13 +2,31 @@
 % Utilities for learning
 %
 
-:- module(bnt_learn_utils, [run_all/1,
-			    clpbn_vars/2]).
+:- module(clpbn_learn_utils, [run_all/1,
+			      clpbn_vars/2,
+			      normalise_counts/2,
+			      compute_likelihood/3]).
+
+:- use_module(library(matrix),
+	      [matrix_agg_lines/3,
+	       matrix_op_to_lines/4,
+	       matrix_to_logs/2,
+	       matrix_op/4,
+	       matrix_sum/2]).
+
+:- meta_predicate run_all(:).
 
 run_all([]).
 run_all([G|Gs]) :-
-	call(user:G),
+	call(G),
 	run_all(Gs).
+run_all(M:Gs) :-
+	run_all(Gs,M).
+
+run_all([],_).
+run_all([G|Gs],M) :-
+	call(M:G),
+	run_all(Gs,M).
 
 clpbn_vars(Vs,BVars) :-
 	get_clpbn_vars(Vs,CVs),
@@ -31,5 +49,15 @@ get_var_has_same_key([K-V|KVs],K,V,KVs0)  :- !,
 	get_var_has_same_key(KVs,K,V,KVs0).
 get_var_has_same_key(KVs,_,_,KVs).
 
+normalise_counts(MAT,NMAT) :-
+	matrix_agg_lines(MAT, +, Sum),
+	matrix_op_to_lines(MAT, Sum, /, NMAT).
+
+compute_likelihood(Table0, NewTable, DeltaLik) :-
+	matrix:matrix_to_list(Table0,L0), writeln(L0),
+	matrix:matrix_to_list(NewTable,L1), writeln(L1),
+	matrix_to_logs(NewTable, Logs),
+	matrix_op(Table0, Logs, *, Logs),
+	matrix_sum(Logs, DeltaLik).