more support for learning

CLPBN/clpbn.yap

@@ -91,7 +91,7 @@ em_solver(vel).
 %output(gviz(user_error)).
 output(no).
 suppress_attribute_display(false).
-parameter_softening(laplace).
+parameter_softening(m_estimate(10)).
 
 clpbn_flag(Flag,Option) :-
 	clpbn_flag(Flag, Option, Option).

CLPBN/clpbn/dists.yap

@@ -12,7 +12,6 @@
 	   get_dist_matrix/5,
 	   get_possibly_deterministic_dist_matrix/5,
 	   get_dist_domain/2,
-	   get_dist_params/2,
 	   get_dist_domain_size/2,
 	   get_dist_params/2,
 	   get_dist_key/2,

CLPBN/clpbn/table.yap

@@ -100,10 +100,10 @@ clpbn_tabled_clause(Head, Body) :-
 	clpbn_tabled_clause(Head, M, Body).
 
 clpbn_tabled_clause(M:Head, _, Body) :- !,
-	clpbn_table(Head, M, Body).
+	clpbn_tabled_clause(Head, M, Body).
 clpbn_tabled_clause(Head, M, Body) :-
 	clpbn_table(Head, M, THead),
-	clause(THead, Body).
+	clause(M:THead, Body).
 
 
 clpbn_tabled_assertz(M:Clause) :- !,

CLPBN/learning/aleph_params.yap

@@ -65,7 +65,7 @@
 
 :- user:set(record_testclause_hook, clpbn_aleph:do_nothing).
 
-%:- user:set(newbest_hook, clpbn_aleph:store_theory).
+:- user:set(newbest_hook, clpbn_aleph:store_theory).
 
 disable_solver(_) :-
 	clpbn_flag(solver, Old, none),
@@ -109,15 +109,17 @@ store_theory(_).
 
 add_correct_cpt((G,B),(G,NB)) :- !,
 	add_correct_cpt(B,NB).
+add_correct_cpt((clpbn:{V = K with Tab }), ({V = K with NTab})) :-
+	correct_tab(Tab,K,NTab).
 add_correct_cpt(({V = K with Tab }), ({V = K with NTab})) :-
 	correct_tab(Tab,K,NTab).
 
 correct_tab(p(Vs,_),K,p(Vs,TDist)) :-
 	get_dist_key(Id, K),
-	get_dist_parms(Id, TDist).
+	get_dist_params(Id, TDist).
 correct_tab(p(Vs,_,Ps),K,p(Vs,TDist,Ps)) :-
 	get_dist_key(Id, K),
-	get_dist_parms(Id, TDist).
+	get_dist_params(Id, TDist).
 
 store_cl(Cl) :-
 	recordz(best_theory, Cl, _).

CLPBN/learning/em.yap

@@ -90,7 +90,7 @@ init_em(Items, state( AllDists, AllDistInstances, MargVars, SolverVars)) :-
 em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF, FTables) :-
 	estimate(State, LPs),
 	maximise(State, Tables, LPs, Likelihood),
-	writeln(Likelihood:Its:Likelihood0:Tables),
+%	writeln(Likelihood:Its:Likelihood0:Tables),
 	(
 	    (
 	     abs((Likelihood - Likelihood0)/Likelihood) < MaxError
@@ -205,7 +205,7 @@ compute_parameters([Id-Samples|Dists], [Id-NewTable|Tables],  MDistTable, Lik0,
 	empty_dist(Id, Table0),
 	add_samples(Samples, Table0, MDistTable),
 	soften_sample(Table0, SoftenedTable),
-	matrix:matrix_sum(Table0,TotM),writeln(Id-TotM),
+	matrix:matrix_sum(Table0,TotM),
 	normalise_counts(SoftenedTable, NewTable),
 	compute_likelihood(Table0, NewTable, DeltaLik),
 	dist_new_table(Id, NewTable),

CLPBN/learning/learn_utils.yap

@@ -68,15 +68,15 @@ soften_sample(T0,T) :-
 
 soften_sample(no,T,T).
 soften_sample(m_estimate(M), T0, T) :-
-	matrix_agg_cols(T0,+,Cols),matrix:matrix_to_list(Cols), writeln(Cols),
+	matrix_agg_cols(T0,+,Cols),
 	matrix_op_to_all(Cols, *, M, R),
-	matrix_op_to_cols(T0,+,R,T).
+	matrix_op_to_cols(T0,R,+,T).
 soften_sample(auto_m, T0,T) :-
-	matrix_agg_cols(T0,+,Cols),matrix:matrix_to_list(Cols), writeln(Cols),
+	matrix_agg_cols(T0,+,Cols),
 	matrix_sum(Cols,TotM),
 	M is sqrt(TotM),
 	matrix_op_to_all(Cols, *, M, R),
-	matrix_op_to_cols(T0,+,R,T).
+	matrix_op_to_cols(T0,R,+,T).
 soften_sample(laplace,T0,T) :-
 	matrix_op_to_all(T0, +, 1, T).