changes to support em: step 1

CLPBN/clpbn/dists.yap

@@ -18,7 +18,8 @@
 	   get_evidence_from_position/3,
 	   dist_to_term/2,
 	   empty_dist/2,
-	   dist_new_table/2
+	   dist_new_table/2,
+	   all_dist_ids/1
 	]).
 
 :- use_module(library(lists),[is_list/1,nth0/3]).
@@ -229,4 +230,3 @@ dist_new_table(Id, NewMat) :-
 	fail.
 dist_new_table(_, _).
 
-

CLPBN/clpbn/gibbs.yap

@@ -8,7 +8,8 @@
 %
 
 :- module(gibbs, [gibbs/3,
-		check_if_gibbs_done/1]).
+		check_if_gibbs_done/1,
+		init_gibbs_solver/3]).
 
 :- use_module(library(rbtrees),
 	      [rb_new/1,
@@ -49,9 +50,7 @@
 gibbs([],_,_) :- !.
 gibbs(LVs,Vs0,AllDiffs) :-
 	LVs = [_], !,
-	clean_up,
+	init_gibbs_solver(Vs0, LVs, Gibbs),
-	check_for_hidden_vars(Vs0, Vs0, Vs1),
-	sort(Vs1,Vs),
 	(clpbn:output(xbif(XBifStream)) -> clpbn2xbif(XBifStream,vel,Vs) ; true),
 	(clpbn:output(gviz(XBifStream)) -> clpbn2gviz(XBifStream,vel,Vs,LVs) ; true),
 	initialise(Vs, Graph, LVs, OutputVars, VarOrder),
@@ -64,6 +63,11 @@ gibbs(LVs,Vs0,AllDiffs) :-
 gibbs(LVs,_,_) :-
 	throw(error(domain_error(solver,LVs),solver(gibbs))).
 
+init_gibbs_solver(LVs, Vs0, Gibbs) :-
+	clean_up,
+	check_for_hidden_vars(Vs0, Vs0, Vs1),
+	sort(Vs1,Vs).
+
 initialise(LVs, Graph, GVs, OutputVars, VarOrder) :-
 	init_keys(Keys0),
 	gen_keys(LVs, 0, VLen, Keys0, Keys),

CLPBN/learning/em.yap

@@ -0,0 +1,114 @@
+%
+% The world famous EM algorithm, in a nutshell
+%
+
+:- module(clpbn_em, [em/6]).
+
+:- use_module(library(lists),
+	      [append/3]).
+
+:- use_module(library('clpbn/learning/learn_utils'),
+	      [run_all/1,
+	       clpbn_vars/2,
+	       normalise_counts/2]).
+
+em(Items, MaxError, MaxIts, Tables, Likelihood) :-
+	init_em(Items, State),
+	em_loop(0, 0.0, state(AllVars,AllDists), MaxError, MaxIts, Likelihood),
+	get_tables(State, Tables).
+
+% This gets you an initial configuration. If there is a lot of evidence
+% tables may be filled in close to optimal, otherwise they may be
+% close to uniform.
+% it also gets you a run for random variables
+init_em(Items, state(AllVars, AllDists, AllDistInstances)) :-
+	run_all(Items),
+	different_dists(AllVars, AllDists, AllDistInstances).
+
+% loop for as long as you want.
+em_loop(MaxIts, Likelihood State, _, _ MaxIts, Likelihood) :- !.
+em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF) :-
+	estimate(State),
+	maximise(State, Likelihood),
+	(
+	 (
+	     (Likelihood - Likelihood0)/Likelihood < MaxError
+	 ;
+	     Its == MaxIts
+	 )	 
+	->
+	 LikelihoodF = Likelihood
+	;
+	 Its1 is Its+1,
+	 em_loop(Its1, Likelihood, State, MaxError, MaxIts, LikelihoodF)
+	).
+
+% collect the different dists we are going to learn next.
+different_dists(AllVars, AllDists, AllInfo) :-
+	all_dists(AllVars, Dists0, AllInfo),
+	sort(Dists0, Dists1),
+	group(Dists1, AllInfo).
+
+group([], []) :-
+group([i(Id,V,Ps)|Dists1], [Id-[[V|Ps]|Extra]|AllInfo]) :-
+	same_id(Dists1, Id, Extra, Rest),
+	group(Rest, AllInfo).
+
+same_id([i(Id,V,Ps)|Dists1], Id, [[V|Ps]|Extra], Rest) :- !,
+	same_id(Dists1, Id, Extra, Rest).
+same_id(Dists, _, [], Dists).
+
+all_dists([], [], []).
+all_dists([V|AllVars], Dists, [i(Id, AllInfo, Parents)|AllInfo]) :-
+	clpbn:get_atts(V, [dist(Id,_)]),
+	with_evidence(V, Id, Dists, Dists0), !,
+	all_dists(AllVars, Dists0, AllInfo).
+
+with_evidence(V, Id) -->
+	{clpbn:get_atts(V, [evidence(Pos)]) }, !,
+	{ dist_pos2bin(Pos, Id, Bin) }.
+with_evidence(V, Id) -->
+	[d(V,Id)].
+
+estimate(state(Vars,Info,_)) :-
+	clpbn_solve_graph(Vars, OVars),
+	marg_vars(Info, Vars).
+
+marg_vars([], _).
+marg_vars([d(V,Id)|Vars], AllVs) :-
+	clpbn_marginalise_in_vars(V, AllVs),
+	marg_vars(Vars, AllVs).
+
+maximise(state(_,_,DistInstances), Tables, Likelihood) :-
+	compute_parameters(DistInstances, Tables, 0.0, Likelihood).
+
+compute_parameters([], [], Lik, Lik).
+compute_parameters([Id-Samples|Dists], [Tab|Tables], Lik0, Lik) :-
+	empty_dist(Id, NewTable),
+	add_samples(Samples, NewTable).
+	normalise_table(Id, NewTable),
+	compute_parameters(Dists, Tables, Lik0, Lik).
+
+add_samples([], _).
+add_samples([S|Samples], Table) :-
+	run_sample(S, 1.0,  Pos, Tot),
+	matrix_add(Table, Pos, Tot),
+	fail.
+add_samples([_|Samples], Table) :-
+	add_samples(Samples, Table)
+	
+run_sample([], Tot,  [], Tot).
+run_sample([V|S], W0,  [P|Pos], Tot) :-
+	{clpbn:get_atts(V, [evidence(P)]) }, !,	
+	run_sample(S, W0,  Pos, Tot).
+run_sample([V|S], W0,  [P|Pos], Tot) :-
+	{clpbn_display:get_atts(V, [posterior,(_,_,Ps,_)]) },
+	count_cases(Ps, 0, D0, P),
+	W1 is D0*W0,
+	run_sample(S, W1,  Pos, Tot).
+	
+count_cases([D0|Ps], I0, D0, I0).
+count_cases([_|Ps], I0, P, W1) :-
+	I is I0+1,
+	count_cases(Ps, I, P, W1).
+