start with pfl learning.

packages/CLPBN/Makefile.in

@@ -93,6 +93,11 @@ CLPBN_HMMER_EXAMPLES= \
 	$(CLPBN_EXDIR)/HMMer/scan.yap \
 	$(CLPBN_EXDIR)/HMMer/score.yap
 
+CLPBN_LEARNING_EXAMPLES= \
+	$(CLPBN_EXDIR)/learning/school_params.yap \
+	$(CLPBN_EXDIR)/learning/sprinkler_params.yap \
+	$(CLPBN_EXDIR)/learning/train.yap
+
 CLPBN_EXAMPLES= \
 	$(CLPBN_EXDIR)/burglary-alarm.fg \
 	$(CLPBN_EXDIR)/burglary-alarm.yap \
@@ -102,6 +107,7 @@ CLPBN_EXAMPLES= \
 	$(CLPBN_EXDIR)/comp_workshops.yap \
 	$(CLPBN_EXDIR)/social_domain1.yap \
 	$(CLPBN_EXDIR)/social_domain2.yap \
+	$(CLPBN_EXDIR)/sprinkler.pfl \
 	$(CLPBN_EXDIR)/sprinkler.yap \
 	$(CLPBN_EXDIR)/workshop_attrs.yap
 
@@ -112,10 +118,12 @@ install: $(CLBN_TOP) $(CLBN_PROGRAMS) $(CLPBN_PROGRAMS)
 	mkdir -p $(DESTDIR)$(EXDIR)
 	mkdir -p $(DESTDIR)$(EXDIR)/School
 	mkdir -p $(DESTDIR)$(EXDIR)/HMMer
+	mkdir -p $(DESTDIR)$(EXDIR)/learning
 	for h in $(CLPBN_TOP); do $(INSTALL_DATA) $$h $(DESTDIR)$(SHAREDIR); done
 	for h in $(CLPBN_PROGRAMS); do $(INSTALL_DATA) $$h $(DESTDIR)$(SHAREDIR)/clpbn; done
 	for h in $(CLPBN_LEARNING_PROGRAMS); do $(INSTALL_DATA) $$h $(DESTDIR)$(SHAREDIR)/clpbn/learning; done
 	for h in $(CLPBN_EXAMPLES); do $(INSTALL_DATA) $$h $(DESTDIR)$(EXDIR); done
 	for h in $(CLPBN_SCHOOL_EXAMPLES); do $(INSTALL_DATA) $$h $(DESTDIR)$(EXDIR)/School; done
 	for h in $(CLPBN_HMMER_EXAMPLES); do $(INSTALL_DATA) $$h $(DESTDIR)$(EXDIR)/HMMer; done
+	for h in $(CLPBN_LEARNING_EXAMPLES); do $(INSTALL_DATA) $$h $(DESTDIR)$(EXDIR)/learning; done
 

packages/CLPBN/clpbn.yap

@@ -512,6 +512,9 @@ clpbn_run_solver(pcg, LVs, LPs, State) :-
 
 add_keys(Key1+V1,_Key2,Key1+V1).
 
+%
+% only useful for probabilistic context free grammars
+%
 clpbn_init_graph(pcg) :- !,
 	pcg_init_graph.
 clpbn_init_graph(_).

packages/CLPBN/clpbn/dists.yap

@@ -293,7 +293,7 @@ empty_dist(Dist, TAB) :-
 dist_new_table(DistId, NewMat) :-
 	use_parfactors(on), !,
 	matrix_to_list(NewMat, List),
-	pfl:set_pfl_parameters(DistId, List).
+	pfl:new_pfl_parameters(DistId, List).
 dist_new_table(Id, NewMat) :-
 	matrix_to_list(NewMat, List),
 	recorded(clpbn_dist_db, db(Id, Key, _, A, B, C, D), R),

packages/CLPBN/examples/learning/sprinkler_params.yap

@@ -0,0 +1,40 @@
+% learn distribution for school database.
+
+:- ['../sprinkler.pfl'].
+
+:- use_module(library(clpbn/learning/em)).
+
+%% data(t,t,t,t).
+data(t,f,_,t).
+%% data(_,t,_,t).
+%% data(t,t,f,f).
+%% data(t,t,f,t).
+%% data(t,_,_,t).
+%% data(t,f,t,t).
+%% data(t,t,f,t).
+%% data(t,_,f,f).
+%% data(t,t,f,f).
+%% data(f,f,t,t).
+%% data(t,t,_,f).
+%% data(t,f,f,t).
+%% data(t,f,t,t).
+
+%:- clpbn:set_clpbn_flag(em_solver,gibbs).
+%:- clpbn:set_clpbn_flag(em_solver,jt).
+%:- clpbn:set_clpbn_flag(em_solver,ve).
+:- clpbn:set_clpbn_flag(em_solver,bp).
+
+timed_main :-
+	statistics(runtime, _),
+	main(Lik),
+	statistics(runtime, [T,_]),
+	format('Took ~d msec and Lik ~3f~n',[T,Lik]).
+
+main(Lik) :-
+	findall(X,scan_data(X),L),
+        em(L,0.01,10,_,Lik).
+
+scan_data(example([wet_grass(W),sprinkler(S),rain(R),cloudy(C)])) :-
+	data(W, S, R, C).
+
+

packages/CLPBN/examples/sprinkler.pfl

@@ -0,0 +1,33 @@
+
+:- style_check(all).
+
+:- ensure_loaded(library(pfl)).
+
+% 1. define domain of random variables
+%    not necessary if they are boolean.
+
+% 2. define parfactors
+
+bayes cloudy ; cloudy_table ; [].
+
+bayes sprinkler, cloudy ; sprinkler_table ; [].
+
+bayes rain, cloudy ; rain_table ; [].
+
+bayes wet_grass, sprinkler, rain ; wet_grass_table ; [].
+
+
+% 3. define CPTs.
+
+wet_grass_table([1.0,0.1,0.1,0.01,
+                 0.0,0.9,0.9,0.99]).
+
+sprinkler_table([0.5,0.9,
+		 0.5,0.1]).
+
+rain_table([0.8,0.2,
+	    0.2,0.8]).
+
+cloudy_table([0.5,0.5]).
+
+

packages/CLPBN/learning/em.yap

@@ -76,15 +76,21 @@ handle_em(error(repeated_parents)) :-
 % and more detailed info on distributions, namely with a list of all instances for the distribution.
 init_em(Items, state( AllDists, AllDistInstances, MargVars, SolverVars)) :-
 	clpbn_flag(em_solver, Solver),
+	% only used for PCGs
 	clpbn_init_graph(Solver),
+	% create the ground network
 	call_run_all(Items),
 %	randomise_all_dists,
+	% set initial values for distributions
 	uniformise_all_dists,
+	% get all variablews to marginalise
 	attributes:all_attvars(AllVars0),
+	% and order them
 	sort_vars_by_key(AllVars0,AllVars,[]),
 	% remove variables that do not have to do with this query.
 %	check_for_hidden_vars(AllVars1, AllVars1, AllVars),
 	different_dists(AllVars, AllDists, AllDistInstances, MargVars),
+	% setup solver by doing parameter independent work.
 	clpbn_init_solver(Solver, MargVars, AllVars, _, SolverVars).
 
 % loop for as long as you want.
@@ -116,15 +122,32 @@ ltables([Id-T|Tables], [Key-LTable|FTables]) :-
 
 % collect the different dists we are going to learn next.
 different_dists(AllVars, AllDists, AllInfo, MargVars) :-
-	all_dists(AllVars, Dists0),
+	all_dists(AllVars, AllVars, Dists0),
 	sort(Dists0, Dists1),
 	group(Dists1, AllDists, AllInfo, MargVars0, []),
 	sort(MargVars0, MargVars).
 
-all_dists([], []).
+%
-all_dists([V|AllVars], [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
+% V -> to Id defining V. We get:
+% the random variables that are parents
+% the cases that can happen, eg if we have A <- B, C 
+% A and B are boolean w/o evidence, and C is f, the cases could be
+% [0,0,1], [0,1,1], [1,0,0], [1,1,0], 
+% Hiddens will be C
+%
+all_dists([], _, []).
+all_dists([V|AllVars], AllVars0, [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
+	clpbn:use_parfactors(on), !,
+	clpbn:get_atts(V, [key(K)]),
+	pfl:factor(bayes,Id,[K|PKeys],_,_,_),
+	find_variables(PKeys, AllVars0, Parents),
+	generate_hidden_cases([V|Parents], CompactCases, Hiddens),
+	uncompact_cases(CompactCases, Cases),
+	all_dists(AllVars, AllVars0, Dists).
+all_dists([V|AllVars], AllVars0, [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
+	% V is an instance of Id
 	clpbn:get_atts(V, [dist(Id,Parents)]),
-	sort([V|Parents], Sorted),
+ 	sort([V|Parents], Sorted),
 	length(Sorted, LengSorted),
         length(Parents, LengParents),
 	(
@@ -133,15 +156,31 @@ all_dists([V|AllVars], [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
 	    true
 	;
 	    throw(error(repeated_parents))
-	),	
+	),
 	generate_hidden_cases([V|Parents], CompactCases, Hiddens),
 	uncompact_cases(CompactCases, Cases),
-	all_dists(AllVars, Dists).
+	all_dists(AllVars, AllVars0, Dists).
+
+find_variables([], _AllVars0, []).
+find_variables([K|PKeys], AllVars0, [Parent|Parents]) :-
+	find_variable(K, AllVars0, Parent),
+	find_variables(PKeys, AllVars0, Parents).
+
+find_variable(K, [Parent|_AllVars0], Parent) :-
+	clpbn:get_atts(Parent, [key(K0)]), K0 =@= K, !.
+find_variable(K, [_|AllVars0], Parent) :-
+	find_variable(K, AllVars0, Parent).
 
 generate_hidden_cases([], [], []).
 generate_hidden_cases([V|Parents], [P|Cases], Hiddens) :-
 	clpbn:get_atts(V, [evidence(P)]), !,
 	generate_hidden_cases(Parents, Cases, Hiddens).
+generate_hidden_cases([V|Parents], [Cases|MoreCases], [V|Hiddens]) :-
+	clpbn:use_parfactors(on), !,
+	clpbn:get_atts(V, [key(K)]),
+	pfl:skolem(K,D), length(D,Sz),
+	gen_cases(0, Sz, Cases),
+	generate_hidden_cases(Parents, MoreCases, Hiddens).
 generate_hidden_cases([V|Parents], [Cases|MoreCases], [V|Hiddens]) :-
 	clpbn:get_atts(V, [dist(Id,_)]),
 	get_dist_domain_size(Id, Sz),

packages/CLPBN/learning/learn_utils.yap

@@ -36,8 +36,10 @@ run_all(M:Gs) :-
 	run_all(Gs,M).
 
 run_all([],_).
+run_all([example(Gs0)|Gs],M) :-
+	run_all(Gs0,M),
+	run_all(Gs,M).
 run_all([G|Gs],M) :-
-%	(G = _:ge(ybr136w,t8,23,-1) -> nb_getval(clpbn_tables, Tab), writeln(Tab) ; true ),
 	( call(M:G) -> true ;  throw(bad_call(M:G)) ),
 	run_all(Gs,M).
 

packages/CLPBN/pfl.yap

@@ -21,7 +21,7 @@
 	 append/3,
 	 member/2]).
 
-:- dynamic factor/5, skolem_in/2, skolem/2, preprocess/3, evidence/2, id/1.
+:- dynamic factor/6, skolem_in/2, skolem/2, preprocess/3, evidence/2, id/1.
 
 :- reexport(library(clpbn),
 	[clpbn_flag/2 as pfl_flag,
@@ -55,7 +55,7 @@ user:term_expansion( markov((Formula ; Phi ; Constraints)), pfl:factor(markov,Id
 	process_args(Formula, Id, 0, _, FList, []).
 user:term_expansion( Id@N, L ) :-
 	atom(Id), number(N), !,
-  N1 is N + 1,
+	N1 is N + 1,
 	findall(G,generate_entity(1, N1, Id, G), L).
 user:term_expansion( Goal, [] ) :-
 	preprocess(Goal, Sk,Var), !,
@@ -112,6 +112,10 @@ process_arg(Sk::D, Id, _I) -->
 process_arg(Sk, Id, _I) -->
 	!,
 	{
+	 % if :: been used before for this skolem
+	 % just keep on using it,
+         % otherwise, assume it is t,f
+         ( \+ \+ skolem(Sk,_D) -> true ; new_skolem(Sk,[t,f]) ),	
 	 assert(skolem_in(Sk, Id))
         },
 	[Sk].
@@ -121,7 +125,7 @@ new_skolem(Sk,D) :-
 	skolem(Sk1, D1),
 	Sk1 =@= Sk,
 	!,
-	D1 = D.
+	( D1 = D -> true ; throw(pfl(permission_error(redefining_domain(Sk),D:D1)))).
 new_skolem(Sk,D) :-
 	interface_predicate(Sk),
 	assert(skolem(Sk, D)).
@@ -155,7 +159,7 @@ get_pfl_parameters(Id,Out) :-
 
 
 new_pfl_parameters(Id, NewPhi) :-
-	retract(factor(Type.Id,FList,FV,_Phi,Constraints)),
+	retract(factor(Type,Id,FList,FV,_Phi,Constraints)),
 	assert(factor(Type,Id,FList,FV,NewPhi,Constraints)),
 	fail.
 new_pfl_parameters(_Id, _NewPhi).
@@ -179,5 +183,3 @@ get_factor_pvariable(Id,Var) :-
 	factor(_Type, Id,FList,_FV,_Phi,_Constraints),
 	member(Var, FList).
 
-
-