more fixes to improve aggregate performance:

handle evidence on children and root nodes.
make graphviz call on top-level.

CLPBN/clpbn.yap

@@ -77,14 +77,17 @@
 	       sort_vars_by_key/3
 	      ]).
 
+:- use_module('clpbn/graphviz',
+	[clpbn2gviz/4]).
+
 :- dynamic solver/1,output/1,use/1,suppress_attribute_display/1, parameter_softening/1, em_solver/1.
 
 solver(jt).
 em_solver(vel).
 
 %output(xbif(user_error)).
-%output(gviz(user_error)).
+output(gviz(user_error)).
-output(no).
+%output(no).
 suppress_attribute_display(false).
 parameter_softening(laplace).
 
@@ -166,6 +169,8 @@ project_attributes(GVars, AVars) :-
 	clpbn_vars(AVars, DiffVars, AllVars),
 	get_clpbn_vars(GVars,CLPBNGVars0),
 	simplify_query_vars(CLPBNGVars0, CLPBNGVars),
+	(output(xbif(XBifStream)) -> clpbn2xbif(XBifStream,vel,AllVars) ; true),
+	(output(gviz(XBifStream)) -> clpbn2gviz(XBifStream,sort,AllVars,GVars) ; true),
 	(
 	    Solver = graphs
 	->

CLPBN/clpbn/aggregates.yap

@@ -15,7 +15,8 @@
 	sumlist/2,
 	sum_list/3,
 	max_list/2,
-	min_list/2
+	min_list/2,
+	nth0/3
     ]).
 
 :- use_module(library(matrix),
@@ -23,17 +24,22 @@
 	matrix_to_list/2,
 	matrix_set/3]).
 
+:- use_module(library('clpbn/matrix_cpt_utils'),
+	[normalise_CPT_on_lines/3]).
+
 :- use_module(dists, [get_dist_domain_size/2]).
 
 cpt_average(AllVars, Key, Els0, Tab, Vs, NewVs) :-
 	cpt_average(AllVars, Key, Els0, 1.0, Tab, Vs, NewVs).
 
 % support variables with evidence from domain. This should make everyone's life easier.
-cpt_average([_|Vars], Key, Els0, Softness, p(Els0, CPT, NewEls), Vs, NewVs) :-
+cpt_average([Ev|Vars], Key, Els0, Softness, p(Els0, CPT, NewParents), Vs, NewVs) :-
 	find_evidence(Vars, 0, TotEvidence, RVars),
-	build_avg_table(RVars, Vars, Els0, Key, TotEvidence, Softness, MAT, NewEls, Vs, NewVs),
+	build_avg_table(RVars, Vars, Els0, Key, TotEvidence, Softness, MAT0, NewParents0, Vs, IVs),
-	matrix_to_list(MAT, CPT).
+	include_qevidence(Ev, MAT0, MAT, NewParents0, NewParents, Vs, IVs, NewVs),
+	matrix_to_list(MAT, CPT), writeln(NewParents: Vs: NewVs: CPT).
 
+% find all fixed kids, this simplifies significantly the function.
 find_evidence([], TotEvidence, TotEvidence, []).
 find_evidence([V|Vars], TotEvidence0, TotEvidence, RVars) :-
 	clpbn:get_atts(V,[evidence(Ev)]), !,
@@ -139,6 +145,35 @@ list_split(I, [H|L], [H|L1], L2) :-
 	I1 is I-1,
 	list_split(I1, L, L1, L2).
 
+%
+% if we have evidence, we need to check if we are always consistent, never consistent, or can be consistent
+%
+include_qevidence(V, MAT0, MAT, NewParents0, NewParents, Vs, IVs, NewVs) :-
+	clpbn:get_atts(V,[evidence(Ev)]), !,
+	normalise_CPT_on_lines(MAT0, MAT1, L1),
+	check_consistency(L1, Ev, MAT0, MAT1, L1, MAT, NewParents0, NewParents, Vs, IVs, NewVs).
+include_qevidence(_, MAT, MAT, NewParents, NewParents, _, Vs, Vs).
+
+check_consistency(L1, Ev, MAT0, MAT1, L1, MAT, NewParents0, NewParents, Vs, IVs, NewVs) :-
+	sumlist(L1, Tot),
+	nth0(Ev, L1, Val),
+	(Val == Tot ->
+writeln(Ev:L1:Val:1),
+	    MAT1 = MAT,
+	    NewParents = [],
+	    Vs = NewVs
+	;
+	 Val == 0.0 ->
+writeln(Ev:L1:Val:2),
+	    throw(error(domain_error(incompatible_evidence),evidence(Ev)))
+	;
+writeln(Ev:L1:Val:3),
+	    MAT0 = MAT,
+	    NewParents = NewParents0,
+	    IVs = NewVs
+	).
+	
+
 %
 % generate actual table, instead of trusting the solver
 %
@@ -147,19 +182,17 @@ average_cpt(Vs, OVars, Vals, Base, _, MCPT) :-
 	get_ds_lengths(Vs,Lengs),
 	length(OVars, N),
 	length(Vals, SVals),
-	Tot is (N-1)*SVals,
-	Factor is SVals/Tot,
 	matrix_new(floats,[SVals|Lengs],MCPT),
-	fill_in_average(Lengs,Factor,Base,MCPT).
+	fill_in_average(Lengs,N,Base,MCPT).
 
 get_ds_lengths([],[]).
 get_ds_lengths([V|Vs],[Sz|Lengs]) :-
 	get_vdist_size(V, Sz),
 	get_ds_lengths(Vs,Lengs).
 	
-fill_in_average(Lengs, SVals, Base, MCPT) :-
+fill_in_average(Lengs, N, Base, MCPT) :-
 	generate(Lengs, Case),
-	average(Case, SVals, Base, Val),
+	average(Case, N, Base, Val),
 	matrix_set(MCPT,[Val|Case],1.0),
 	fail.
 fill_in_average(_,_,_,_).
@@ -175,9 +208,9 @@ from(I1,M,J) :-
 	I < M,
 	from(I,M,J).
 
-average(Case, SVals, Base, Val) :-
+average(Case, N, Base, Val) :-
 	sum_list(Case, Base, Tot),
-	Val is integer(round(Tot*SVals)).
+	Val is integer(round(Tot/N)).
 
 
 sum_cpt(Vs,Vals,_,CPT) :-

CLPBN/clpbn/dists.yap

@@ -227,6 +227,8 @@ get_dist_key(Id, Key) :-
 get_dist_nparams(Id, NParms) :-
 	recorded(clpbn_dist_db, db(Id, _, _, _, _, NParms, _), _).
 
+get_evidence_position(El, avg(Domain), Pos) :- !,
+	nth0(Pos, Domain, El), !.
 get_evidence_position(El, Id, Pos) :-
 	recorded(clpbn_dist_db, db(Id, _, _, _, Domain, _, _), _),
 	nth0(Pos, Domain, El), !.

CLPBN/clpbn/gibbs.yap

@@ -65,8 +65,6 @@
 %
 gibbs(LVs,Vs0,AllDiffs) :-
 	init_gibbs_solver(LVs, Vs0, AllDiffs, Vs),
-	(clpbn:output(xbif(XBifStream)) -> clpbn2xbif(XBifStream,vel,Vs) ; true),
-	(clpbn:output(gviz(XBifStream)) -> clpbn2gviz(XBifStream,vel,Vs,LVs) ; true),
 	run_gibbs_solver(LVs, LPs, Vs),
 	clpbn_bind_vals(LVs,LPs,AllDiffs),
 	clean_up.

CLPBN/clpbn/graphviz.yap

@@ -1,4 +1,4 @@
-:- module(gviz, [clpbn2gviz/4]).
+:- module(clpbn_gviz, [clpbn2gviz/4]).
 
 clpbn2gviz(Stream, Name, Network, Output) :-
 	format(Stream, 'digraph ~w {

CLPBN/clpbn/matrix_cpt_utils.yap

@@ -16,7 +16,8 @@
 	   column_from_possibly_deterministic_CPT/3,
 	   multiply_possibly_deterministic_factors/3,
 	   random_CPT/2,
-	   uniform_CPT/2]).
+	   uniform_CPT/2,
+	   normalise_CPT_on_lines/3]).
 
 :- use_module(dists,
 	      [get_dist_domain_size/2,
@@ -41,6 +42,7 @@
 	       matrix_set_all_that_disagree/5,
 	       matrix_to_list/2,
 	       matrix_agg_lines/3,
+	       matrix_agg_cols/3,
 	       matrix_op_to_lines/4,
 	       matrix_column/3]).
 
@@ -252,4 +254,9 @@ uniform_CPT(Dims, M) :-
 	matrix_new_set(floats,Dims,1.0,M1),
 	normalise_possibly_deterministic_CPT(M1, M).
 
-	
+normalise_CPT_on_lines(MAT0, MAT2, L1) :-
+	matrix_agg_cols(MAT0, +, MAT1), 
+	matrix_sum(MAT1, SUM),
+	matrix_op_to_all(MAT1, /, SUM, MAT2),
+	matrix:matrix_to_list(MAT2,L1).
+

CLPBN/clpbn/vel.yap

@@ -83,10 +83,7 @@ init_vel_solver(Qs, Vs0, _, LVis) :-
 	% LVi will have a  list of CLPBN variables
 	% Tables0 will have the full data on each variable
 	init_influences(Vs1, G, RG),
-	init_vel_solver_for_questions(Qs, G, RG, Vs0F, LVis),
+	init_vel_solver_for_questions(Qs, G, RG, _, LVis).
-	term_variables(Vs0F, Vs),
-	(clpbn:output(xbif(XBifStream)) -> clpbn2xbif(XBifStream,vel,Vs) ; true),
-	(clpbn:output(gviz(XBifStream)) -> clpbn2gviz(XBifStream,vel,Vs,_) ; true).
 
 check_for_special_vars([], []).
 check_for_special_vars([V|Vs0], [V|Vs1]) :-
@@ -211,7 +208,7 @@ process(LV0, _, Out) :-
 
 find_best([], V, _TF, V, _, [], _).
 %:-
-%	clpbn:get_atts(V,[key(K)]), write(chosen:K:TF), nl.
+%	clpbn:get_atts(V,[key(K)]), writeln(chosen:K:_TF).
 % root_with_single_child
 %find_best([var(V,I,_,_,[],Ev,[Dep],K)|LV], _, _, V, [Dep], LVF, Inputs) :- !.	
 find_best([var(V,I,Sz,Vals,Parents,Ev,Deps,K)|LV], _, Threshold, VF, NWorktables, LVF, Inputs) :-
@@ -226,8 +223,9 @@ find_best([var(V,I,Sz,Vals,Parents,Ev,Deps,K)|LV], _, Threshold, VF, NWorktables
 find_best([V|LV], V0, Threshold, VF, WorkTables, [V|LVF], Inputs) :-
 	find_best(LV, V0, Threshold, VF, WorkTables, LVF, Inputs).
 
-multiply_tables([Table], Table) :- !.
+multiply_tables([Table], Table) :- !. %, Table = tab(T,D,S),matrix:matrix_to_list(T,L),writeln(D:S:L).
 multiply_tables([TAB1, TAB2| Tables], Out) :-
+%TAB1 = tab(T,_,_),matrix:matrix_to_list(T,L),writeln(doing:L),
 	multiply_CPTs(TAB1, TAB2, TAB, _),
 	multiply_tables([TAB| Tables], Out).