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use avg CPT type.

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how to deal with it is a solver problem, not an app issue.
This commit is contained in:
Vitor Santos Costa 2008-11-03 16:02:15 +00:00
parent 45df10e86d
commit f6c5d16f63
5 changed files with 91 additions and 54 deletions
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100
CLPBN/clpbn/aggregates.yap
View File

@ -1,9 +1,11 @@
%
% generate explicit CPTs
%
:- module(clpbn_aggregates, [
cpt_average/4,
cpt_average/5,
cpt_max/4,
cpt_min/4
cpt_average/6,
cpt_average/7,
cpt_max/6,
cpt_min/6
]).
:- use_module(library(clpbn), [{}/1]).
@ -11,6 +13,7 @@
:- use_module(library(lists),
[last/2,
sumlist/2,
sum_list/3,
max_list/2,
min_list/2
]).
@ -22,66 +25,77 @@
:- use_module(dists, [get_dist_domain_size/2]).
cpt_average(Vars, Key, Els0, CPT) :-
build_avg_table(Vars, Els0, Key, 1.0, CPT).
cpt_average(AllVars, Key, Els0, Tab, Vs, NewVs) :-
cpt_average(AllVars, Key, Els0, 1.0, Tab, Vs, NewVs).
cpt_average(Vars, Key, Els0, Softness, CPT) :-
build_avg_table(Vars, Els0, Key, Softness, CPT).
% 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) :-
find_evidence(Vars, 0, TotEvidence, RVars),
build_avg_table(RVars, Vars, Els0, Key, TotEvidence, Softness, MAT, NewEls, Vs, NewVs),
matrix_to_list(MAT, CPT).
cpt_max(Vars, Key, Els0, CPT) :-
build_max_table(Vars, Els0, Els0, Key, 1.0, CPT).
find_evidence([], TotEvidence, TotEvidence, []).
find_evidence([V|Vars], TotEvidence0, TotEvidence, RVars) :-
clpbn:get_atts(V,[evidence(Ev)]), !,
TotEvidenceI is TotEvidence0+Ev,
find_evidence(Vars, TotEvidenceI, TotEvidence, RVars).
find_evidence([V|Vars], TotEvidence0, TotEvidence, [V|RVars]) :-
find_evidence(Vars, TotEvidence0, TotEvidence, RVars).
cpt_min(Vars, Key, Els0, CPT) :-
build_min_table(Vars, Els0, Els0, Key, 1.0, CPT).
cpt_max([_|Vars], Key, Els0, CPT, Vs, NewVs) :-
build_max_table(Vars, Els0, Els0, Key, 1.0, CPT, Vs, NewVs).
build_avg_table(Vars, Domain, _, Softness, p(Domain, CPT, Vars)) :-
cpt_min([_|Vars], Key, Els0, CPT, Vs, NewVs) :-
build_min_table(Vars, Els0, Els0, Key, 1.0, CPT, Vs, NewVs).
build_avg_table(Vars, OVars, Domain, _, TotEvidence, Softness, CPT, Vars, Vs, Vs) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize),
TabSize =< 16,
TabSize =< 256,
/* case gmp is not there !! */
TabSize > 0, !,
average_cpt(Vars, Domain, Softness, CPT).
build_avg_table(Vars, Domain, Key, Softness, p(Domain, CPT, [V1,V2])) :-
average_cpt(Vars, OVars, Domain, TotEvidence, Softness, CPT).
build_avg_table(Vars, OVars, Domain, Key, TotEvidence, Softness, CPT, [V1,V2], Vs, [V1,V2|NewVs]) :-
length(Vars,L),
LL1 is L//2,
LL2 is L-LL1,
list_split(LL1, Vars, L1, L2),
Min = 0,
length(Domain,Max1), Max is Max1-1,
build_intermediate_table(LL1, sum(Min,Max), L1, V1, Key, 1.0, 0, I1),
build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, 1.0, I1, _),
average_cpt([V1,V2], Domain, Softness, CPT).
build_intermediate_table(LL1, sum(Min,Max), L1, V1, Key, 1.0, 0, I1, Vs, Vs1),
build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, 1.0, I1, _, Vs1, NewVs),
average_cpt([V1,V2], OVars, Domain, TotEvidence, Softness, CPT).
build_max_table(Vars, Domain, Softness, p(Domain, CPT, Vars)) :-
build_max_table(Vars, Domain, Softness, p(Domain, CPT, Vars), Vs, Vs) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize),
TabSize =< 16,
/* case gmp is not there !! */
TabSize > 0, !,
max_cpt(Vars, Domain, Softness, CPT).
build_max_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2])) :-
build_max_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2]), Vs, [V1,V2|NewVs]) :-
length(Vars,L),
LL1 is L//2,
LL2 is L-LL1,
list_split(LL1, Vars, L1, L2),
build_intermediate_table(LL1, max(Domain,CPT), L1, V1, Key, 1.0, 0, I1),
build_intermediate_table(LL2, max(Domain,CPT), L2, V2, Key, 1.0, I1, _),
build_intermediate_table(LL1, max(Domain,CPT), L1, V1, Key, 1.0, 0, I1, Vs, Vs1),
build_intermediate_table(LL2, max(Domain,CPT), L2, V2, Key, 1.0, I1, _, Vs1, NewVs),
max_cpt([V1,V2], Domain, Softness, CPT).
build_min_table(Vars, Domain, Softness, p(Domain, CPT, Vars)) :-
build_min_table(Vars, Domain, Softness, p(Domain, CPT, Vars), Vs, Vs) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize),
TabSize =< 16,
/* case gmp is not there !! */
TabSize > 0, !,
min_cpt(Vars, Domain, Softness, CPT).
build_min_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2])) :-
build_min_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2]), Vs, [V1,V2|NewVs]) :-
length(Vars,L),
LL1 is L//2,
LL2 is L-LL1,
list_split(LL1, Vars, L1, L2),
build_intermediate_table(LL1, min(Domain,CPT), L1, V1, Key, 1.0, 0, I1),
build_intermediate_table(LL2, min(Domain,CPT), L2, V2, Key, 1.0, I1, _),
build_intermediate_table(LL1, min(Domain,CPT), L1, V1, Key, 1.0, 0, I1, Vs, Vs1),
build_intermediate_table(LL2, min(Domain,CPT), L2, V2, Key, 1.0, I1, _, Vs1, NewVs),
min_cpt([V1,V2], Domain, Softness, CPT).
int_power([], _, TabSize, TabSize).
@ -89,17 +103,17 @@ int_power([_|L], X, I0, TabSize) :-
I is I0*X,
int_power(L, X, I, TabSize).
build_intermediate_table(1,_,[V],V, _, _, I, I) :- !.
build_intermediate_table(2, Op, [V1,V2], V, Key, Softness, I0, If) :- !,
build_intermediate_table(1,_,[V],V, _, _, I, I, Vs, Vs) :- !.
build_intermediate_table(2, Op, [V1,V2], V, Key, Softness, I0, If, Vs, Vs) :- !,
If is I0+1,
generate_tmp_random(Op, 2, [V1,V2], V, Key, Softness, I0).
build_intermediate_table(N, Op, L, V, Key, Softness, I0, If) :-
build_intermediate_table(N, Op, L, V, Key, Softness, I0, If, Vs, [V1,V2|NewVs]) :-
LL1 is N//2,
LL2 is N-LL1,
list_split(LL1, L, L1, L2),
I1 is I0+1,
build_intermediate_table(LL1, Op, L1, V1, Key, Softness, I1, I2),
build_intermediate_table(LL2, Op, L2, V2, Key, Softness, I2, If),
build_intermediate_table(LL1, Op, L1, V1, Key, Softness, I1, I2, Vs, Vs1),
build_intermediate_table(LL2, Op, L2, V2, Key, Softness, I2, If, Vs1, NewVs),
generate_tmp_random(Op, N, [V1,V2], V, Key, Softness, I0).
% averages are transformed into sums.
@ -129,26 +143,26 @@ list_split(I, [H|L], [H|L1], L2) :-
% generate actual table, instead of trusting the solver
%
average_cpt(Vs,Vals,_,CPT) :-
average_cpt(Vs, OVars, Vals, Base, _, MCPT) :-
get_ds_lengths(Vs,Lengs),
sumlist(Lengs, Tot),
length(Vals,SVals),
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,MCPT),
matrix_to_list(MCPT,CPT).
fill_in_average(Lengs,Factor,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,MCPT) :-
fill_in_average(Lengs, SVals, Base, MCPT) :-
generate(Lengs, Case),
average(Case, SVals, Val),
average(Case, SVals, Base, Val),
matrix_set(MCPT,[Val|Case],1.0),
fail.
fill_in_average(_,_,_).
fill_in_average(_,_,_,_).
generate([], []).
generate([N|Lengs], [C|Case]) :-
@ -161,8 +175,8 @@ from(I1,M,J) :-
I < M,
from(I,M,J).
average(Case, SVals, Val) :-
sumlist(Case, Tot),
average(Case, SVals, Base, Val) :-
sum_list(Case, Base, Tot),
Val is integer(round(Tot*SVals)).

8
CLPBN/clpbn/dists.yap
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@ -68,6 +68,9 @@ where Id is the id,
DSize is the domain size,
Type is
tab for tabular
avg for average
max for maximum
min for minimum
trans for HMMs
continuous
Domain is
@ -98,6 +101,9 @@ dist(V, Id, Key, Parents) :-
dist(V, Id, Key, Parents) :-
var(Key), !,
when(Key, dist(V, Id, Key, Parents)).
dist(avg(Domain, Parents), avg(Domain), _, Parents).
dist(max(Domain, Parents), max(Domain), _, Parents).
dist(min(Domain, Parents), min(Domain), _, Parents).
dist(p(Type, CPT), Id, Key, FParents) :-
copy_structure(Key, Key0),
distribution(Type, CPT, Id, Key0, [], FParents).
@ -207,6 +213,8 @@ get_dsizes([P|Parents], [Sz|Sizes], Sizes0) :-
get_dist_params(Id, Parms) :-
recorded(clpbn_dist_db, db(Id, _, Parms, _, _, _, _), _).
get_dist_domain_size(avg(D,_), DSize) :- !,
length(D, DSize).
get_dist_domain_size(Id, DSize) :-
recorded(clpbn_dist_db, db(Id, _, _, _, _, _, DSize), _).

6
CLPBN/clpbn/examples/School/schema.yap
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@ -47,8 +47,7 @@ course_professor(Key, PKey) :-
course_rating(CKey, Rat) :-
setof(Sat, RKey^(registration_course(RKey,CKey), registration_satisfaction(RKey,Sat)), Sats),
build_rating_table(Sats, rating(CKey), Table),
{ Rat = rating(CKey) with Table }.
{ Rat = rating(CKey) with avg([h,m,l],Sats) }.
course_difficulty(Key, Dif) :-
dif_table(Key, Dist),
@ -64,8 +63,7 @@ student_intelligence(Key, Int) :-
student_ranking(Key, Rank) :-
setof(Grade, CKey^(registration_student(CKey,Key),
registration_grade(CKey, Grade)), Grades),
build_grades_table(Grades, ranking(Key), GradesTable),
{ Rank = ranking(Key) with GradesTable }.
{ Rank = ranking(Key) with avg([a,b,c,d],Grades) }.
:- ensure_loaded(tables).

29
CLPBN/clpbn/vel.yap
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@ -31,12 +31,12 @@
:- use_module(library('clpbn/dists'),
[
dist/4,
get_dist_domain_size/2,
get_dist_matrix/5]).
:- use_module(library('clpbn/utils'), [
clpbn_not_var_member/2,
check_for_hidden_vars/3]).
clpbn_not_var_member/2]).
:- use_module(library('clpbn/display'), [
clpbn_bind_vals/3]).
@ -60,6 +60,10 @@
append/3
]).
:- use_module(library('clpbn/aggregates'),
[cpt_average/6]).
check_if_vel_done(Var) :-
get_atts(Var, [size(_)]), !.
@ -70,14 +74,12 @@ vel([[]],_,_) :- !.
vel([LVs],Vs0,AllDiffs) :-
init_vel_solver([LVs], Vs0, AllDiffs, State),
% variable elimination proper
run_vel_solver([LVs], [Ps], State),
% from array to list
list_from_CPT(Ps, LPs),
run_vel_solver([LVs], [LPs], State),
% bind Probs back to variables so that they can be output.
clpbn_bind_vals([LVs],[LPs],AllDiffs).
init_vel_solver(Qs, Vs0, _, LVis) :-
check_for_hidden_vars(Vs0, Vs0, Vs1),
check_for_special_vars(Vs0, Vs1),
% LVi will have a list of CLPBN variables
% Tables0 will have the full data on each variable
init_influences(Vs1, G, RG),
@ -86,6 +88,21 @@ init_vel_solver(Qs, Vs0, _, LVis) :-
(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]) :-
clpbn:get_atts(V, [key(K), dist(Id,Parents)]), !,
simplify_dist(Id, V, K, Parents, Vs0, Vs00),
check_for_special_vars(Vs00, Vs1).
check_for_special_vars([_|Vs0], Vs1) :-
check_for_special_vars(Vs0, Vs1).
% transform aggregate distribution into tree
simplify_dist(avg(Domain), V, Key, Parents, Vs0, VsF) :- !,
cpt_average([V|Parents], Key, Domain, NewDist, Vs0, VsF),
dist(NewDist, Id, Key, ParentsF),
clpbn:put_atts(V, [dist(Id,ParentsF)]).
simplify_dist(_, _, _, _, Vs0, Vs0).
init_vel_solver_for_questions([], _, _, [], []).
init_vel_solver_for_questions([Vs|MVs], G, RG, [NVs|MNVs0], [NVs|LVis]) :-
influences(Vs, _, NVs0, G, RG),

2
CLPBN/learning/example/school_params.yap
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@ -11,7 +11,7 @@ main :-
em(L,0.01,10,CPTs,Lik),
writeln(Lik:CPTs).
missing(0.3).
missing(0.1).
% miss 30% of the examples.
goal(professor_ability(P,V)) :-