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new implementation of aggregates, using matrices.

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Vitor Santos Costa 2008-10-29 20:50:21 +00:00
parent 22b17b5856
commit 922424abd0
1 changed files with 110 additions and 193 deletions
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CLPBN/clpbn/aggregates.yap
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@ -8,72 +8,81 @@
:- use_module(library(clpbn), [{}/1]). :- use_module(library(clpbn), [{}/1]).
:- use_module(library(lists), [last/2]). :- use_module(library(lists),
[last/2,
sumlist/2,
max_list/2,
min_list/2
]).
:- use_module(library(matrix),
[matrix_new/3,
matrix_to_list/2,
matrix_set/3]).
:- use_module(dists, [get_dist_domain_size/2]). :- use_module(dists, [get_dist_domain_size/2]).
cpt_average(Vars, Key, Els0, CPT) :- cpt_average(Vars, Key, Els0, CPT) :-
check_domain(Els0, Els), build_avg_table(Vars, Els0, Key, 1.0, CPT).
length(Els, SDomain),
build_avg_table(Vars, Els, SDomain, Els0, Key, 1.0, CPT).
cpt_average(Vars, Key, Els0, Softness, CPT) :- cpt_average(Vars, Key, Els0, Softness, CPT) :-
check_domain(Els0, Els), build_avg_table(Vars, Els0, Key, Softness, CPT).
length(Els, SDomain),
build_avg_table(Vars, Els, SDomain, Els0, Key, Softness, CPT).
cpt_max(Vars, Key, Els0, CPT) :- cpt_max(Vars, Key, Els0, CPT) :-
check_domain(Els0, Els), build_max_table(Vars, Els0, Els0, Key, 1.0, CPT).
length(Els, SDomain),
build_max_table(Vars, Els, SDomain, Els0, Key, CPT).
cpt_min(Vars, Key, Els0, CPT) :- cpt_min(Vars, Key, Els0, CPT) :-
check_domain(Els0, Els), build_min_table(Vars, Els0, Els0, Key, 1.0, CPT).
length(Els, SDomain),
build_min_table(Vars, Els, SDomain, Els0, Key, CPT).
build_avg_table(Vars, Domain, SDomain, ODomain, _, 1.0, p(ODomain, CPT, Vars)) :- build_avg_table(Vars, Domain, _, Softness, p(Domain, CPT, Vars)) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize), int_power(Vars, SDomain, 1, TabSize),
TabSize =< 16, TabSize =< 16,
/* case gmp is not there !! */ /* case gmp is not there !! */
TabSize > 0, !, TabSize > 0, !,
average_cpt(Vars, Domain, CPT). average_cpt(Vars, Domain, Softness, CPT).
build_avg_table(Vars, Domain, _, ODomain, Key, Softness, p(ODomain, CPT, [V1,V2])) :- build_avg_table(Vars, Domain, Key, Softness, p(Domain, CPT, [V1,V2])) :-
length(Vars,L), length(Vars,L),
LL1 is L//2, LL1 is L//2,
LL2 is L-LL1, LL2 is L-LL1,
list_split(LL1, Vars, L1, L2), list_split(LL1, Vars, L1, L2),
Domain = [Min|Els1], Min = 0,
last(Els1,Max), length(Domain,Max1), Max is Max1-1,
build_intermediate_table(LL1, sum(Min,Max), L1, V1, Key, Softness, 0, I1), build_intermediate_table(LL1, sum(Min,Max), L1, V1, Key, 1.0, 0, I1),
build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, Softness, I1, _), build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, 1.0, I1, _),
normalised_average_cpt(L, [V1,V2], Domain, Softness, CPT). average_cpt([V1,V2], Domain, Softness, CPT).
build_max_table(Vars, Domain, SDomain, ODomain, _, p(ODomain, CPT, Vars)) :- build_max_table(Vars, Domain, Softness, p(Domain, CPT, Vars)) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize), int_power(Vars, SDomain, 1, TabSize),
TabSize =< 16, !, TabSize =< 16,
max_cpt(Vars, Domain, CPT). /* case gmp is not there !! */
build_max_table(Vars, Domain, _, ODomain, Key, p(ODomain, CPT, [V1,V2])) :- TabSize > 0, !,
max_cpt(Vars, Domain, Softness, CPT).
build_max_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2])) :-
length(Vars,L), length(Vars,L),
LL1 is L//2, LL1 is L//2,
LL2 is L-LL1, LL2 is L-LL1,
list_split(LL1, Vars, L1, L2), list_split(LL1, Vars, L1, L2),
build_intermediate_table(LL1, max(Domain,CPT), L1, V1, Key, 1.0, 0, I1), 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(LL2, max(Domain,CPT), L2, V2, Key, 1.0, I1, _),
max_cpt([V1,V2], Domain, CPT). max_cpt([V1,V2], Domain, Softness, CPT).
build_min_table(Vars, Domain, SDomain, ODomain, _, p(ODomain, CPT, Vars)) :- build_min_table(Vars, Domain, Softness, p(Domain, CPT, Vars)) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize), int_power(Vars, SDomain, 1, TabSize),
TabSize =< 16, !, TabSize =< 16,
min_cpt(Vars, Domain, CPT). /* case gmp is not there !! */
build_min_table(Vars, Domain, _, ODomain, Key, p(ODomain, CPT, [V1,V2])) :- TabSize > 0, !,
min_cpt(Vars, Domain, Softness, CPT).
build_min_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2])) :-
length(Vars,L), length(Vars,L),
LL1 is L//2, LL1 is L//2,
LL2 is L-LL1, LL2 is L-LL1,
list_split(LL1, Vars, L1, L2), list_split(LL1, Vars, L1, L2),
build_intermediate_table(LL1, min(Domain,CPT), L1, V1, Key, 1.0, 0, I1), 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(LL2, min(Domain,CPT), L2, V2, Key, 1.0, I1, _),
min_cpt([V1,V2], Domain, CPT). min_cpt([V1,V2], Domain, Softness, CPT).
int_power([], _, TabSize, TabSize). int_power([], _, TabSize, TabSize).
int_power([_|L], X, I0, TabSize) :- int_power([_|L], X, I0, TabSize) :-
@ -116,185 +125,93 @@ list_split(I, [H|L], [H|L1], L2) :-
I1 is I-1, I1 is I-1,
list_split(I1, L, L1, L2). list_split(I1, L, L1, L2).
% allow quick description for a range.
check_domain([I0|Is], [I0|Is]) :-
integer(I0),
check_integer_domain(Is,I0), !.
check_domain(D, ND) :-
normalise_domain(D, 0, ND).
check_integer_domain([],_).
check_integer_domain([I1|Is],I0) :-
I0 < I1,
check_integer_domain(Is,I1).
normalise_domain([], _, []).
normalise_domain([_|D], I0, [I0|ND]) :-
I is I0+1,
normalise_domain(D, I, ND).
% %
% generate actual table, instead of trusting the solver % generate actual table, instead of trusting the solver
% %
average_cpt(Vs,Vals,CPT) :- average_cpt(Vs,Vals,_,CPT) :-
generate_indices(Vals,Inds,0,Av), get_ds_lengths(Vs,Lengs),
combine_all(Vs, Inds, Cs), sumlist(Lengs, Tot),
length(Vs, Max), length(Vals,SVals),
average_possible_cases(0, Av, Max, Cs, 1.0, CPT). Factor is SVals/Tot,
matrix_new(floats,[SVals|Lengs],MCPT),
fill_in_average(Lengs,Factor,MCPT),
matrix_to_list(MCPT,CPT).
sum_cpt(Vs, Vals, Softness, CPT) :- get_ds_lengths([],[]).
length(Vals,Sz), get_ds_lengths([V|Vs],[Sz|Lengs]) :-
combine_all(Vs, Cs), get_vdist_size(V, Sz),
sum_possible_cases(0, Sz, Cs, Softness, CPT). get_ds_lengths(Vs,Lengs).
normalised_average_cpt(Max, Vs, Vals, Softness, CPT) :- fill_in_average(Lengs,SVals,MCPT) :-
generate_indices(Vals,_,0,Sz), generate(Lengs, Case),
combine_all(Vs, Cs), average(Case, SVals, Val),
average_possible_cases(0, Sz, Max, Cs, Softness, CPT). matrix_set(MCPT,[Val|Case],1.0),
fail.
fill_in_average(_,_,_).
generate([], []).
generate([N|Lengs], [C|Case]) :-
from(0,N,C),
generate(Lengs, Case).
from(I,_,I).
from(I1,M,J) :-
I is I1+1,
I < M,
from(I,M,J).
average(Case, SVals, Val) :-
sumlist(Case, Tot),
Val is integer(round(Tot*SVals)).
generate_indices([],[],Av,Av). sum_cpt(Vs,Vals,_,CPT) :-
generate_indices([_|Ls],[I|Inds],I,Av) :- get_ds_lengths(Vs,Lengs),
I1 is I+1, length(Vals,SVals),
generate_indices(Ls,Inds,I1,Av). matrix_new(floats,[SVals|Lengs],MCPT),
fill_in_sum(Lengs,MCPT),
matrix_to_list(MCPT,CPT).
fill_in_sum(Lengs,MCPT) :-
generate(Lengs, Case),
sumlist(Case, Val),
matrix_set(MCPT,[Val|Case],1.0),
fail.
fill_in_sum(_,_).
combine_all([], [[]]). max_cpt(Vs,Vals,_,CPT) :-
combine_all([V|LV], Cs) :- get_ds_lengths(Vs,Lengs),
combine_all(LV, Cs0), length(Vals,SVals),
get_vdist_size(V,Sz), matrix_new(floats,[SVals|Lengs],MCPT),
generate_indices(0, Sz, Vals), fill_in_max(Lengs,MCPT),
add_vals(Vals, Cs0, Cs). matrix_to_list(MCPT,CPT).
combine_all([], _, [[]]). fill_in_max(Lengs,MCPT) :-
combine_all([_|LV], Vals, Cs) :- generate(Lengs, Case),
combine_all(LV, Vals, Cs0), max_list(Case, Val),
add_vals(Vals, Cs0, Cs). matrix_set(MCPT,[Val|Case],1.0),
fail.
generate_indices(Sz,Sz,[]) :- !. fill_in_max(_,_).
generate_indices(I0,Sz,[I0|Vals]) :-
I is I0+1,
generate_indices(I,Sz,Vals).
add_vals([], _, []). min_cpt(Vs,Vals,_,CPT) :-
add_vals([V|Vs], Cs0, Csf) :- get_ds_lengths(Vs,Lengs),
add_vals(Vs, Cs0, Cs), length(Vals,SVals),
add_val_to_cases(Cs0, V, Cs, Csf). matrix_new(floats,[SVals|Lengs],MCPT),
fill_in_max(Lengs,MCPT),
matrix_to_list(MCPT,CPT).
add_val_to_cases([], _, Cs, Cs). fill_in_min(Lengs,MCPT) :-
add_val_to_cases([C|Cs], V, Cs0, [[V|C]|Csf]) :- generate(Lengs, Case),
add_val_to_cases(Cs, V, Cs0, Csf). max_list(Case, Val),
matrix_set(MCPT,[Val|Case],1.0),
fail.
fill_in_min(_,_).
sum_all([],N,N).
sum_all([C|Cs],N0,N) :-
X is C+N0,
sum_all(Cs,X,N).
average_possible_cases(Av,Av,_,_,_,[]) :- !.
average_possible_cases(I,Av,Max,Cs,Softness,Lf) :-
average_cases2(Cs,I,Av,Softness,Lf,L0),
I1 is I+1,
average_possible_cases(I1,Av,Max,Cs,Softness,L0).
average_cases2([], _, _, _, L, L).
average_cases2([C|Cs], I, Av, Softness, [P|Lf], L0) :-
calculate_avg_prob(C, I, Av, Softness, P),
average_cases2(Cs, I, Av, Softness, Lf, L0).
calculate_avg_prob(C, I, Av, Softness, Softness) :-
sum_all(C,0,N),
I =:= integer(round(N/Av)), !.
calculate_avg_prob(_, _, Av, Softness, Comp) :-
Comp is (1.0-Softness)/(Av-1).
sum_possible_cases(Av,Av,_, _, []) :- !.
sum_possible_cases(I,Av,Cs,Softness, Lf) :-
sum_cases2(Cs,I, Av, Softness, Lf,L0),
I1 is I+1,
sum_possible_cases(I1,Av,Cs,Softness, L0).
sum_cases2([], _, _, _, L, L).
sum_cases2([C|Cs], I, Av, Softness, [P|Lf], L0) :-
calculate_sum_prob(C, I, Av, Softness, P),
sum_cases2(Cs, I, Av, Softness, Lf, L0).
calculate_sum_prob(C, I, _, Softness, Softness) :-
sum_all(C,0,N),
I =:= N, !.
calculate_sum_prob(_, _, Av, Softness, Comp) :-
Comp is (1.0-Softness)/(Av-1).
%
% generate a CPT for max.
%
max_cpt(Vs, Domain, CPT) :-
combinations(Vs, Domain, Combinations),
cpt_from_domain(Domain, Combinations, Domain, max, CPT).
min_cpt(Vs, Domain, CPT) :-
combinations(Vs, Domain, Combinations),
cpt_from_domain(Domain, Combinations, Domain, min, CPT).
combinations(Vs, Domain, Combinations) :-
mult_domains(Vs, Domain, Domains),
cart(Domains, Combinations).
mult_domains([], _, []).
mult_domains([_|Vs], Domain, [Domain|Domains]) :-
mult_domains(Vs, Domain, Domains).
cart([], [[]]).
cart([L|R], Rf) :-
cart(R, R1),
add(L, R1, Rf).
add([], _, []).
add([A|R], R1, RsF) :-
add_head(R1, A, RsF, Rs0),
add(R, R1, Rs0).
add_head([], _, Rs, Rs).
add_head([H|L], A, [[A|H]|Rs], Rs0) :-
add_head(L, A, Rs, Rs0).
cpt_from_domain([], _, _, _, []).
cpt_from_domain([El|Domain], Combinations, Domain0, OP, CPT) :-
cpt_from_domain_el(Combinations, El, Domain0, OP, CPT, CPT0),
cpt_from_domain(Domain, Combinations, Domain0, OP, CPT0).
cpt_from_domain_el([], _, _, _, CPT, CPT).
cpt_from_domain_el([C|Combinations], El, Domain, OP, [P|CPT], CPT0) :-
cpt_for_el(C, OP, El, Domain, 0.0, P),
cpt_from_domain_el(Combinations, El, Domain, OP, CPT, CPT0).
cpt_for_el([], _, _, _, P, P).
cpt_for_el([El|Cs], MAX, El, Domain, _, P) :- !,
cpt_for_el(Cs, MAX, El, Domain, 1.0, P).
cpt_for_el([C|_], MAX, El, Domain, _, 0.0) :-
op_broken(MAX, C, El, Domain), !.
cpt_for_el([_|Cs], MAX, El, Domain, P0, P) :-
cpt_for_el(Cs, MAX, El, Domain, P0, P).
op_broken(max, C, El, Domain) :-
lg(Domain, C, El).
op_broken(min, C, El, Domain) :-
sm(Domain, C, El).
lg([El|_], _, El) :- !.
lg([C|_], C, _) :- !, fail.
lg([_|Vs], C, El) :-
lg(Vs, C, El).
sm([El|_], _, El) :- !, fail.
sm([V|_], V, _) :- !.
sm([_|Vs], C, El) :-
sm(Vs, C, El).
get_vdist_size(V, Sz) :- get_vdist_size(V, Sz) :-
clpbn:get_atts(V, [dist(Dist,_)]), clpbn:get_atts(V, [dist(Dist,_)]),
get_dist_domain_size(Dist, Sz). get_dist_domain_size(Dist, Sz).