232 lines
6.5 KiB
Prolog
232 lines
6.5 KiB
Prolog
%
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% generate explicit CPTs
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%
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:- module(clpbn_aggregates, [
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cpt_average/6,
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cpt_average/7,
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cpt_max/6,
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cpt_min/6
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]).
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:- use_module(library(clpbn), [{}/1]).
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:- use_module(library(lists),
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[last/2,
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sumlist/2,
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sum_list/3,
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max_list/2,
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min_list/2
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]).
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:- use_module(library(matrix),
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[matrix_new/3,
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matrix_to_list/2,
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matrix_set/3]).
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:- use_module(dists, [get_dist_domain_size/2]).
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cpt_average(AllVars, Key, Els0, Tab, Vs, NewVs) :-
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cpt_average(AllVars, Key, Els0, 1.0, Tab, Vs, NewVs).
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% support variables with evidence from domain. This should make everyone's life easier.
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cpt_average([_|Vars], Key, Els0, Softness, p(Els0, CPT, NewEls), Vs, NewVs) :-
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find_evidence(Vars, 0, TotEvidence, RVars),
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build_avg_table(RVars, Vars, Els0, Key, TotEvidence, Softness, MAT, NewEls, Vs, NewVs),
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matrix_to_list(MAT, CPT).
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find_evidence([], TotEvidence, TotEvidence, []).
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find_evidence([V|Vars], TotEvidence0, TotEvidence, RVars) :-
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clpbn:get_atts(V,[evidence(Ev)]), !,
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TotEvidenceI is TotEvidence0+Ev,
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find_evidence(Vars, TotEvidenceI, TotEvidence, RVars).
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find_evidence([V|Vars], TotEvidence0, TotEvidence, [V|RVars]) :-
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find_evidence(Vars, TotEvidence0, TotEvidence, RVars).
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cpt_max([_|Vars], Key, Els0, CPT, Vs, NewVs) :-
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build_max_table(Vars, Els0, Els0, Key, 1.0, CPT, Vs, NewVs).
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cpt_min([_|Vars], Key, Els0, CPT, Vs, NewVs) :-
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build_min_table(Vars, Els0, Els0, Key, 1.0, CPT, Vs, NewVs).
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build_avg_table(Vars, OVars, Domain, _, TotEvidence, Softness, CPT, Vars, Vs, Vs) :-
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length(Domain, SDomain),
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int_power(Vars, SDomain, 1, TabSize),
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TabSize =< 256,
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/* case gmp is not there !! */
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TabSize > 0, !,
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average_cpt(Vars, OVars, Domain, TotEvidence, Softness, CPT).
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build_avg_table(Vars, OVars, Domain, Key, TotEvidence, Softness, CPT, [V1,V2], Vs, [V1,V2|NewVs]) :-
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length(Vars,L),
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LL1 is L//2,
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LL2 is L-LL1,
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list_split(LL1, Vars, L1, L2),
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Min = 0,
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length(Domain,Max1), Max is Max1-1,
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build_intermediate_table(LL1, sum(Min,Max), L1, V1, Key, 1.0, 0, I1, Vs, Vs1),
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build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, 1.0, I1, _, Vs1, NewVs),
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average_cpt([V1,V2], OVars, Domain, TotEvidence, Softness, CPT).
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build_max_table(Vars, Domain, Softness, p(Domain, CPT, Vars), Vs, Vs) :-
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length(Domain, SDomain),
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int_power(Vars, SDomain, 1, TabSize),
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TabSize =< 16,
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/* case gmp is not there !! */
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TabSize > 0, !,
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max_cpt(Vars, Domain, Softness, CPT).
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build_max_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2]), Vs, [V1,V2|NewVs]) :-
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length(Vars,L),
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LL1 is L//2,
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LL2 is L-LL1,
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list_split(LL1, Vars, L1, L2),
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build_intermediate_table(LL1, max(Domain,CPT), L1, V1, Key, 1.0, 0, I1, Vs, Vs1),
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build_intermediate_table(LL2, max(Domain,CPT), L2, V2, Key, 1.0, I1, _, Vs1, NewVs),
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max_cpt([V1,V2], Domain, Softness, CPT).
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build_min_table(Vars, Domain, Softness, p(Domain, CPT, Vars), Vs, Vs) :-
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length(Domain, SDomain),
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int_power(Vars, SDomain, 1, TabSize),
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TabSize =< 16,
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/* case gmp is not there !! */
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TabSize > 0, !,
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min_cpt(Vars, Domain, Softness, CPT).
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build_min_table(Vars, Domain, Softness, p(Domain, CPT, [V1,V2]), Vs, [V1,V2|NewVs]) :-
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length(Vars,L),
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LL1 is L//2,
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LL2 is L-LL1,
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list_split(LL1, Vars, L1, L2),
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build_intermediate_table(LL1, min(Domain,CPT), L1, V1, Key, 1.0, 0, I1, Vs, Vs1),
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build_intermediate_table(LL2, min(Domain,CPT), L2, V2, Key, 1.0, I1, _, Vs1, NewVs),
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min_cpt([V1,V2], Domain, Softness, CPT).
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int_power([], _, TabSize, TabSize).
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int_power([_|L], X, I0, TabSize) :-
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I is I0*X,
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int_power(L, X, I, TabSize).
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build_intermediate_table(1,_,[V],V, _, _, I, I, Vs, Vs) :- !.
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build_intermediate_table(2, Op, [V1,V2], V, Key, Softness, I0, If, Vs, Vs) :- !,
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If is I0+1,
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generate_tmp_random(Op, 2, [V1,V2], V, Key, Softness, I0).
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build_intermediate_table(N, Op, L, V, Key, Softness, I0, If, Vs, [V1,V2|NewVs]) :-
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LL1 is N//2,
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LL2 is N-LL1,
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list_split(LL1, L, L1, L2),
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I1 is I0+1,
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build_intermediate_table(LL1, Op, L1, V1, Key, Softness, I1, I2, Vs, Vs1),
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build_intermediate_table(LL2, Op, L2, V2, Key, Softness, I2, If, Vs1, NewVs),
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generate_tmp_random(Op, N, [V1,V2], V, Key, Softness, I0).
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% averages are transformed into sums.
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generate_tmp_random(sum(Min,Max), N, [V1,V2], V, Key, Softness, I) :-
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Lower is Min*N,
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Upper is Max*N,
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generate_list(Lower, Upper, Nbs),
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sum_cpt([V1,V2], Nbs, Softness, CPT),
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% write(sum(Nbs, CPT, [V1,V2])),nl, % debugging
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{ V = 'AVG'(I,Key) with p(Nbs,CPT,[V1,V2]) }.
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generate_tmp_random(max(Domain,CPT), _, [V1,V2], V, Key, I) :-
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{ V = 'MAX'(I,Key) with p(Domain,CPT,[V1,V2]) }.
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generate_tmp_random(min(Domain,CPT), _, [V1,V2], V, Key, I) :-
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{ V = 'MIN'(I,Key) with p(Domain,CPT,[V1,V2]) }.
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generate_list(M, M, [M]) :- !.
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generate_list(I, M, [I|Nbs]) :-
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I1 is I+1,
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generate_list(I1, M, Nbs).
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list_split(0, L, [], L) :- !.
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list_split(I, [H|L], [H|L1], L2) :-
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I1 is I-1,
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list_split(I1, L, L1, L2).
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%
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% generate actual table, instead of trusting the solver
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%
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average_cpt(Vs, OVars, Vals, Base, _, MCPT) :-
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get_ds_lengths(Vs,Lengs),
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length(OVars, N),
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length(Vals, SVals),
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Tot is (N-1)*SVals,
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Factor is SVals/Tot,
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matrix_new(floats,[SVals|Lengs],MCPT),
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fill_in_average(Lengs,Factor,Base,MCPT).
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get_ds_lengths([],[]).
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get_ds_lengths([V|Vs],[Sz|Lengs]) :-
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get_vdist_size(V, Sz),
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get_ds_lengths(Vs,Lengs).
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fill_in_average(Lengs, SVals, Base, MCPT) :-
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generate(Lengs, Case),
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average(Case, SVals, Base, Val),
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matrix_set(MCPT,[Val|Case],1.0),
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fail.
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fill_in_average(_,_,_,_).
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generate([], []).
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generate([N|Lengs], [C|Case]) :-
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from(0,N,C),
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generate(Lengs, Case).
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from(I,_,I).
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from(I1,M,J) :-
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I is I1+1,
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I < M,
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from(I,M,J).
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average(Case, SVals, Base, Val) :-
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sum_list(Case, Base, Tot),
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Val is integer(round(Tot*SVals)).
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sum_cpt(Vs,Vals,_,CPT) :-
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get_ds_lengths(Vs,Lengs),
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length(Vals,SVals),
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matrix_new(floats,[SVals|Lengs],MCPT),
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fill_in_sum(Lengs,MCPT),
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matrix_to_list(MCPT,CPT).
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fill_in_sum(Lengs,MCPT) :-
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generate(Lengs, Case),
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sumlist(Case, Val),
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matrix_set(MCPT,[Val|Case],1.0),
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fail.
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fill_in_sum(_,_).
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max_cpt(Vs,Vals,_,CPT) :-
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get_ds_lengths(Vs,Lengs),
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length(Vals,SVals),
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matrix_new(floats,[SVals|Lengs],MCPT),
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fill_in_max(Lengs,MCPT),
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matrix_to_list(MCPT,CPT).
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fill_in_max(Lengs,MCPT) :-
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generate(Lengs, Case),
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max_list(Case, Val),
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matrix_set(MCPT,[Val|Case],1.0),
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fail.
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fill_in_max(_,_).
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min_cpt(Vs,Vals,_,CPT) :-
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get_ds_lengths(Vs,Lengs),
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length(Vals,SVals),
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matrix_new(floats,[SVals|Lengs],MCPT),
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fill_in_max(Lengs,MCPT),
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matrix_to_list(MCPT,CPT).
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fill_in_min(Lengs,MCPT) :-
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generate(Lengs, Case),
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max_list(Case, Val),
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matrix_set(MCPT,[Val|Case],1.0),
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fail.
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fill_in_min(_,_).
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get_vdist_size(V, Sz) :-
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clpbn:get_atts(V, [dist(Dist,_)]),
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get_dist_domain_size(Dist, Sz).
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