new implementation of aggregates, using matrices.
This commit is contained in:
Vitor Santos Costa
parent
22b17b5856
commit
922424abd0
@ -8,72 +8,81 @@
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:- use_module(library(clpbn), [{}/1]).
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:- use_module(library(lists), [last/2]).
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:- use_module(library(lists),
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[last/2,
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sumlist/2,
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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(Vars, Key, Els0, CPT) :-
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check_domain(Els0, Els),
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length(Els, SDomain),
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build_avg_table(Vars, Els, SDomain, Els0, Key, 1.0, CPT).
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build_avg_table(Vars, Els0, Key, 1.0, CPT).
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cpt_average(Vars, Key, Els0, Softness, CPT) :-
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check_domain(Els0, Els),
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length(Els, SDomain),
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build_avg_table(Vars, Els, SDomain, Els0, Key, Softness, CPT).
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build_avg_table(Vars, Els0, Key, Softness, CPT).
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cpt_max(Vars, Key, Els0, CPT) :-
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check_domain(Els0, Els),
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length(Els, SDomain),
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build_max_table(Vars, Els, SDomain, Els0, Key, CPT).
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build_max_table(Vars, Els0, Els0, Key, 1.0, CPT).
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cpt_min(Vars, Key, Els0, CPT) :-
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check_domain(Els0, Els),
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length(Els, SDomain),
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build_min_table(Vars, Els, SDomain, Els0, Key, CPT).
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build_min_table(Vars, Els0, Els0, Key, 1.0, CPT).
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build_avg_table(Vars, Domain, SDomain, ODomain, _, 1.0, p(ODomain, CPT, Vars)) :-
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build_avg_table(Vars, Domain, _, Softness, p(Domain, CPT, Vars)) :-
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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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average_cpt(Vars, Domain, CPT).
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build_avg_table(Vars, Domain, _, ODomain, Key, Softness, p(ODomain, CPT, [V1,V2])) :-
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average_cpt(Vars, Domain, Softness, CPT).
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build_avg_table(Vars, Domain, Key, Softness, p(Domain, CPT, [V1,V2])) :-
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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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Domain = [Min|Els1],
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last(Els1,Max),
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build_intermediate_table(LL1, sum(Min,Max), L1, V1, Key, Softness, 0, I1),
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build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, Softness, I1, _),
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normalised_average_cpt(L, [V1,V2], Domain, Softness, CPT).
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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),
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build_intermediate_table(LL2, sum(Min,Max), L2, V2, Key, 1.0, I1, _),
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average_cpt([V1,V2], Domain, Softness, CPT).
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build_max_table(Vars, Domain, SDomain, ODomain, _, p(ODomain, CPT, Vars)) :-
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build_max_table(Vars, Domain, Softness, p(Domain, CPT, Vars)) :-
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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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max_cpt(Vars, Domain, CPT).
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build_max_table(Vars, Domain, _, ODomain, Key, p(ODomain, CPT, [V1,V2])) :-
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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])) :-
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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),
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build_intermediate_table(LL2, max(Domain,CPT), L2, V2, Key, 1.0, I1, _),
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max_cpt([V1,V2], Domain, CPT).
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max_cpt([V1,V2], Domain, Softness, CPT).
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build_min_table(Vars, Domain, SDomain, ODomain, _, p(ODomain, CPT, Vars)) :-
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build_min_table(Vars, Domain, Softness, p(Domain, CPT, Vars)) :-
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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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min_cpt(Vars, Domain, CPT).
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build_min_table(Vars, Domain, _, ODomain, Key, p(ODomain, CPT, [V1,V2])) :-
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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])) :-
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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),
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build_intermediate_table(LL2, min(Domain,CPT), L2, V2, Key, 1.0, I1, _),
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min_cpt([V1,V2], Domain, CPT).
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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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@ -116,185 +125,93 @@ 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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% allow quick description for a range.
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check_domain([I0|Is], [I0|Is]) :-
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integer(I0),
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check_integer_domain(Is,I0), !.
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check_domain(D, ND) :-
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normalise_domain(D, 0, ND).
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check_integer_domain([],_).
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check_integer_domain([I1|Is],I0) :-
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I0 < I1,
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check_integer_domain(Is,I1).
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normalise_domain([], _, []).
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normalise_domain([_|D], I0, [I0|ND]) :-
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I is I0+1,
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normalise_domain(D, I, ND).
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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,Vals,_,CPT) :-
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get_ds_lengths(Vs,Lengs),
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sumlist(Lengs, Tot),
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length(Vals,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,MCPT),
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matrix_to_list(MCPT,CPT).
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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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average_cpt(Vs,Vals,CPT) :-
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generate_indices(Vals,Inds,0,Av),
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combine_all(Vs, Inds, Cs),
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length(Vs, Max),
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average_possible_cases(0, Av, Max, Cs, 1.0, CPT).
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fill_in_average(Lengs,SVals,MCPT) :-
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generate(Lengs, Case),
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average(Case, SVals, 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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sum_cpt(Vs, Vals, Softness, CPT) :-
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length(Vals,Sz),
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combine_all(Vs, Cs),
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sum_possible_cases(0, Sz, Cs, Softness, CPT).
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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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normalised_average_cpt(Max, Vs, Vals, Softness, CPT) :-
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generate_indices(Vals,_,0,Sz),
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combine_all(Vs, Cs),
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average_possible_cases(0, Sz, Max, Cs, Softness, CPT).
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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, Val) :-
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sumlist(Case, Tot),
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Val is integer(round(Tot*SVals)).
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generate_indices([],[],Av,Av).
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generate_indices([_|Ls],[I|Inds],I,Av) :-
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I1 is I+1,
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generate_indices(Ls,Inds,I1,Av).
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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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combine_all([], [[]]).
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combine_all([V|LV], Cs) :-
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combine_all(LV, Cs0),
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get_vdist_size(V,Sz),
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generate_indices(0, Sz, Vals),
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add_vals(Vals, Cs0, Cs).
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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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combine_all([], _, [[]]).
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combine_all([_|LV], Vals, Cs) :-
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combine_all(LV, Vals, Cs0),
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add_vals(Vals, Cs0, Cs).
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generate_indices(Sz,Sz,[]) :- !.
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generate_indices(I0,Sz,[I0|Vals]) :-
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I is I0+1,
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generate_indices(I,Sz,Vals).
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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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add_vals([], _, []).
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add_vals([V|Vs], Cs0, Csf) :-
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add_vals(Vs, Cs0, Cs),
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add_val_to_cases(Cs0, V, Cs, Csf).
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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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add_val_to_cases([], _, Cs, Cs).
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add_val_to_cases([C|Cs], V, Cs0, [[V|C]|Csf]) :-
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add_val_to_cases(Cs, V, Cs0, Csf).
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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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sum_all([],N,N).
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sum_all([C|Cs],N0,N) :-
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X is C+N0,
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sum_all(Cs,X,N).
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average_possible_cases(Av,Av,_,_,_,[]) :- !.
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average_possible_cases(I,Av,Max,Cs,Softness,Lf) :-
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average_cases2(Cs,I,Av,Softness,Lf,L0),
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I1 is I+1,
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average_possible_cases(I1,Av,Max,Cs,Softness,L0).
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average_cases2([], _, _, _, L, L).
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average_cases2([C|Cs], I, Av, Softness, [P|Lf], L0) :-
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calculate_avg_prob(C, I, Av, Softness, P),
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average_cases2(Cs, I, Av, Softness, Lf, L0).
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calculate_avg_prob(C, I, Av, Softness, Softness) :-
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sum_all(C,0,N),
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I =:= integer(round(N/Av)), !.
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calculate_avg_prob(_, _, Av, Softness, Comp) :-
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Comp is (1.0-Softness)/(Av-1).
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sum_possible_cases(Av,Av,_, _, []) :- !.
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sum_possible_cases(I,Av,Cs,Softness, Lf) :-
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sum_cases2(Cs,I, Av, Softness, Lf,L0),
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I1 is I+1,
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sum_possible_cases(I1,Av,Cs,Softness, L0).
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sum_cases2([], _, _, _, L, L).
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sum_cases2([C|Cs], I, Av, Softness, [P|Lf], L0) :-
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calculate_sum_prob(C, I, Av, Softness, P),
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sum_cases2(Cs, I, Av, Softness, Lf, L0).
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calculate_sum_prob(C, I, _, Softness, Softness) :-
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sum_all(C,0,N),
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I =:= N, !.
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calculate_sum_prob(_, _, Av, Softness, Comp) :-
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Comp is (1.0-Softness)/(Av-1).
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%
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% generate a CPT for max.
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%
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max_cpt(Vs, Domain, CPT) :-
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combinations(Vs, Domain, Combinations),
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cpt_from_domain(Domain, Combinations, Domain, max, CPT).
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min_cpt(Vs, Domain, CPT) :-
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combinations(Vs, Domain, Combinations),
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cpt_from_domain(Domain, Combinations, Domain, min, CPT).
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combinations(Vs, Domain, Combinations) :-
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mult_domains(Vs, Domain, Domains),
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cart(Domains, Combinations).
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mult_domains([], _, []).
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mult_domains([_|Vs], Domain, [Domain|Domains]) :-
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mult_domains(Vs, Domain, Domains).
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cart([], [[]]).
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cart([L|R], Rf) :-
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cart(R, R1),
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add(L, R1, Rf).
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add([], _, []).
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add([A|R], R1, RsF) :-
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add_head(R1, A, RsF, Rs0),
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add(R, R1, Rs0).
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add_head([], _, Rs, Rs).
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add_head([H|L], A, [[A|H]|Rs], Rs0) :-
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add_head(L, A, Rs, Rs0).
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cpt_from_domain([], _, _, _, []).
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cpt_from_domain([El|Domain], Combinations, Domain0, OP, CPT) :-
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cpt_from_domain_el(Combinations, El, Domain0, OP, CPT, CPT0),
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cpt_from_domain(Domain, Combinations, Domain0, OP, CPT0).
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cpt_from_domain_el([], _, _, _, CPT, CPT).
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cpt_from_domain_el([C|Combinations], El, Domain, OP, [P|CPT], CPT0) :-
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cpt_for_el(C, OP, El, Domain, 0.0, P),
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cpt_from_domain_el(Combinations, El, Domain, OP, CPT, CPT0).
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cpt_for_el([], _, _, _, P, P).
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cpt_for_el([El|Cs], MAX, El, Domain, _, P) :- !,
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cpt_for_el(Cs, MAX, El, Domain, 1.0, P).
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cpt_for_el([C|_], MAX, El, Domain, _, 0.0) :-
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op_broken(MAX, C, El, Domain), !.
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cpt_for_el([_|Cs], MAX, El, Domain, P0, P) :-
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cpt_for_el(Cs, MAX, El, Domain, P0, P).
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op_broken(max, C, El, Domain) :-
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lg(Domain, C, El).
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op_broken(min, C, El, Domain) :-
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sm(Domain, C, El).
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lg([El|_], _, El) :- !.
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lg([C|_], C, _) :- !, fail.
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lg([_|Vs], C, El) :-
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lg(Vs, C, El).
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sm([El|_], _, El) :- !, fail.
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sm([V|_], V, _) :- !.
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sm([_|Vs], C, El) :-
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sm(Vs, C, El).
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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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