/usr/share/Yap/clpbn/bp.yap is in yap 6.2.2-6.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Belief Propagation in CLP(BN)
This should connect to C-code.
*********************************/
:- module(clpbn_bp, [bp/3,
check_if_bp_done/1,
init_bp_solver/4,
run_bp_solver/3]).
:- attribute all_diffs/1.
:- use_module(library(ordsets),
[ord_union/3,
ord_member/2]).
:- use_module(library('clpbn/matrix_cpt_utils'),
[reorder_CPT/5]).
:- use_module(library('clpbn/dists'),
[
dist/4,
get_dist_domain/2,
get_dist_params/2]).
:- use_module(library('clpbn/utils'), [
clpbn_not_var_member/2]).
:- use_module(library('clpbn/display'), [
clpbn_bind_vals/3]).
:- use_module(library('clpbn/connected'),
[
init_influences/3,
influences/5
]).
:- use_module(library(lists),
[
append/3
]).
:- use_module(library('clpbn/aggregates'),
[check_for_agg_vars/2]).
check_if_bp_done(_Var).
%
% implementation of belief propagation
%
% A1=+QueryVars -> sets of independent marginalization variables
% A2=*AllVars -> list
% A3=-Output -> output probabilities
%
%
% Other important variables:
%
% State0 initialized graph, is used to pass data from initialization
% to query solving (eg, State might be the JT and be used to run
% different queries).
%
% Process
%
bp([[]],_,_) :- !.
bp([QueryVars],AllVars,Output) :-
writeln(QueryVars:AllVars:Output),
init_bp_solver([QueryVars], AllVars, Output, State),
writeln(State),
% variable elimination proper
run_bp_solver([], [LPs], State),
% bind Probs back to variables so that they can be output.
clpbn_bind_vals([QueryVars],[LPs],Output).
% initialise necessary data for query solver
init_bp_solver(Qs, AllVars, _, graph(LVis)) :-
% replace average, max, min and friends
% by binary nodes.
check_for_agg_vars(AllVars, UnFoldedVars),
writeln(AllVars:UnFoldedVars),
% replace the variables reachable from G
% Tables0 will have the full data on each variable
init_influences(UnfoldedVars, G, RG),
writeln(G:RG),
init_bp_solver_for_questions(Qs, G, RG, _, LVis).
init_bp_solver_for_questions([], _, _, [], []).
init_bp_solver_for_questions([Vs|MVs], G, RG, [NVs|MNVs0], [NVs|LVis]) :-
% find variables connectd to Vs
% influences(Vs, _, NVs0, G, RG),
G = RG,
sort(NVs0, NVs),
%clpbn_gviz:clpbn2gviz(user_error, test, NVs, Vs),
init_bp_solver_for_questions(MVs, G, RG, MNVs0, LVis).
% use a findall to recover space without needing for GC
run_bp_solver(LVs, LPs, graph(LNVs)) :-
findall(Ps, solve_bp(LVs, LNVs, Ps), LPs).
solve_bp([LVs|_], [NVs0|_], Ps) :-
% length(NVs0, L), (L > 64 -> clpbn_gviz:clpbn2gviz(user_error,sort,NVs0,LVs) ; true ),
find_all_clpbn_vars(NVs0, LVi),
% construct the graph
process(LVi, LVs, P).
solve_bp([_|MoreLVs], [_|MoreLVis], Ps) :-
solve_bp(MoreLVs, MoreLVis, Ps).
% get a list of variables plus associated tables
%
find_all_clpbn_vars([], []).
find_all_clpbn_vars([V|Vs], [var(V,Id,Parents,Domain,Matrix,Ev)|LV]) :-
clpbn:get_atts(V, [dist(Id,Parents)]), !,
get_dist_domain(Id, Domain),
get_dist_params(Id, Matrix),
get_evidence(V, Ev),
find_all_clpbn_vars(Vs, LV).
find_all_clpbn_vars([_|Vs], LV) :-
find_all_clpbn_vars(Vs, LV).
get_evidence(V, Ev) :-
clpbn:get_atts(V, [evidence(Ev)]), !.
get_evidence(V, -1). % no evidence!!!
% to be defined in C
% +LVO is the list of all variables
% +InputVs are the variables to be marginalised
% -Out is some output term stating the probabilities
%
process(LV0, InputVs, Out) :-
length(LV0, N),
length(InputVs, NI),
writeln(process(LV0, InputVs, Out)),
bp_process(N, LV0, NI, InputVs, Out),
fail.
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