:- module(clpbn, [{}/1, clpbn_flag/2, set_clpbn_flag/2, clpbn_flag/3, clpbn_key/2, clpbn_init_solver/4, clpbn_run_solver/3, clpbn_init_solver/5, clpbn_run_solver/4]). :- use_module(library(atts)). :- use_module(library(lists)). :- use_module(library(terms)). :- op( 500, xfy, with). % % avoid the overhead of using goal_expansion/2. % :- multifile user:term_expansion/2. :- dynamic user:term_expansion/2. :- attribute key/1, dist/2, evidence/1, starter/0. :- use_module('clpbn/vel', [vel/3, check_if_vel_done/1, init_vel_solver/4, run_vel_solver/3 ]). :- use_module('clpbn/jt', [jt/3, init_jt_solver/4, run_jt_solver/3 ]). :- use_module('clpbn/bnt', [do_bnt/3, check_if_bnt_done/1 ]). :- use_module('clpbn/gibbs', [gibbs/3, check_if_gibbs_done/1, init_gibbs_solver/4, run_gibbs_solver/3 ]). :- use_module('clpbn/graphs', [ clpbn2graph/1 ]). :- use_module('clpbn/dists', [ dist/4, get_dist/4, get_evidence_position/3, get_evidence_from_position/3 ]). :- use_module('clpbn/evidence', [ store_evidence/1, incorporate_evidence/2, check_stored_evidence/2, add_evidence/2 ]). :- use_module('clpbn/utils', [ sort_vars_by_key/3 ]). :- use_module('clpbn/graphviz', [clpbn2gviz/4]). :- dynamic solver/1,output/1,use/1,suppress_attribute_display/1, parameter_softening/1, em_solver/1. solver(vel). em_solver(vel). %output(xbif(user_error)). %output(gviz(user_error)). output(no). suppress_attribute_display(false). parameter_softening(m_estimate(10)). clpbn_flag(Flag,Option) :- clpbn_flag(Flag, Option, Option). set_clpbn_flag(Flag,Option) :- clpbn_flag(Flag, _, Option). clpbn_flag(output,Before,After) :- retract(output(Before)), assert(output(After)). clpbn_flag(solver,Before,After) :- retract(solver(Before)), assert(solver(After)). clpbn_flag(em_solver,Before,After) :- retract(em_solver(Before)), assert(em_solver(After)). clpbn_flag(bnt_solver,Before,After) :- retract(bnt:bnt_solver(Before)), assert(bnt:bnt_solver(After)). clpbn_flag(bnt_path,Before,After) :- retract(bnt:bnt_path(Before)), assert(bnt:bnt_path(After)). clpbn_flag(bnt_model,Before,After) :- retract(bnt:bnt_model(Before)), assert(bnt:bnt_model(After)). clpbn_flag(suppress_attribute_display,Before,After) :- retract(suppress_attribute_display(Before)), assert(suppress_attribute_display(After)). clpbn_flag(parameter_softening,Before,After) :- retract(parameter_softening(Before)), assert(parameter_softening(After)). {_} :- solver(none), !. {Var = Key with Dist} :- put_atts(El,[key(Key),dist(DistInfo,Parents)]), dist(Dist, DistInfo, Key, Parents), add_evidence(Var,Key,DistInfo,El). check_constraint(Constraint, _, _, Constraint) :- var(Constraint), !. check_constraint((A->D), _, _, (A->D)) :- var(A), !. check_constraint((([A|B].L)->D), Vars, NVars, (([A|B].NL)->D)) :- !, check_cpt_input_vars(L, Vars, NVars, NL). check_constraint(Dist, _, _, Dist). check_cpt_input_vars([], _, _, []). check_cpt_input_vars([V|L], Vars, NVars, [NV|NL]) :- replace_var(Vars, V, NVars, NV), check_cpt_input_vars(L, Vars, NVars, NL). replace_var([], V, [], V). replace_var([V|_], V0, [NV|_], NV) :- V == V0, !. replace_var([_|Vars], V, [_|NVars], NV) :- replace_var(Vars, V, NVars, NV). add_evidence(V,Key,Distinfo,NV) :- nonvar(V), !, get_evidence_position(V, Distinfo, Pos), check_stored_evidence(Key, Pos), clpbn:put_atts(NV,evidence(Pos)). add_evidence(V,K,_,V) :- add_evidence(K,V). clpbn_marginalise(V, Dist) :- attributes:all_attvars(AVars), project_attributes([V], AVars), vel:get_atts(V, posterior(_,_,Dist,_)). % % called by top-level % or by call_residue/2 % project_attributes(GVars, AVars) :- suppress_attribute_display(false), AVars = [_|_], solver(Solver), ( GVars = [_|_] ; Solver = graphs), !, clpbn_vars(AVars, DiffVars, AllVars), get_clpbn_vars(GVars,CLPBNGVars0), simplify_query_vars(CLPBNGVars0, CLPBNGVars), (output(xbif(XBifStream)) -> clpbn2xbif(XBifStream,vel,AllVars) ; true), (output(gviz(XBifStream)) -> clpbn2gviz(XBifStream,sort,AllVars,GVars) ; true), ( Solver = graphs -> write_out(Solver, [[]], AllVars, DiffVars) ; write_out(Solver, [CLPBNGVars], AllVars, DiffVars) ). project_attributes(_, _). clpbn_vars(AVars, DiffVars, AllVars) :- sort_vars_by_key(AVars,SortedAVars,DiffVars), incorporate_evidence(SortedAVars, AllVars). get_clpbn_vars([],[]). get_clpbn_vars([V|GVars],[V|CLPBNGVars]) :- get_atts(V, [key(_)]), !, get_clpbn_vars(GVars,CLPBNGVars). get_clpbn_vars([_|GVars],CLPBNGVars) :- get_clpbn_vars(GVars,CLPBNGVars). simplify_query_vars(LVs0, LVs) :- sort(LVs0,LVs1), get_rid_of_ev_vars(LVs1,LVs). % % some variables might already have evidence in the data-base. % get_rid_of_ev_vars([],[]). get_rid_of_ev_vars([V|LVs0],LVs) :- clpbn:get_atts(V, [dist(Id,_),evidence(Pos)]), !, get_evidence_from_position(Ev, Id, Pos), clpbn_display:put_atts(V, [posterior([],Ev,[],[])]), !, get_rid_of_ev_vars(LVs0,LVs). get_rid_of_ev_vars([V|LVs0],[V|LVs]) :- get_rid_of_ev_vars(LVs0,LVs). % do nothing if we don't have query variables to compute. write_out(graphs, _, AVars, _) :- clpbn2graph(AVars). write_out(vel, GVars, AVars, DiffVars) :- vel(GVars, AVars, DiffVars). write_out(jt, GVars, AVars, DiffVars) :- jt(GVars, AVars, DiffVars). write_out(gibbs, GVars, AVars, DiffVars) :- gibbs(GVars, AVars, DiffVars). write_out(bnt, GVars, AVars, DiffVars) :- do_bnt(GVars, AVars, DiffVars). get_bnode(Var, Goal) :- get_atts(Var, [key(Key),dist(Dist,Parents)]), get_dist(Dist,_,Domain,CPT), (Parents = [] -> X = tab(Domain,CPT) ; X = tab(Domain,CPT,Parents)), dist_goal(X, Key, Goal0), include_evidence(Var, Goal0, Key, Goali), include_starter(Var, Goali, Key, Goal). include_evidence(Var, Goal0, Key, ((Key:-Ev),Goal0)) :- get_atts(Var, [evidence(Ev)]), !. include_evidence(_, Goal0, _, Goal0). include_starter(Var, Goal0, Key, ((:-Key),Goal0)) :- get_atts(Var, [starter]), !. include_starter(_, Goal0, _, Goal0). dist_goal(Dist, Key, (Key=NDist)) :- term_variables(Dist, DVars), process_vars(DVars, DKeys), my_copy_term(Dist,DVars, NDist,DKeys). my_copy_term(V, DVars, Key, DKeys) :- find_var(DVars, V, Key, DKeys). my_copy_term(A, _, A, _) :- atomic(A), !. my_copy_term(T, Vs, NT, Ks) :- T =.. [Na|As], my_copy_terms(As, Vs, NAs, Ks), NT =.. [Na|NAs]. my_copy_terms([], _, [], _). my_copy_terms([A|As], Vs, [NA|NAs], Ks) :- my_copy_term(A, Vs, NA, Ks), my_copy_terms(As, Vs, NAs, Ks). find_var([V1|_], V, Key, [Key|_]) :- V1 == V, !. find_var([_|DVars], V, Key, [_|DKeys]) :- find_var(DVars, V, Key, DKeys). process_vars([], []). process_vars([V|Vs], [K|Ks]) :- process_var(V, K), process_vars(Vs, Ks). process_var(V, K) :- get_atts(V, [key(K)]), !. % oops: this variable has no attributes. process_var(V, _) :- throw(error(instantiation_error,clpbn(attribute_goal(V)))). % % unify a CLPBN variable with something. % verify_attributes(Var, T, Goals) :- get_atts(Var, [key(Key),dist(Dist,Parents)]), !, /* oops, someone trying to bind a clpbn constrained variable */ Goals = [], bind_clpbn(T, Var, Key, Dist, Parents). verify_attributes(_, _, []). bind_clpbn(T, Var, _, _, _) :- nonvar(T), !, ( add_evidence(Var,T) -> true ; writeln(T:Var), fail ). bind_clpbn(T, Var, Key, Dist, Parents) :- var(T), get_atts(T, [key(Key1),dist(Dist1,Parents1)]), !, bind_clpbns(Key, Dist, Parents, Key1, Dist1, Parents1), ( get_atts(T, [evidence(Ev1)]) -> bind_evidence_from_extra_var(Ev1,Var) ; get_atts(Var, [evidence(Ev)]) -> bind_evidence_from_extra_var(Ev,T) ; true). bind_clpbn(_, Var, _, _, _, _) :- use(bnt), check_if_bnt_done(Var), !. bind_clpbn(_, Var, _, _, _, _) :- use(vel), check_if_vel_done(Var), !. bind_clpbn(_, Var, _, _, _, _) :- use(jt), check_if_vel_done(Var), !. bind_clpbn(T, Var, Key0, _, _, _) :- get_atts(Var, [key(Key)]), !, ( Key = Key0 -> true ; add_evidence(Var,T) ). fresh_attvar(Var, NVar) :- get_atts(Var, LAtts), put_atts(NVar, LAtts). % I will now allow two CLPBN variables to be bound together. %bind_clpbns(Key, Dist, Parents, Key, Dist, Parents). bind_clpbns(Key, Dist, Parents, Key1, Dist1, Parents1) :- Key == Key1, !, get_dist(Dist,Type,Domain,Table), get_dist(Dist1,Type1,Domain1,Table1), ( Dist == Dist1, same_parents(Parents,Parents1) -> true ; throw(error(domain_error(bayesian_domain),bind_clpbns(var(Dist, Key, Type, Domain, Table, Parents),var(Dist1, Key1, Type1, Domain1, Table1, Parents1)))) ). bind_clpbns(Key, _, _, _, Key1, _, _, _) :- Key\=Key1, !, fail. bind_clpbns(_, _, _, _, _, _, _, _) :- format(user_error, 'unification of two bayesian vars not supported~n', []). same_parents([],[]). same_parents([P|Parents],[P1|Parents1]) :- same_node(P,P1), same_parents(Parents,Parents1). same_node(P,P1) :- P == P1, !. same_node(P,P1) :- get_atts( P,[key(K)]), get_atts(P1,[key(K)]), P = P1. bind_evidence_from_extra_var(Ev1,Var) :- get_atts(Var, [evidence(Ev0)]), !, Ev0 = Ev1. bind_evidence_from_extra_var(Ev1,Var) :- put_atts(Var, [evidence(Ev1)]). user:term_expansion((A :- {}), ( :- true )) :- !, % evidence prolog_load_context(module, M), store_evidence(M:A). clpbn_key(Var,Key) :- get_atts(Var, [key(Key)]). % % This is a routine to start a solver, called by the learning procedures (ie, em). % LVs is a list of lists of variables one is interested in eventually marginalising out % Vs0 gives the original graph % AllDiffs gives variables that are not fully constrainted, ie, we don't fully know % the key. In this case, we assume different instances will be bound to different % values at the end of the day. % clpbn_init_solver(LVs, Vs0, VarsWithUnboundKeys, State) :- solver(Solver), clpbn_init_solver(Solver, LVs, Vs0, VarsWithUnboundKeys, State). clpbn_init_solver(gibbs, LVs, Vs0, VarsWithUnboundKeys, State) :- init_gibbs_solver(LVs, Vs0, VarsWithUnboundKeys, State). clpbn_init_solver(vel, LVs, Vs0, VarsWithUnboundKeys, State) :- init_vel_solver(LVs, Vs0, VarsWithUnboundKeys, State). clpbn_init_solver(jt, LVs, Vs0, VarsWithUnboundKeys, State) :- init_jt_solver(LVs, Vs0, VarsWithUnboundKeys, State). % % LVs is the list of lists of variables to marginalise % Vs is the full graph % Ps are the probabilities on LVs. % % clpbn_run_solver(LVs, LPs, State) :- solver(Solver, State), clpbn_run_solver(Solver, LVs, LPs, State). clpbn_run_solver(gibbs, LVs, LPs, State) :- run_gibbs_solver(LVs, LPs, State). clpbn_run_solver(vel, LVs, LPs, State) :- run_vel_solver(LVs, LPs, State). clpbn_run_solver(jt, LVs, LPs, State) :- run_jt_solver(LVs, LPs, State).