improve CLP(BN)/BNT interface
git-svn-id: https://yap.svn.sf.net/svnroot/yap/trunk@1918 b08c6af1-5177-4d33-ba66-4b1c6b8b522a
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@ -1151,10 +1151,10 @@ RecoverDelayArena(Term delay_arena)
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{
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attvar_record *pt = DelayArenaPt(delay_arena),
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*max = DelayTop();
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if (max == pt-DelayArenaSz(delay_arena))
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if (max == pt-DelayArenaSz(delay_arena)) {
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SetDelayTop(pt);
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}
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}
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#endif
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static void
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5
C/grow.c
5
C/grow.c
@ -698,12 +698,11 @@ static_growglobal(long size, CELL **ptr, CELL *hsplit)
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do_grow = FALSE;
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}
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}
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size0 = size;
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if (size < ((char *)H0-omax)/8)
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size = ((char *)H0-omax)/8;
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if (do_grow) {
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size0 = size = AdjustPageSize(size);
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} else {
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size0 = size;
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size = AdjustPageSize(size);
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}
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/* adjust to a multiple of 256) */
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Yap_ErrorMessage = NULL;
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@ -333,6 +333,7 @@ write_var(CELL *t, struct write_globs *wglb)
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/* make sure we don't get no creepy spaces where they shouldn't be */
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lastw = separator;
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if (CellPtr(t) < H0) {
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Int vcount = (H0-t)/(sizeof(attvar_record)/sizeof(CELL));
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#if COROUTINING
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#if DEBUG
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if (Yap_Portray_delays) {
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@ -365,7 +366,7 @@ write_var(CELL *t, struct write_globs *wglb)
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}
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#endif
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wrputc('D', wglb->writewch);
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wrputn((Int) ((attvar_record *)H0-(attvar_record *)t),wglb->writewch);
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wrputn(vcount,wglb->writewch);
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#endif
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} else {
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wrputn(((Int) (t- H0)),wglb->writewch);
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@ -81,6 +81,9 @@ clpbn_flag(bnt_solver,Before,After) :-
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clpbn_flag(bnt_path,Before,After) :-
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retract(bnt:bnt_path(Before)),
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assert(bnt:bnt_path(After)).
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clpbn_flag(bnt_model,Before,After) :-
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retract(bnt:bnt_model(Before)),
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assert(bnt:bnt_model(After)).
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{Var = Key with Dist} :-
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put_atts(El,[key(Key),dist(DistInfo,Parents)]),
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@ -121,12 +124,15 @@ project_attributes(GVars, AVars) :-
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AVars = [_|_],
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solver(Solver),
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( GVars = [_|_] ; Solver = graphs), !,
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sort_vars_by_key(AVars,SortedAVars,DiffVars),
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clpbn_vars(AVars, DiffVars, AllVars),
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get_clpbn_vars(GVars,CLPBNGVars),
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incorporate_evidence(SortedAVars, AllVars),
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write_out(Solver,CLPBNGVars, AllVars, DiffVars).
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project_attributes(_, _).
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clpbn_vars(AVars, DiffVars, AllVars) :-
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sort_vars_by_key(AVars,SortedAVars,DiffVars),
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incorporate_evidence(SortedAVars, AllVars).
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get_clpbn_vars([],[]).
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get_clpbn_vars([V|GVars],[V|CLPBNGVars]) :-
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get_atts(V, [key(_)]), !,
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@ -236,12 +242,24 @@ bind_clpbns(Key, Dist, Parents, Key1, Dist1, Parents1) :-
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Key == Key1, !,
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get_dist(Dist,Type,Domain,Table),
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get_dist(Dist1,Type1,Domain1,Table1),
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( Dist == Dist1, Parents == Parents1 -> true ; throw(error(domain_error(bayesian_domain),bind_clpbns(var(Key, Type, Domain, Table, Parents),var(Key1, Type1, Domain1, Table1, Parents1))))).
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( Dist == Dist1, same_parents(Parents,Parents1) -> true ; throw(error(domain_error(bayesian_domain),bind_clpbns(var(Key, Type, Domain, Table, Parents),var(Key1, Type1, Domain1, Table1, Parents1))))).
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bind_clpbns(Key, _, _, _, Key1, _, _, _) :-
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Key\=Key1, !, fail.
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bind_clpbns(_, _, _, _, _, _, _, _) :-
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format(user_error, 'unification of two bayesian vars not supported~n', []).
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same_parents([],[]).
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same_parents([P|Parents],[P1|Parents1]) :-
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same_node(P,P1),
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same_parents(Parents,Parents1).
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same_node(P,P1) :- P == P1, !.
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same_node(P,P1) :-
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get_atts( P,[key(K)]),
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get_atts(P1,[key(K)]),
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P = P1.
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bind_evidence_from_extra_var(Ev1,Var) :-
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get_atts(Var, [evidence(Ev0)]),!,Ev0 = Ev1.
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bind_evidence_from_extra_var(Ev1,Var) :-
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@ -295,5 +295,6 @@ 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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get_dist_domain_size(Dist, Sz).
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@ -1,5 +1,5 @@
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:- module(bnt, [do_bnt/3,
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create_bnt_graph/2,
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check_if_bnt_done/1]).
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:- use_module(library('clpbn/display'), [
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@ -8,7 +8,11 @@
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:- use_module(library('clpbn/dists'), [
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get_dist_domain_size/2,
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get_dist_domain/2,
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get_dist_params/2]).
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get_dist_params/2
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]).
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:- use_module(library('clpbn/discrete_utils'), [
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reorder_CPT/5]).
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:- use_module(library(matlab), [start_matlab/1,
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close_matlab/0,
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@ -16,6 +20,7 @@
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matlab_eval_string/1,
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matlab_eval_string/2,
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matlab_matrix/4,
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matlab_vector/2,
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matlab_sequence/3,
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matlab_initialized_cells/4,
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matlab_get_variable/2,
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@ -30,6 +35,12 @@
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dgraph_edges/2
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]).
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:- use_module(library(lists), [
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member/2]).
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:- use_module(library(ordsets), [
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ord_insert/3]).
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:- yap_flag(write_strings,on).
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% syntactic sugar for matlab_call.
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@ -42,7 +53,7 @@ G <-- Y :-
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:- dynamic bnt/1.
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:- dynamic bnt_solver/1, bnt_path/1.
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:- dynamic bnt_solver/1, bnt_path/1, bnt_model/1.
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% belprop
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bnt_solver(jtree).
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@ -50,7 +61,13 @@ bnt_solver(jtree).
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bnt_path('/u/vitor/Yap/CLPBN/FullBNT-1.0.3/BNT').
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%
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% What BNT are we using:
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% a propositional one
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% a tied parameter one.
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%
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%bnt_model(propositional).
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bnt_model(tied).
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/*****************************************
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@ -71,16 +88,23 @@ check_if_bnt_done(Var) :-
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do_bnt([], _, _) :- !.
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do_bnt(QueryVars, AllVars, AllDiffs) :-
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init_matlab,
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extract_graph(AllVars, Graph),
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dgraph_top_sort(Graph, SortedVertices),
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number_graph(SortedVertices, NumberedVertices, 0, Size),
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init_bnet(SortedVertices, NumberedVertices, Size),
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create_bnt_graph(AllVars, _, SortedVertices, NumberedVertices, Size),
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set_inference,
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add_evidence(SortedVertices, Size, NumberedVertices),
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marginalize(QueryVars, Ps),
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clpbn_bind_vals(QueryVars, Ps, AllDiffs).
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create_bnt_graph(AllVars, Representatives) :-
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create_bnt_graph(AllVars, Representatives, _, _, _).
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create_bnt_graph(AllVars, Representatives, SortedVertices, NumberedVertices, Size) :-
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init_matlab,
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sort_nodes(AllVars, SortedVertices),
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number_graph(SortedVertices, NumberedVertices, 0, Size),
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bnt_model(ModelType),
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init_bnet(ModelType, SortedVertices, NumberedVertices, Size, Representatives).
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% make sure MATLAB works.
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init_matlab :-
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bnt(on), !.
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@ -97,6 +121,75 @@ start_matlab :-
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start_matlab :-
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start_matlab('matlab -nojvm -nosplash').
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sort_nodes(AllVars, SortedVertices) :-
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bnt_model(tied), !,
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extract_tied(AllVars, SortedVertices).
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sort_nodes(AllVars, SortedVertices) :-
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bnt_model(propositional), !,
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extract_graph(AllVars, Graph),
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dgraph_top_sort(Graph, SortedVertices).
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extract_tied(AllVars, SortedVars) :-
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extract_kvars(AllVars,KVars),
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keysort(KVars,SVars),
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split_tied_vars(SVars,TVars, Vertices),
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tied_graph(TVars,TGraph,Vertices),
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dgraph_top_sort(TGraph, Sort),
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distribute_tied_variables(Sort, TVars, 1, SortedVars).
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extract_kvars([],[]).
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extract_kvars([V|AllVars],[N-i(V,Parents)|KVars]) :-
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clpbn:get_atts(V, [dist(N,Parents)]),
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extract_kvars(AllVars,KVars).
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split_tied_vars([],[],[]).
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split_tied_vars([N-i(V,Par)|More],[N-g(Vs,Ns,Es)|TVars],[N|LNs]) :-
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get_pars(Par,N,V,NPs,[],Es0,Es),
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get_tied(More,N,Vs,[V],Ns,NPs,Es,Es0,SVars),
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split_tied_vars(SVars,TVars,LNs).
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get_pars([],_,_,NPs,NPs,Es,Es).
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get_pars([V|Par],N,V0,NPs,NPs0,Es,Es0) :-
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clpbn:get_atts(V, [dist(N,_)]), !,
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get_pars(Par,N,V0,NPs,NPs0,Es,[V-V0|Es0]).
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get_pars([V|Par],N,V0,NPs,NPs0,Es,Es0) :-
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clpbn:get_atts(V, [dist(M,_)]),
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ord_insert(NPs0,M,NPsI),
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get_pars(Par,N,V0,NPs,NPsI,Es,Es0).
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get_tied([N-i(V,Par)|More],N,Vs,Vs0,Ns,NPs,Es,Es0,SVars) :- !,
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get_pars(Par,N,V,NPsI,NPs,EsI,Es0),
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get_tied(More,N,Vs,[V|Vs0],Ns,NPsI,Es,EsI,SVars).
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get_tied(More,_,Vs,Vs,Ns,Ns,Es,Es,More).
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tied_graph(TVars,Graph,Vertices) :-
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dgraph_new(Graph0),
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dgraph_add_vertices(Vertices, Graph0, Graph1),
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get_tied_edges(TVars,Edges),
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dgraph_add_edges(Edges, Graph1, Graph).
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get_tied_edges([],[]).
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get_tied_edges([N-g(_,Vs,_)|TGraph],Edges) :-
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add_tied(Vs,N,Edges,Edges0),
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get_tied_edges(TGraph,Edges0).
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add_tied([],_,Edges,Edges).
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add_tied([N1|Vs],N,[N1-N|Edges],Edges0) :-
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add_tied(Vs,N,Edges,Edges0).
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distribute_tied_variables([], _, _, []).
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distribute_tied_variables([N|Sort], TVars, I0, SortedVars) :-
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member(N-g(Vs,_,_),TVars),
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distribute_tied(Vs,I0,In,SortedVars,SortedVars0),
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distribute_tied_variables(Sort, TVars, In, SortedVars0).
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distribute_tied([],I,I,Vs,Vs).
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distribute_tied([V|Vs],I0,In,[V|NVs],NVs0) :-
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I is I0+1,
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put_atts(V, [bnt_id(I0)]),
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% clpbn:get_atts(V,[key(K)]),
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distribute_tied(Vs,I,In,NVs,NVs0).
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extract_graph(AllVars, Graph) :-
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dgraph_new(Graph0),
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dgraph_add_vertices(AllVars, Graph0, Graph1),
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@ -117,14 +210,20 @@ number_graph([], [], I, I).
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number_graph([V|SortedGraph], [I|Is], I0, IF) :-
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I is I0+1,
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put_atts(V, [bnt_id(I)]),
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% clpbn:get_atts(V,[key(K)]),
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% write(I:K),nl,
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number_graph(SortedGraph, Is, I, IF).
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init_bnet(SortedGraph, NumberedGraph, Size) :-
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init_bnet(propositional, SortedGraph, NumberedGraph, Size, []) :-
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build_dag(SortedGraph, Size),
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matlab_sequence(1,Size,discrete_nodes),
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mksizes(SortedGraph, Size),
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init_discrete_nodes(SortedGraph, Size),
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bnet <-- mk_bnet(dag, node_sizes, \discrete, discrete_nodes),
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dump_cpts(SortedGraph, NumberedGraph).
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dump_cpts(SortedGraph, NumberedGraph),
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matlab_eval_string('bnet.CPD{3}',S),format('~s~n',[S]).
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init_bnet(tied, SortedGraph, NumberedGraph, Size, Representatives) :-
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build_dag(SortedGraph, Size),
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init_discrete_nodes(SortedGraph, Size),
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dump_tied_cpts(SortedGraph, NumberedGraph, Representatives).
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build_dag(SortedVertices, Size) :-
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get_numbered_edges(SortedVertices, Edges),
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@ -145,6 +244,10 @@ add_numbered_edges([P|Ps], N, [PN-N|Edges], Edges0) :-
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v2number(V,N) :-
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get_atts(V,[bnt_id(N)]).
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init_discrete_nodes(SortedGraph, Size) :-
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matlab_sequence(1,Size,discrete_nodes),
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mksizes(SortedGraph, Size).
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mkdag(N,Els) :-
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Tot is N*N,
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functor(Dag,dag,Tot),
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@ -177,13 +280,71 @@ get_szs([V|SortedVertices],[LD|Sizes]) :-
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dump_cpts([], []).
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dump_cpts([V|SortedGraph], [I|Is]) :-
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clpbn:get_atts(V, [dist(Id,_)]),
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clpbn:get_atts(V, [dist(Id,Parents)]),
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get_dist_params(Id,CPT),
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mkcpt(bnet,I,CPT),
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reorder_cpt(CPT,V,Parents,Tab),
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mkcpt(bnet,I,Tab),
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dump_cpts(SortedGraph, Is).
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mkcpt(BayesNet, V, Tab) :-
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(BayesNet.'CPD'({V})) <-- tabular_CPD(BayesNet,V,Tab).
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%
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% This is complicated, the BNT and we have different orders
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%
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reorder_cpt(CPT,_, [], CPT) :- !.
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reorder_cpt(CPT,V,Parents,Tab) :-
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% get BNT label
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get_sizes_and_ids(Parents,Ids),
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% sort to BNT
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keysort(Ids,NIds),
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% get vars in order
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extract_vars(NIds, [], NParents),
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% do the actual work
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reorder_CPT([V|Parents],CPT,[V|NParents],STab,_),
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STab=..[_|Tab].
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get_sizes_and_ids([],[]).
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get_sizes_and_ids([V|Parents],[Id-V|Ids]) :-
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get_atts(V, [bnt_id(Id)]),
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get_sizes_and_ids(Parents,Ids).
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extract_vars([], L, L).
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extract_vars([_-V|NIds], NParents, Vs) :-
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extract_vars(NIds, [V|NParents], Vs).
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mkcpt(BayesNet, I, Tab) :-
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(BayesNet.'CPD'({I})) <-- tabular_CPD(BayesNet,I,Tab).
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dump_tied_cpts(Graph, Is, Reps) :-
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create_class_vector(Graph, Is, Classes, Reps0),
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matlab_vector(Classes, eclass),
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keysort(Reps0,Reps1),
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representatives(Reps1,Reps),
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bnet <-- mk_bnet(dag, node_sizes, \discrete, discrete_nodes, \equiv_class, eclass),
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dump_tied_cpts(Reps).
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create_class_vector([], [], [],[]).
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create_class_vector([V|Graph], [I|Is], [Id|Classes], [Id-v(V,I,Parents)|Sets]) :-
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clpbn:get_atts(V, [dist(Id,Parents)]),
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create_class_vector(Graph, Is,Classes,Sets).
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representatives([],[]).
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representatives([Class-Rep|Reps1],[Class-Rep|Reps]) :-
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nonrepresentatives(Reps1, Class, Reps2),
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representatives(Reps2,Reps).
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nonrepresentatives([Class-_|Reps1], Class, Reps2) :- !,
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nonrepresentatives(Reps1, Class, Reps2).
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nonrepresentatives(Reps, _, Reps).
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dump_tied_cpts([]).
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dump_tied_cpts([Class-v(V,Id,Parents)|SortedGraph]) :-
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get_dist_params(Class,CPT),
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reorder_cpt(CPT,V,Parents,NCPT),
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mktiedcpt(bnet,Id,Class,NCPT),
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dump_tied_cpts(SortedGraph).
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mktiedcpt(BayesNet, V, Class, Tab) :-
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(BayesNet.'CPD'({Class})) <-- tabular_CPD(BayesNet,V,Tab).
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set_inference :-
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bnt_solver(Solver),
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@ -198,7 +359,7 @@ init_solver(likelihood_weighting) :-
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init_solver(enumerative) :-
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engine <-- enumerative_inf_engine(bnet).
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init_solver(gibbs) :-
|
||||
engine <-- gibbs_inf_engine(bnet).
|
||||
engine <-- gibbs_sampling_inf_engine(bnet).
|
||||
init_solver(global_joint) :-
|
||||
engine <-- global_joint_inf_engine(bnet).
|
||||
init_solver(pearl) :-
|
||||
|
||||
@ -5,6 +5,7 @@
|
||||
:- module(clpbn_dist,[
|
||||
dist/1,
|
||||
dist/3,
|
||||
dists/1,
|
||||
get_dist/4,
|
||||
get_dist_domain/2,
|
||||
get_dist_params/2,
|
||||
@ -66,6 +67,7 @@ new_id(Id) :-
|
||||
Id1 is Id+1,
|
||||
assert(id(Id1)).
|
||||
|
||||
dists(X) :- id(X1), X is X1-1.
|
||||
|
||||
dist(V, Id, Parents) :-
|
||||
var(V), !,
|
||||
|
||||
@ -46,10 +46,10 @@ build_sats_table(LSats, Key, Table) :-
|
||||
*/
|
||||
|
||||
build_rating_table(LSats, Key, Table) :-
|
||||
cpt_average(LSats, Key, [h,m,l], 0.95, Table).
|
||||
cpt_average(LSats, Key, [h,m,l], 1.00, Table).
|
||||
|
||||
build_grades_table(LGrades, Key, Table) :-
|
||||
cpt_average(LGrades, Key, [a,b,c,d], 0.95, Table).
|
||||
cpt_average(LGrades, Key, [a,b,c,d], 1.00, Table).
|
||||
|
||||
/*
|
||||
build_grades_table(LGrades, Key, Table) :-
|
||||
|
||||
121
CLPBN/learning/bnt_parms.yap
Normal file
121
CLPBN/learning/bnt_parms.yap
Normal file
@ -0,0 +1,121 @@
|
||||
%
|
||||
% Learn parameters using the BNT toolkit
|
||||
%
|
||||
|
||||
:- yap_flag(unknown,error).
|
||||
|
||||
:- style_check(all).
|
||||
|
||||
:- module(bnt_parameters, [learn_parameters/2]).
|
||||
|
||||
:- use_module(library('clpbn'), [
|
||||
clpbn_flag/3]).
|
||||
|
||||
:- use_module(library('clpbn/bnt'), [
|
||||
create_bnt_graph/2]).
|
||||
|
||||
:- use_module(library('clpbn/display'), [
|
||||
clpbn_bind_vals/3]).
|
||||
|
||||
:- use_module(library('clpbn/dists'), [
|
||||
get_dist_domain/2
|
||||
]).
|
||||
|
||||
:- use_module(library(matlab), [matlab_initialized_cells/4,
|
||||
matlab_call/2,
|
||||
matlab_get_variable/2
|
||||
]).
|
||||
|
||||
:- dynamic bnt_em_max_iter/1.
|
||||
bnt_em_max_iter(10).
|
||||
|
||||
|
||||
% syntactic sugar for matlab_call.
|
||||
:- op(800,yfx,<--).
|
||||
|
||||
G <-- Y :-
|
||||
matlab_call(Y,G).
|
||||
|
||||
|
||||
learn_parameters(Items, Tables) :-
|
||||
run_all(Items),
|
||||
clpbn_flag(solver, OldSolver, bnt),
|
||||
clpbn_flag(bnt_model, Old, tied),
|
||||
attributes:all_attvars(AVars),
|
||||
% sort and incorporte evidence
|
||||
clpbn_vars(AVars, AllVars),
|
||||
length(AllVars,NVars),
|
||||
create_bnt_graph(AllVars, Reps),
|
||||
mk_sample(AllVars,NVars,EvVars),
|
||||
bnt_learn_parameters(NVars,EvVars),
|
||||
get_parameters(Reps, Tables),
|
||||
clpbn_flag(solver, bnt, OldSolver),
|
||||
clpbn_flag(bnt_model, tied, Old).
|
||||
|
||||
run_all([]).
|
||||
run_all([G|Gs]) :-
|
||||
call(user:G),
|
||||
run_all(Gs).
|
||||
|
||||
clpbn_vars(Vs,BVars) :-
|
||||
get_clpbn_vars(Vs,CVs),
|
||||
keysort(CVs,KVs),
|
||||
merge_vars(KVs,BVars).
|
||||
|
||||
get_clpbn_vars([],[]).
|
||||
get_clpbn_vars([V|GVars],[K-V|CLPBNGVars]) :-
|
||||
clpbn:get_atts(V, [key(K)]), !,
|
||||
get_clpbn_vars(GVars,CLPBNGVars).
|
||||
get_clpbn_vars([_|GVars],CLPBNGVars) :-
|
||||
get_clpbn_vars(GVars,CLPBNGVars).
|
||||
|
||||
merge_vars([],[]).
|
||||
merge_vars([K-V|KVs],[V|BVars]) :-
|
||||
get_var_has_same_key(KVs,K,V,KVs0),
|
||||
merge_vars(KVs0,BVars).
|
||||
|
||||
get_var_has_same_key([K-V|KVs],K,V,KVs0) :- !,
|
||||
get_var_has_same_key(KVs,K,V,KVs0).
|
||||
get_var_has_same_key(KVs,_,_,KVs).
|
||||
|
||||
|
||||
mk_sample(AllVars,NVars, LL) :-
|
||||
add2sample(AllVars, LN),
|
||||
length(LN,LL),
|
||||
matlab_initialized_cells( NVars, 1, LN, sample).
|
||||
|
||||
add2sample([], []).
|
||||
add2sample([V|Vs],[val(VId,1,Val)|Vals]) :-
|
||||
clpbn:get_atts(V, [evidence(Ev),dist(Id,_)]), !,
|
||||
bnt:get_atts(V,[bnt_id(VId)]),
|
||||
get_dist_domain(Id, Domain),
|
||||
evidence_val(Ev,1,Domain,Val),
|
||||
add2sample(Vs, Vals).
|
||||
add2sample([_V|Vs],Vals) :-
|
||||
add2sample(Vs, Vals).
|
||||
|
||||
evidence_val(Ev,Val,[Ev|_],Val) :- !.
|
||||
evidence_val(Ev,I0,[_|Domain],Val) :-
|
||||
I1 is I0+1,
|
||||
evidence_val(Ev,I1,Domain,Val).
|
||||
|
||||
bnt_learn_parameters(_,_) :-
|
||||
engine <-- jtree_inf_engine(bnet),
|
||||
% engine <-- var_elim_inf_engine(bnet),
|
||||
% engine <-- gibbs_sampling_inf_engine(bnet),
|
||||
% engine <-- belprop_inf_engine(bnet),
|
||||
% engine <-- pearl_inf_engine(bnet),
|
||||
bnt_em_max_iter(MaxIters),
|
||||
[new_bnet, trace] <-- learn_params_em(engine, sample, MaxIters).
|
||||
|
||||
|
||||
get_parameters([],[]).
|
||||
get_parameters([Rep-v(_,_,_)|Reps],[CPT|CPTs]) :-
|
||||
get_new_table(Rep,CPT),
|
||||
get_parameters(Reps,CPTs).
|
||||
|
||||
get_new_table(Rep,CPT) :-
|
||||
s <-- struct(new_bnet.'CPD'({Rep})),
|
||||
matlab_get_variable( s.'CPT', CPT).
|
||||
|
||||
|
||||
@ -16,6 +16,8 @@
|
||||
|
||||
<h2>Yap-5.1.3:</h2>
|
||||
<ul>
|
||||
<li> FIXED: be careful to give delayarena exactly what it needs to expand.</li>
|
||||
<li> FIXED: improve BNT support with CLP(BN).</li>
|
||||
<li> FIXED: support checking for big_nums and readline. (obs from
|
||||
Rui Camacho)</li>
|
||||
<li> FIXED: make thread_create more compatible with SWI. (obs from
|
||||
|
||||
@ -27,6 +27,7 @@
|
||||
dgraph_transitive_closure/2,
|
||||
dgraph_symmetric_closure/2,
|
||||
dgraph_top_sort/2,
|
||||
dgraph_top_sort/3,
|
||||
dgraph_min_path/5,
|
||||
dgraph_max_path/5,
|
||||
dgraph_min_paths/3,
|
||||
@ -87,14 +88,14 @@ all_vertices_in_edges([V1-V2|Edges],[V1,V2|Vertices]) :-
|
||||
edges2graphl([], [], []).
|
||||
edges2graphl([V|Vertices], [VV-V1|SortedEdges], [V-[V1|Children]|GraphL]) :-
|
||||
V == VV, !,
|
||||
get_extra_children(SortedEdges,V,Children,RemEdges),
|
||||
get_extra_children(SortedEdges,VV,Children,RemEdges),
|
||||
edges2graphl(Vertices, RemEdges, GraphL).
|
||||
edges2graphl([V|Vertices], SortedEdges, [V-[]|GraphL]) :-
|
||||
edges2graphl(Vertices, SortedEdges, GraphL).
|
||||
|
||||
|
||||
dgraph_add_edges([],[]) --> [].
|
||||
dgraph_add_edges([V|Vs],[V-V1|Es]) --> !,
|
||||
dgraph_add_edges([V|Vs],[V0-V1|Es]) --> { V == V0 }, !,
|
||||
{ get_extra_children(Es,V,Children,REs) },
|
||||
dgraph_update_vertex(V,[V1|Children]),
|
||||
dgraph_add_edges(Vs,REs).
|
||||
@ -103,7 +104,7 @@ dgraph_add_edges([V|Vs],Es) --> !,
|
||||
dgraph_add_edges(Vs,Es).
|
||||
|
||||
get_extra_children([V-C|Es],VV,[C|Children],REs) :- V == VV, !,
|
||||
get_extra_children(Es,V,Children,REs).
|
||||
get_extra_children(Es,VV,Children,REs).
|
||||
get_extra_children(Es,_,[],Es).
|
||||
|
||||
dgraph_update_vertex(V,Children, Vs0, Vs) :-
|
||||
@ -292,6 +293,9 @@ invert_edges([V1-V2|Edges], [V2-V1|InvertedEdges]) :-
|
||||
invert_edges(Edges, InvertedEdges).
|
||||
|
||||
dgraph_top_sort(G, Q) :-
|
||||
dgraph_top_sort(G, Q, []).
|
||||
|
||||
dgraph_top_sort(G, Q, RQ0) :-
|
||||
% O(E)
|
||||
rb_visit(G, Vs),
|
||||
% O(E)
|
||||
@ -303,7 +307,7 @@ dgraph_top_sort(G,Q) :-
|
||||
% O(E)
|
||||
dgraph_vertices(G, AllVs),
|
||||
start_queue(AllVs, InvertedEdges, Q, RQ),
|
||||
continue_queue(Q, LinkedG, RQ).
|
||||
continue_queue(Q, LinkedG, RQ, RQ0).
|
||||
|
||||
invert_and_link([], [], [], [], []).
|
||||
invert_and_link([V-Vs|Edges], [V-NVs|ExtraEdges], UnsortedInvertedEdges, [V|AllVs],[_|Q]) :-
|
||||
@ -329,12 +333,12 @@ link_edges([V-e(A,B,S,E)|InvertedEdges], VV, A, S, E, RemEdges) :- V == VV, !,
|
||||
link_edges(InvertedEdges, VV, B, S, E, RemEdges).
|
||||
link_edges(RemEdges, _, A, _, A, RemEdges).
|
||||
|
||||
continue_queue([], _, []).
|
||||
continue_queue([V|Q], LinkedG, RQ) :-
|
||||
continue_queue([], _, RQ0, RQ0).
|
||||
continue_queue([V|Q], LinkedG, RQ, RQ0) :-
|
||||
rb_lookup(V, Links, LinkedG),
|
||||
close_links(Links, RQ, RQ0),
|
||||
close_links(Links, RQ, RQI),
|
||||
% not clear whether I should deleted V from LinkedG
|
||||
continue_queue(Q, LinkedG, RQ0).
|
||||
continue_queue(Q, LinkedG, RQI, RQ0).
|
||||
|
||||
close_links([], RQ, RQ).
|
||||
close_links([l(V,A,A,S,E)|Links], RQ, RQ0) :-
|
||||
|
||||
@ -12,6 +12,7 @@
|
||||
matlab_zeros/3,
|
||||
matlab_zeros/4,
|
||||
matlab_matrix/4,
|
||||
matlab_vector/2,
|
||||
matlab_vector/3,
|
||||
matlab_set/4,
|
||||
matlab_get_variable/2,
|
||||
|
||||
Reference in New Issue
Block a user