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More whitespace cleanups

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This commit is contained in:
Tiago Gomes
2012-12-20 23:19:10 +00:00
parent 421d6f72ee
commit 01d8f8e178
61 changed files with 209 additions and 210 deletions
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8
packages/CLPBN/clpbn/aggregates.yap
View File

@@ -96,11 +96,11 @@ find_ev(_Evs, Key, RemKeys, [Key|RemKeys], Ev, Ev).
% +final CPT
% - New Parents
% + - list of new keys
%
%
avg_table(Vars, OVars, Domain, Key, TotEvidence, Softness, Vars, Vs, Vs, Id) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize),
TabSize =< 256,
TabSize =< 256,
/* case gmp is not there !! */
TabSize > 0, !,
average_cpt(Vars, OVars, Domain, TotEvidence, Softness, CPT),
@@ -170,7 +170,7 @@ cpt_min([_|Vars], Key, Els0, CPT, Vs, NewVs) :-
build_avg_table(Vars, OVars, Domain, _, TotEvidence, Softness, CPT, Vars, Vs, Vs) :-
length(Domain, SDomain),
int_power(Vars, SDomain, 1, TabSize),
TabSize =< 256,
TabSize =< 256,
/* case gmp is not there !! */
TabSize > 0, !,
average_cpt(Vars, OVars, Domain, TotEvidence, Softness, CPT).
@@ -304,7 +304,7 @@ get_ds_lengths([],[]).
get_ds_lengths([V|Vs],[Sz|Lengs]) :-
get_vdist_size(V, Sz),
get_ds_lengths(Vs,Lengs).
fill_in_average(Lengs, N, Base, MCPT) :-
generate(Lengs, Case),
average(Case, N, Base, Val),

38
packages/CLPBN/clpbn/bdd.yap
View File

@@ -9,12 +9,12 @@ V = v(Va, Vb, Vc)
The generic formula is
V <- X, Y
V <- X, Y
Va <- P*X1*Y1 + Q*X2*Y2 + ...
**************************************************/
:- module(clpbn_bdd,
@@ -80,8 +80,8 @@ bdds(bdd).
%
% QVars: all query variables?
%
%
%
%
init_bdd_ground_solver(QueryKeys, AllKeys, Factors, Evidence, bdd(QueryKeys, AllKeys, Factors, Evidence)).
%
@@ -161,7 +161,7 @@ sort_keys(AllFs, AllVars, Leaves) :-
dgraph_top_sort(Graph, AllVars).
add_node(f([K|Parents],_,_,_), Graph0, Graph) :-
dgraph_add_vertex(Graph0, K, Graph1),
dgraph_add_vertex(Graph0, K, Graph1),
foldl(add_edge(K), Parents, Graph1, Graph).
add_edge(K, K0, Graph0, Graph) :-
@@ -176,7 +176,7 @@ sort_vars(AllVars0, AllVars, Leaves) :-
build_graph([], Graph, Graph).
build_graph([V|AllVars0], Graph0, Graph) :-
clpbn:get_atts(V, [dist(_DistId, Parents)]), !,
dgraph_add_vertex(Graph0, V, Graph1),
dgraph_add_vertex(Graph0, V, Graph1),
add_parents(Parents, V, Graph1, GraphI),
build_graph(AllVars0, GraphI, Graph).
build_graph(_V.AllVars0, Graph0, Graph) :-
@@ -184,7 +184,7 @@ build_graph(_V.AllVars0, Graph0, Graph) :-
add_parents([], _V, Graph, Graph).
add_parents([V0|Parents], V, Graph0, GraphF) :-
dgraph_add_edge(Graph0, V0, V, GraphI),
dgraph_add_edge(Graph0, V0, V, GraphI),
add_parents(Parents, V, GraphI, GraphF).
get_keys_info([], _, _, _, Vs, Vs, Ps, Ps, _, _) --> [].
@@ -378,7 +378,7 @@ avg_tree([Vals|PVars], P, Max, Im, IM, Size, O, H0, HF) :-
MaxI is Max-(Size-1),
avg_exp(Vals, PVars, 0, P, MaxI, Size, Im, IM, HI, HF, Exp),
simplify_exp(Exp, Simp).
avg_exp([], _, _, _P, _Max, _Size, _Im, _IM, H, H, 0).
avg_exp([Val|Vals], PVars, I0, P0, Max, Size, Im, IM, HI, HF, O) :-
(Vals = [] -> O=O1 ; O = Val*O1+not(Val)*O2 ),
@@ -434,7 +434,7 @@ bup_avg(V, Size, Domain, Parents0, Vs, Vs2, Lvs, Outs, DIST) :-
bin_sums(Vs, Sums, F) :-
vs_to_sums(Vs, Sums0),
bin_sums(Sums0, Sums, F, []).
vs_to_sums([], []).
vs_to_sums([V|Vs], [Sum|Sums0]) :-
Sum =.. [sum|V],
@@ -536,7 +536,7 @@ sum_all([_V|Vs], Pos, I, Max0, Sums, List) :-
gen_arg(J, Sums, Max, S0) :-
gen_arg(0, Max, J, Sums, S0).
gen_arg(Max, Max, J, Sums, S0) :- !,
I is Max+1,
arg(I, Sums, A),
@@ -647,19 +647,19 @@ copy(N, [], [], Ms, Parms0, Parms, ParmVars) :-!,
copy(N, Ms, NewMs, NewMs, Parms0, Parms, ParmVars).
copy(N, D.Ds, ND.NDs, New, El.Parms0, NEl.Parms, V.ParmVars) :-
N1 is N-1,
(El == 0.0 ->
(El == 0.0 ->
NEl = 0,
V = NEl,
ND = D
;El == 1.0 ->
;El == 1.0 ->
NEl = 1,
V = NEl,
ND = 0.0
;El == 0 ->
;El == 0 ->
NEl = 0,
V = NEl,
ND = D
;El =:= 1 ->
;El =:= 1 ->
NEl = 1,
V = NEl,
ND = 0.0,
@@ -818,7 +818,7 @@ get_key_evidence(V, Evs, _, Tree, Ev, F0, F, Leaves, Finals) :-
%% deterministic(V, DistId),
%% !,
%% one_list(Ev),
%% eval_outs(F0).
%% eval_outs(F0).
%% no evidence !!!
get_key_evidence(V, _, _, Tree, _Values, F0, F1, Leaves, Finals) :-
insert_output(Leaves, V, Finals, Tree, Outs, SendOut),
@@ -836,14 +836,14 @@ get_evidence(V, _Tree, Ev, F0, [], _Leaves, _Finals) :-
( Name = 'AVG' ; Name = 'MAX' ; Name = 'MIN' ),
!,
one_list(Ev),
eval_outs(F0).
eval_outs(F0).
%% no evidence !!!
get_evidence(V, Tree, _Values, F0, F1, Leaves, Finals) :-
insert_output(Leaves, V, Finals, Tree, Outs, SendOut),
get_outs(F0, F1, SendOut, Outs).
zero_pos(_, _Pos, []).
zero_pos(Pos, Pos, [1|Values]) :- !,
zero_pos(Pos, Pos, [1|Values]) :- !,
I is Pos+1,
zero_pos(I, Pos, Values).
zero_pos(I0, Pos, [0|Values]) :-
@@ -855,7 +855,7 @@ one_list(1.Ev) :-
one_list(Ev).
%
% insert a node with the disj of all alternatives, this is only done if node ends up to be in the output
% insert a node with the disj of all alternatives, this is only done if node ends up to be in the output
%
insert_output([], _V, [], _Out, _Outs, []).
insert_output(V._Leaves, V0, [Top|_], Top, Outs, [Top = Outs]) :- V == V0, !.
@@ -1057,7 +1057,7 @@ generate_exclusions([V0|SeenVs], V) -->
build_cnf(CNF, IVs, Indics, AllParms, AllParmValues, Val) :-
%(numbervars(CNF,1,_), writeln(cnf_to_ddnnf(CNF, Vars, IVs, [], F)), fail ; true ),
cnf_to_ddnnf(CNF, AllParms, F),
cnf_to_ddnnf(CNF, AllParms, F),
AllParms = AllParmValues,
IVs = Indics,
term_variables(CNF, Extra),

8
packages/CLPBN/clpbn/bnt.yap
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@@ -101,7 +101,7 @@ do_bnt(QueryVars, AllVars, AllDiffs) :-
add_evidence(SortedVertices, Size, NumberedVertices),
marginalize(QueryVars, SortedVertices, NumberedVertices, Ps),
clpbn_bind_vals(QueryVars, Ps, AllDiffs).
create_bnt_graph(AllVars, Representatives) :-
create_bnt_graph(AllVars, Representatives, _, _, _).
@@ -320,7 +320,7 @@ get_sizes_and_ids([V|Parents],[Id-V|Ids]) :-
extract_vars([], L, L).
extract_vars([_-V|NIds], NParents, Vs) :-
extract_vars(NIds, [V|NParents], Vs).
mkcpt(BayesNet, I, Tab) :-
(BayesNet.'CPD'({I})) <-- tabular_CPD(BayesNet,I,Tab).
@@ -336,7 +336,7 @@ create_class_vector([], [], [],[]).
create_class_vector([V|Graph], [I|Is], [Id|Classes], [Id-v(V,I,Parents)|Sets]) :-
clpbn:get_atts(V, [dist(Id,Parents)]),
create_class_vector(Graph, Is,Classes,Sets).
representatives([],[]).
representatives([Class-Rep|Reps1],[Class-Rep|Reps]) :-
nonrepresentatives(Reps1, Class, Reps2),
@@ -390,7 +390,7 @@ mk_evidence([V|L], [I|Is], [ar(1,I,EvVal1)|LN]) :-
mk_evidence(L, Is, LN).
mk_evidence([_|L], [_|Is], LN) :-
mk_evidence(L, Is, LN).
evidence_val(Ev,Val,[Ev|_],Val) :- !.
evidence_val(Ev,I0,[_|Domain],Val) :-
I1 is I0+1,

2
packages/CLPBN/clpbn/discrete_utils.yap
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@@ -24,7 +24,7 @@ propagate_evidence(V, Evs) :-
clpbn:get_atts(V, [evidence(Ev),dist(Id,_)]), !,
get_dist_domain(Id, Out),
generate_szs_with_evidence(Out,Ev,0,Evs,Found),
(var(Found) ->
(var(Found) ->
clpbn:get_atts(V, [key(K)]),
throw(clpbn(evidence_does_not_match,K,Ev,[Out]))
;

2
packages/CLPBN/clpbn/display.yap
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@@ -78,7 +78,7 @@ clpbn_bind_vals([Vs|MoreVs],[Ps|MorePs],AllDiffs) :-
clpbn_bind_vals2([],_,_) :- !.
% simple case, we want a distribution on a single variable.
clpbn_bind_vals2([V],Ps,AllDiffs) :-
clpbn_bind_vals2([V],Ps,AllDiffs) :-
use_parfactors(on), !,
clpbn:get_atts(V, [key(K)]),
pfl:skolem(K,Vals),

2
packages/CLPBN/clpbn/dists.yap
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@@ -90,7 +90,7 @@ where Id is the id,
dna for [a,c,g,t]
rna for [a,c,g,u]
reals
********************************************/

6
packages/CLPBN/clpbn/evidence.yap
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@@ -31,7 +31,7 @@
:- dynamic node/3, edge/2, evidence/2.
%
% new evidence storage algorithm. The idea is that instead of
% new evidence storage algorithm. The idea is that instead of
% redoing all the evidence every time we query the network, we shall
% keep a precompiled version around.
%
@@ -51,9 +51,9 @@ compute_evidence(_,PreviousSolver) :-
set_clpbn_flag(solver, PreviousSolver).
get_clpbn_vars(G, Vars) :-
% attributes:all_attvars(Vars0),
% attributes:all_attvars(Vars0),
once(G),
attributes:all_attvars(Vars).
attributes:all_attvars(Vars).
evidence_error(Ball,PreviousSolver) :-
set_clpbn_flag(solver,PreviousSolver),

14
packages/CLPBN/clpbn/gibbs.yap
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@@ -137,7 +137,7 @@ graph_representation([V|Vs], Graph, I0, Keys, [I-IParents|TGraph]) :-
graph_representation(Vs, Graph, I, Keys, TGraph).
write_pars([]).
write_pars([V|Parents]) :-
write_pars([V|Parents]) :-
clpbn:get_atts(V, [key(K),dist(I,_)]),write(K:I),nl,
write_pars(Parents).
@@ -149,7 +149,7 @@ get_sizes([V|Parents], [Sz|Szs]) :-
parent_indices([], _, []).
parent_indices([V|Parents], Keys, [I|IParents]) :-
rb_lookup(V, I, Keys),
rb_lookup(V, I, Keys),
parent_indices(Parents, Keys, IParents).
@@ -174,7 +174,7 @@ propagate2parents([V|NewParents], Table, Variables, Graph, Keys) :-
propagate2parents(NewParents,Table, Variables, Graph, Keys).
add2graph(V, Vals, Table, IParents, Graph, Keys) :-
rb_lookup(V, Index, Keys),
rb_lookup(V, Index, Keys),
(var(Vals) -> true ; length(Vals,Sz)),
arg(Index, Graph, var(V,Index,_,Vals,Sz,VarSlot,_,_,_)),
member(tabular(Table,Index,IParents), VarSlot), !.
@@ -239,7 +239,7 @@ mult_list([Sz|Sizes],Mult0,Mult) :-
MultI is Sz*Mult0,
mult_list(Sizes,MultI,Mult).
% compile node as set of facts, faster execution
% compile node as set of facts, faster execution
compile_var(TotSize,I,_Vals,Sz,CPTs,Parents,_Sizes,Graph) :-
TotSize < 1024*64, TotSize > 0, !,
multiply_all(I,Parents,CPTs,Sz,Graph).
@@ -367,8 +367,8 @@ generate_est_mults([], [], _, [], 1).
generate_est_mults([V|Vs], [I|Is], Graph, [M0|Mults], M) :-
arg(V,Graph,var(_,I,_,_,Sz,_,_,_,_)),
generate_est_mults(Vs, Is, Graph, Mults, M0),
M is M0*Sz.
M is M0*Sz.
gen_e0(0,[]) :- !.
gen_e0(Sz,[0|E0L]) :-
Sz1 is Sz-1,
@@ -531,7 +531,7 @@ add_up_mes(Counts,[me(_,_,Cs)|Chains], Add) :-
sum_lists(Counts, Cs, NCounts),
add_up_mes(NCounts, Chains, Add).
sum_lists([],[],[]).
sum_lists([],[],[]).
sum_lists([Count|Counts], [C|Cs], [NC|NCounts]) :-
NC is Count+C,
sum_lists(Counts, Cs, NCounts).

2
packages/CLPBN/clpbn/graphviz.yap
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@@ -50,7 +50,7 @@ output_parents1(Stream,[V|L]) :-
put_code(Stream, 0' ), %'
output_parents1(Stream,L).
output_v(V,Stream) :-
output_v(V,Stream) :-
clpbn:get_atts(V,[key(Key)]),
output_key(Stream,Key).

2
packages/CLPBN/clpbn/horus.yap
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@@ -1,7 +1,7 @@
/*******************************************************
Horus Interface
********************************************************/
:- module(clpbn_horus,

2
packages/CLPBN/clpbn/horus_ground.yap
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@@ -4,7 +4,7 @@
- Variable Elimination
- Belief Propagation
- Counting Belief Propagation
********************************************************/
:- module(clpbn_horus_ground,

4
packages/CLPBN/clpbn/jt.yap
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@@ -98,7 +98,7 @@ jt(LLVs,Vs0,AllDiffs) :-
init_jt_solver(LLVs, Vs0, _, State) :-
check_for_agg_vars(Vs0, Vs1),
check_for_agg_vars(Vs0, Vs1),
init_influences(Vs1, G, RG),
maplist(init_jt_solver_for_question(G, RG), LLVs, State).
@@ -163,7 +163,7 @@ initial_graph(_,Parents, CPTs) :-
% from the very beginning.
dgraph_transpose(V1, V2),
dgraph_to_ugraph(V2, Parents).
problem_graph([], []).
problem_graph([V|BNet], GraphF) :-

8
packages/CLPBN/clpbn/matrix_cpt_utils.yap
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@@ -87,7 +87,7 @@ evidence(V, Pos) :-
clpbn:get_atts(V, [evidence(Pos)]).
vnth([V1|Deps], N, V, N, Deps) :-
V == V1, !.
V == V1, !.
vnth([V1|Deps], N0, V, N, [V1|NDeps]) :-
N1 is N0+1,
vnth(Deps, N1, V, N, NDeps).
@@ -207,9 +207,9 @@ generate_map([V|DimsNew], [V0|Dims0], [0|Map]) :- V == V0, !,
generate_map(DimsNew, Dims0, Map).
generate_map([V|DimsNew], Dims0, [Sz|Map]) :-
clpbn:get_atts(V, [dist(Id,_)]),
clpbn_dist:get_dist_domain_size(Id, Sz),
clpbn_dist:get_dist_domain_size(Id, Sz),
generate_map(DimsNew, Dims0, Map).
unit_CPT(V,CPT) :-
clpbn:get_atts(V, [dist(Id,_)]),
clpbn_dist:get_dist_domain_size(Id, Sz),
@@ -287,7 +287,7 @@ uniform_CPT(Dims, M) :-
normalise_possibly_deterministic_CPT(M1, M).
normalise_CPT_on_lines(MAT0, MAT2, L1) :-
matrix_agg_cols(MAT0, +, MAT1),
matrix_agg_cols(MAT0, +, MAT1),
matrix_sum(MAT1, SUM),
matrix_op_to_all(MAT1, /, SUM, MAT2),
matrix:matrix_to_list(MAT2,L1).

4
packages/CLPBN/clpbn/numbers.yap
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@@ -1,7 +1,7 @@
:- module(clpbn_numbers,
[keys_to_numbers/7,
keys_to_numbers/9,
[keys_to_numbers/7,
keys_to_numbers/9,
lists_of_keys_to_ids/6
]).

2
packages/CLPBN/clpbn/pgrammar.yap
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@@ -176,7 +176,7 @@ get_internal(S, InternalS, Arg) :-
extract_probability(p(Id,Goals), P) :-
id(Id,_,P0,_),
LogP0 is log(P0),
LogP0 is log(P0),
extract_logprobability(Goals, LogP0, LogP),
P is exp(LogP).

10
packages/CLPBN/clpbn/table.yap
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@@ -37,11 +37,11 @@
clpbn_tabled_clause_ref(:.?,?),
clpbn_tabled_retract(:),
clpbn_tabled_abolish(:),
clpbn_tabled_asserta(:),
clpbn_tabled_assertz(:),
clpbn_tabled_asserta(:,-),
clpbn_tabled_assertz(:,-),
clpbn_tabled_number_of_clauses(:,-),
clpbn_tabled_asserta(:),
clpbn_tabled_assertz(:),
clpbn_tabled_asserta(:,-),
clpbn_tabled_assertz(:,-),
clpbn_tabled_number_of_clauses(:,-),
clpbn_is_tabled(:).
:- use_module(library(terms),

4
packages/CLPBN/clpbn/utils.yap
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@@ -66,7 +66,7 @@ merge_same_key([K1-V1,K2-V2|Vs], SortedAVars, Ks, UnifiableVars) :-
attributes:fast_unify_attributed(V1,V2),
merge_same_key([K1-V1|Vs], SortedAVars, Ks, UnifiableVars).
merge_same_key([K1-V1,K2-V2|Vs], [V1|SortedAVars], Ks, [K1|UnifiableVars]) :-
(in_keys(K1, Ks) ; \+ \+ K1 == K2), !,
(in_keys(K1, Ks) ; \+ \+ K1 == K2), !,
add_to_keys(K1, Ks, NKs),
merge_same_key([K2-V2|Vs], SortedAVars, NKs, UnifiableVars).
merge_same_key([K-V|Vs], [V|SortedAVars], Ks, UnifiableVars) :-
@@ -74,7 +74,7 @@ merge_same_key([K-V|Vs], [V|SortedAVars], Ks, UnifiableVars) :-
merge_same_key(Vs, SortedAVars, NKs, UnifiableVars).
in_keys(K1,[K|_]) :- \+ \+ K1 = K, !.
in_keys(K1,[_|Ks]) :-
in_keys(K1,[_|Ks]) :-
in_keys(K1,Ks).
add_to_keys(K1, Ks, Ks) :- ground(K1), !.

22
packages/CLPBN/clpbn/ve.yap
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@@ -11,7 +11,7 @@
all tables they connect to;
multiply their size
order by size
*********************************/
:- module(clpbn_ve,
@@ -134,7 +134,7 @@ evtotree(K=V,Ev0,Ev) :-
factor_to_graph( f(Nodes, Sizes, _Pars0, Id), Factors0, Factors, Edges0, Edges, I0, I) :-
I is I0+1,
pfl:get_pfl_parameters(Id, Pars0),
init_CPT(Pars0, Sizes, CPT0),
init_CPT(Pars0, Sizes, CPT0),
reorder_CPT(Nodes, CPT0, FIPs, CPT, _),
F = f(I0, FIPs, CPT),
rb_insert(Factors0, I0, F, Factors),
@@ -196,7 +196,7 @@ id_to_factor(VMap, V-I, IF0, IF, Fs0, Fs, Evs0, Evs) :-
get_dist_params(D, Pars0),
get_dist_domain_size(D, DS),
maplist(parent_to_id(VMap), Ps, Sizes, IPs),
init_CPT(Pars0, [DS|Sizes], CPT0),
init_CPT(Pars0, [DS|Sizes], CPT0),
reorder_CPT([I|IPs], CPT0, FIPs, CPT, _),
rb_insert(Fs0, IF0, f(IF0, FIPs, CPT), Fs),
IF is IF0+1.
@@ -261,7 +261,7 @@ solve([_|LQVs], FIds, Bigraph, Ev, LPs) :-
do_solve(IQVs, IVs, bigraph(OldVs, IF, _Fs), Ev, Ps) :-
% get only what is relevant to query,
project_to_query_related(IVs, OldVs, SVs, Fs1),
project_to_query_related(IVs, OldVs, SVs, Fs1),
% and also prune using evidence
rb_visit(Ev, EvL),
foldl2(clean_v_ev, EvL, Fs1, Fs2, SVs, EVs),
@@ -300,9 +300,9 @@ run_ve_solver(_, LLPs, state(LQVs, LVs, _VMap, Bigraph, Ev)) :-
%
solve_ve([IQVs|_], [IVs|_], bigraph(OldVs, IF, _Fs), Ev, Ps) :-
% get only what is relevant to query,
project_to_query_related(IVs, OldVs, SVs, Fs1),
project_to_query_related(IVs, OldVs, SVs, Fs1),
% and also prune using evidence
foldl2(clean_v_ev, Ev, Fs1, Fs2, SVs, EVs),
foldl2(clean_v_ev, Ev, Fs1, Fs2, SVs, EVs),
% eliminate
eliminate(IQVs, digraph(EVs, IF, Fs2), Dist),
% writeln(m:Dist),matrix:matrix_to_list(Dist,LD),writeln(LD),
@@ -319,7 +319,7 @@ solve_ve([_|MoreLVs], [_|MoreLVis], Digraph, Ev, Ps) :-
project_to_query_related(IVs0, OldVs, NVs, NFs) :-
sort(IVs0, IVs),
rb_new(Vs0),
foldl(cp_to_vs, IVs, Vs0, AuxVs),
foldl(cp_to_vs, IVs, Vs0, AuxVs),
rb_new(NFs0),
foldl(simplify_graph_node(OldVs, AuxVs), IVs, VFs, NFs0, NFs),
list_to_rbtree(VFs, NVs).
@@ -343,14 +343,14 @@ simplify_graph_node(OldVs, NVs, V, V-RemFs, NFs0, NFs) :-
%
% Two cases: first time factor comes up: all its vars must be in subgraph
% second case: second time it comes up, it must be already in graph
%
% args: +Factor F, +current V (int), +rbtree with all Vs,
%
% args: +Factor F, +current V (int), +rbtree with all Vs,
% -Factors in new Graph, +factors in current graph, -rbtree of factors
%
%
check_factor(V, NVs, F, NFs0, NFs, RemFs, NewRemFs) :-
F = f(IF, [V|More], _), !,
(
(
checklist(check_v(NVs), More)
->
rb_insert(NFs0, IF, F, NFs),
@@ -361,7 +361,7 @@ check_factor(V, NVs, F, NFs0, NFs, RemFs, NewRemFs) :-
).
check_factor(_V, _NVs, F, NFs, NFs, RemFs, NewRemFs) :-
F = f(Id, _, _),
(
(
rb_lookup(Id, F, NFs)
->
NewRemFs = [F|RemFs]

2
packages/CLPBN/clpbn/vmap.yap
View File

@@ -12,7 +12,7 @@
:- use_module(library(maplist)).
%
% vmap: map V->I
% vmap: map V->I
% contiguous Vs to contiguous integers
%
init_vmap(vmap(0,Empty)) :-