I want to implement SKI combinators in Prolog.
There are just 3 simple rules:
(I x) = x
((K x) y) = x
(S x y z) = (x z (y z))
I came up with the following code by using epilog:
term(s)
term(k)
term(i)
term(app(X,Y)) :- term(X) & term(Y)
proc(s, s)
proc(k, k)
proc(i, i)
proc(app(i,Y), Y1) :- proc(Y,Y1)
proc(app(app(k,Y),Z), Y1) :- proc(Y,Y1)
proc(app(app(app(s,P1),P2),P3), Y1) :- proc(app( app(P1,P3), app(P2, P3) ), Y1)
proc(app(X, Y), app(X1, Y1)) :- proc(X, X1) & proc(Y, Y1)
proc(X,X)
It works for some cases but has 2 issues:
It takes too much time to execute simple queries:
term(X) & proc(app(app(k, X), s), app(s,k))
100004 unification(s)
It requires multiple queries to process some terms. For example:
((((S(K(SI)))K)S)K) -> (KS)
requires 2 runs:
proc(app(app(app(app(s,app(k,app(s,i))),k),s),k), X) ==>
proc(app(app(app(app(s,app(k,app(s,i))),k),s),k), app(app(app(s,i),app(k,s)),k))
proc(app(app(app(s,i),app(k,s)),k), X) ==>
proc(app(app(app(s,i),app(k,s)),k), app(k,s))
Can you please suggest how to optimize my implementation and make it work on complex combinators?
edit: The goal is to reduce combinators. I want to enumerate them (without duplicates) where the last one is in normal form (if it exists of course).
It can be implemented with iterative deepening like this:
term(s) --> "S".
term(k) --> "K".
term(i) --> "I".
term(a(E0,E)) --> "(", term(E0), term(E), ")".
reduce_(s, s).
reduce_(k, k).
reduce_(i, i).
% Level 1.
reduce_(a(s,A0), a(s,A)) :-
reduce_(A0, A).
reduce_(a(k,A0), a(k,A)) :-
reduce_(A0, A).
reduce_(a(i,A), A).
% level 2.
reduce_(a(a(s,E0),A0), a(a(s,E),A)) :-
reduce_(E0, E),
if_(E0 = E, reduce_(A0, A), A0 = A).
% reduce_(A0, A). % Without `reif`.
reduce_(a(a(k,E),_), E).
reduce_(a(a(i,E),A), a(E,A)).
% level 3.
reduce_(a(a(a(s,E),F),A), a(a(E,A),a(F,A))).
reduce_(a(a(a(k,E),_),A), a(E,A)).
reduce_(a(a(a(i,E),F),A), a(a(E,F),A)).
% Recursion.
reduce_(a(a(a(a(E0,E1),E2),E3),A0), a(E,A)) :-
reduce_(a(a(a(E0,E1),E2),E3), E),
if_(a(a(a(E0,E1),E2),E3) = E, reduce_(A0, A), A0 = A).
% reduce_(A0, A). % Without `reif`.
step(E, E0, E) :-
reduce_(E0, E).
reduce_(N, E0, E, [E0|Es]) :-
length(Es, N),
foldl(step, Es, E0, E).
reduce(N, E0, E) :-
reduce_(N, E0, E, _),
reduce_(E, E), % Fix point.
!. % Commit.
The term can be inputted and outputted as a list of characters with term//1. The grammar rule term//1 can also generate unique terms.
?- length(Cs, M), M mod 3 =:= 1, phrase(term(E0), Cs).
The goal is to be as lazy as possible when reducing a term thus dif/2 and the library reif is used in reduce_/2. The predicate reduce_/2 does a single reduction. If any of the argument of reduce_/2 is ground then termination is guarantee (checked with cTI).
To reduce a term, reduce_/4 can be used. The first argument specifies the depth, the last argument holds the list of terms. The predicate reduce_/4 is pure and does not terminate.
?- Cs = "(((SK)K)S)", phrase(term(E0), Cs), reduce_(N, E0, E, Es).
The predicate reduce/3 succeeds if there is a normal form. It is recommended to provide a maximum depth (e.g. Cs = "(((SI)I)((SI)(SI)))"):
?- length(Cs, M), M mod 3 =:= 1, phrase(term(E0), Cs), \+ reduce(16, E0, _).
Test with ((((S(K(SI)))K)S)K):
?- Cs0 = "((((S(K(SI)))K)S)K)", phrase(term(E0), Cs0),
reduce(N, E0, E), phrase(term(E), Cs).
Cs0="((((S(K(SI)))K)S)K)", E0=a(a(a(a(s,a(k,a(s,i))),k),s),k), N=5, E=a(k,s), Cs="(KS)"
Translating your code trivially to Prolog, using the built-in left-associating infix operator - for app, to improve readability,
term(s).
term(k).
term(i).
term( X-Y ) :- term(X) , term(Y).
/* proc(s, s). %%% not really needed.
proc(k, k).
proc(i, i). */
proc( i-Y, Y1) :- proc( Y,Y1).
proc( k-Y-Z, Y1) :- proc( Y,Y1).
proc( s-X-Y-Z, Y1) :- proc( X-Z-(Y-Z), Y1).
proc( X-Y, X1-Y1 ) :- proc( X, X1) , proc( Y, Y1).
proc( X, X).
executing in SWI Prolog,
26 ?- time( (term(X), proc( k-X-s, s-k)) ).
% 20 inferences, 0.000 CPU in 0.001 seconds (0% CPU, Infinite Lips)
X = s-k ;
% 1 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = s-k ;
Action (h for help) ? abort
% 952,783 inferences, 88.359 CPU in 90.112 seconds (98% CPU, 10783 Lips)
% Execution Aborted
27 ?-
the first result is produced in 20 inferences.
Furthermore, indeed
32 ?- time( proc( s-(k-(s-i))-k-s-k, X) ).
% 10 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = s-i- (k-s)-k ;
% 2 inferences, 0.000 CPU in 0.001 seconds (0% CPU, Infinite Lips)
X = s-i- (k-s)-k ;
% 5 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = s-i- (k-s)-k ;
% 2 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = s-i- (k-s)-k ;
% 11 inferences, 0.000 CPU in 0.001 seconds (0% CPU, Infinite Lips)
X = k- (s-i)-s- (k-s)-k ;
% 2 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = k- (s-i)-s- (k-s)-k . % stopped manually
and then
33 ?- time( proc( s-i- (k-s)-k, X) ).
% 5 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = k-s ;
% 5 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = k- (k-s-k) ;
% 2 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = k- (k-s-k) ;
% 1 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = k- (k-s-k) ;
% 5 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = i-k-s ;
% 5 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = i-k- (k-s-k) ;
% 2 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = i-k- (k-s-k) ;
% 1 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = i-k- (k-s-k) ;
% 3 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = i-k-s ;
% 5 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = i-k- (k-s-k) . % stopped manually
but probably the result you wanted will still get generated directly, just after some more time.
Based on Will Ness answer here is my solution:
term(s).
term(k).
term(i).
term(app(X,Y)) :- term(X), term(Y).
eq(s,s).
eq(k,k).
eq(i,i).
eq(app(X,Y),app(X,Y)).
proc(s, s).
proc(k, k).
proc(i, i).
proc(app(i,Y), Y1) :- proc(Y,Y1).
proc(app(app(k,Y),Z), Y1) :- proc(Y,Y1).
proc(app(app(app(s,P1),P2),P3), Y1) :- proc(app( app(P1,P3), app(P2, P3) ), Y1).
proc(app(X, Y), Z) :- proc(X, X1), proc(Y, Y1), eq(X, X1), eq(X, X1), eq(app(X, Y), Z).
proc(app(X, Y), Z) :- proc(X, X1), proc(Y, Y1), not(eq(X, X1)), proc(app(X1, Y1), Z).
proc(app(X, Y), Z) :- proc(X, X1), proc(Y, Y1), not(eq(Y, Y1)), proc(app(X1, Y1), Z).
Move code to swish prolog. It works much faster
time((term(X), proc(app(app(k, X), s), app(s,k)))).
% 356 inferences, 0.000 CPU in 0.000 seconds (94% CPU, 3768472 Lips)
X = app(s,k)
Implemented complete reduction procedure:
proc(app(app(app(app(s,app(k,app(s,i))),k),s),k), X)
answer contains: X = app(k,s)
There are still issues that I can not resolve
time((term(X), proc(app(app(X, k), s), app(s,k)))). runs forever
Answers are not ordered by reductions.
Related
I have a rule that matches bc. When I encounter that in a string, I don't want to parse that string, otherwise parse anything else.
% Prolog
bc(B, C) --> [B, C], {
B = "b",
C = "c"
}.
not_bc(O) --> [O], % ?! bc(O, C).
% ?- phrase(not_bc(O), "bcdefg").
% false.
% ?- phrase(not_bc(O), "abcdefg").
% O = "a".
% ?- phrase(not_bc(O), "wxcybgz")
% O = "w".
% ?- phrase(not_bc(O), "wxybgz")
% O = "w".
Simplified version of my problem, hopefully solutions are isomorphic.
Similar to this question:
Translation to DCG Semicontext not working - follow on
An alternative:
process_bc(_) --> "bc", !, { fail }.
process_bc(C) --> [C].
This differs from my other solution in accepting:
?- time(phrase(process_bc(C), `b`, _)).
% 8 inferences, 0.000 CPU in 0.000 seconds (83% CPU, 387053 Lips)
C = 98.
In swi-prolog:
process_text(C1) --> [C1, C2], { dif([C1, C2], `bc`) }.
Results:
?- time(phrase(process_text(C), `bca`, _)).
% 11 inferences, 0.000 CPU in 0.000 seconds (79% CPU, 376790 Lips)
false.
?- time(phrase(process_text(C), `bd`, _)).
% 10 inferences, 0.000 CPU in 0.000 seconds (80% CPU, 353819 Lips)
C = 98.
?- time(phrase(process_text(C), `zbcagri4gj40w9tu4tu34ty3ty3478t348t`, _)).
% 10 inferences, 0.000 CPU in 0.000 seconds (80% CPU, 372717 Lips)
C = 122.
A single character, or no characters, are both presumably meant to be failures.
This is nicely efficient, only having to check the first 2 characters.
edge is a :- dynamic edge/2
the edge indicates only if the vertex are joined, example:
edge(a, b).
edge(c, d).
edge(r, c).
edge(c, t).
edge(a, t).
And I want to know if a vertex have 3 or more edges but only one of them, if there are more than one with 3 or more should return no.
Thanks
In SWI Prolog:
test() :-
findall([A,B], edge(A,B), VRaw), % find every vertex.
flatten(VRaw, VFlat), % make a flat list of them.
msort(VFlat, VSorted), % sort them.
clumped(VSorted, VClumped), % count how many of each.
include([_-C]>>(C>=3), VClumped, V3), % filter the ones with count>=3.
length(V3, 1). % was there exactly one?
Another possible solution:
unique_vertex_with_three_or_more_edges(V) :-
setof(V, vertex_with_three_or_more_edges(V), [V]).
vertex_with_three_or_more_edges(V) :-
setof(W, (edge(V,W) ; edge(W,V)), [_,_,_|_]).
Example:
?- time(unique_vertex_with_three_or_more_edges(X)).
% 78 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
X = c.
?- time(unique_vertex_with_three_or_more_edges(c)).
% 35 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
true.
?- time(unique_vertex_with_three_or_more_edges(a)).
% 33 inferences, 0.000 CPU in 0.000 seconds (?% CPU, Infinite Lips)
false.
I am new to this language and am having trouble coming up with a solution to this problem. The program must implement the following cases.
Both variables are instantiated:
pivot( [1,2,3,4,5,6,7], [5,6,7,4,1,2,3] ).`
yields a true/yes result.
Only Before is instantiated:
pivot( [1,2,3,4,5,6], R ).
unifies R = [4,5,6,1,2,3] as its one result.
Only After is instantiated:
pivot(L, [1,2]).
unifies L = [2,1] as its one result.
Neither variable is instantiated:
pivot(L, R).
is undefined (since results are generated arbitrarily).
If by pivot, you mean to split the list in 2 and swap the halves, then something like this would work.
First, consider the normal case: If you have an instantiated list, pivoting it is trivial. You just need to
figure out half the length of the list
break it up into
a prefix, consisting of that many items, and
a suffix, consisting of whatever is left over
concatenate those two lists in reverse order
Once you have that, everything else is just a matter of deciding which variable is bound and using that as the source list.
It is a common Prolog idiom to have a single "public" predicate that invokes a "private" worker predicate that does the actual work.
Given that the problem statement requires that at least one of the two variable in your pivot/2 must be instantiated, we can define our public predicate along these lines:
pivot( Ls , Rs ) :- nonvar(Ls), !, pivot0(Ls,Rs) .
pivot( Ls , Rs ) :- nonvar(Rs), !, pivot0(Rs,Ls) .
If Ls is bound, we invoke the worker, pivot0/2 with the arguments as-is. But if Ls is unbound, and Rs is bound, we invoke it with the arguments reversed. The cuts (!) are there to prevent the predicate from succeeding twice if invoked with both arguments bound (pivot([a,b,c],[a,b,c]).).
Our private helper, pivot0/2 is simple, because it knows that the 1st argument will always be bound:
pivot0( Ls , Rs ) :- % to divide a list in half and exchange the halves...
length(Ls,N0) , % get the length of the source list
N is N0 // 2 , % divide it by 2 using integer division
length(Pfx,N) , % construct a unbound list of the desired length
append(Pfx,Sfx,Ls) , % break the source list up into its two halves
append(Sfx,Pfx,Rs) % put the two halves back together in the desired order
. % Easy!
In swi-prolog:
:- use_module(library(dcg/basics)).
pivot_using_dcg3(Lst, LstPivot) :-
list_first(Lst, LstPivot, L1, L2, IsList),
phrase(piv3_up(L1), L1, L2),
% Improve determinism
(IsList = true -> ! ; true).
piv3_up(L), string(Ri), string(M), string(Le) --> piv3(L, Le, M, Ri).
piv3([], [], [], Ri) --> [], remainder(Ri).
piv3([_], [], [H], Ri) --> [H], remainder(Ri).
piv3([_, _|Lst], [H|T], M, Ri) --> [H], piv3(Lst, T, M, Ri).
% From 2 potential lists, rearrange them in order of usefulness
list_first(V1, V2, L1, L2, IsList) :-
( is_list(V1) ->
L1 = V1, L2 = V2,
IsList = true
; L1 = V2, L2 = V1,
(is_list(L1) -> IsList = true ; IsList = false)
).
Is general and deterministic, with good performance:
?- time(pivot_using_dcg3(L, P)).
% 18 inferences, 0.000 CPU in 0.000 seconds (88% CPU, 402441 Lips)
L = P, P = [] ;
% 8 inferences, 0.000 CPU in 0.000 seconds (86% CPU, 238251 Lips)
L = P, P = [_] ;
% 10 inferences, 0.000 CPU in 0.000 seconds (87% CPU, 275073 Lips)
L = [_A,_B],
P = [_B,_A] ;
% 10 inferences, 0.000 CPU in 0.000 seconds (94% CPU, 313391 Lips)
L = [_A,_B,_C],
P = [_C,_B,_A] ;
% 12 inferences, 0.000 CPU in 0.000 seconds (87% CPU, 321940 Lips)
L = [_A,_B,_C,_D],
P = [_C,_D,_A,_B] ;
% 12 inferences, 0.000 CPU in 0.000 seconds (86% CPU, 345752 Lips)
L = [_A,_B,_C,_D,_E],
P = [_D,_E,_C,_A,_B] ;
% 14 inferences, 0.000 CPU in 0.000 seconds (88% CPU, 371589 Lips)
L = [_A,_B,_C,_D,_E,_F],
P = [_D,_E,_F,_A,_B,_C] ;
?- numlist(1, 5000000, P), time(pivot_using_dcg3(L, P)).
% 7,500,018 inferences, 1.109 CPU in 1.098 seconds (101% CPU, 6759831 Lips)
The performance could be improved further, using difference lists for the final left-middle-right append, and cuts (sacrificing generality).
TL;DR: This question is about one particular aspect of evaluating candidate failure-slices.
The following code of ancestor_of/2 expresses the transitive-closure of child_of/2:
ancestor_of(X, Y) :-
child_of(Y, X).
ancestor_of(X, Z) :-
child_of(Z, Y),
ancestor_of(X, Y).
If we define child_of/2 and include several cycles ...
child_of(xx, xy).
child_of(xy, xx).
child_of(x, x).
... ancestor_of(X, Y) does not terminate universally:
?- ancestor_of(X, Y), false.
**LOOPS**
With SWI-Prolog, we can limit the amount of work invested in the execution of the query:
?- time(call_with_inference_limit((ancestor_of(_,_),false), 10000, R)).
% 10,008 inferences, 0.002 CPU in 0.002 seconds (100% CPU, 4579243 Lips)
R = inference_limit_exceeded ;
% 5 inferences, 0.000 CPU in 0.000 seconds (85% CPU, 235172 Lips)
false.
Alright, but it could be better!
First, we might be able to prove definitely that the goal loops.
Second, we might be able to do so with less effort.
Let's add an additional argument to ancestor_of/2 for transferring call stack information!
ancestor_of_open(X, Y, Open) :-
G = ancestor_of(X,Y),
( member(G, Open)
-> throw(loop(G,Open))
; ancestor_of_aux(X, Y, [G|Open])
).
ancestor_of_aux(X, Y, _Open) :-
child_of(Y, X).
ancestor_of_aux(X, Z, Open) :-
child_of(Z, Y),
ancestor_of_open(X, Y, Open).
Sample query:
?- time(ancestor_of_open(X,Y,[])).
% 4 inferences, 0.000 CPU in 0.000 seconds (92% CPU, 219696 Lips)
X = xy,
Y = xx ;
% 1 inferences, 0.000 CPU in 0.000 seconds (79% CPU, 61839 Lips)
X = xx,
Y = xy ;
% 1 inferences, 0.000 CPU in 0.000 seconds (79% CPU, 61084 Lips)
X = Y, Y = x ;
% 8 inferences, 0.000 CPU in 0.000 seconds (87% CPU, 317473 Lips)
X = Y, Y = xx ;
% 11 inferences, 0.000 CPU in 0.000 seconds (90% CPU, 262530 Lips)
ERROR: Unhandled exception: loop(ancestor_of(_G282,xx),[ancestor_of(_G282,xy),ancestor_of(_G282,xx)])
Is this it?! That was too easy. Does this cover the general case?
I feel like I'm missing something important, but I can't quite point my finger at it. Please help!
CLPFD-systems are not primarily targeted to handle quadratic equations efficiently, nevertheless, are there better ways to formulate problems like the following?
It seems the problem boils down to equations like the following. SWI with library(clpfd) gave:
?- time( ((L+7)^2#=L^2+189, L in 0..10000000) ).
% 252,169,718 inferences, 87208.554 CPU in 87445.038 seconds (100% CPU, 2892 Lips)
ERROR: Out of local stack
But now the latest version in SWI gives
?- time( ((L+7)^2#=L^2+189, L in 0..10000000) ).
% 3,805,545,940 inferences, 868.635 CPU in 870.311 seconds (100% CPU, 4381063 Lips)
L = 10.
and in SICStus 4.3beta7 I get:
| ?- statistics(runtime,_).
yes
| ?- (L+7)*(L+7)#=L*L+189, L in 0..10000000.
L = 10 ? ;
no
| ?- statistics(runtime,[_,T_ms]).
T_ms = 2550 ? ;
no
To solve this quickly, a constraint solver that has already a primitive X #= Y^2 constraint, might also implement the following rules:
Rule #1:
X #= (Y+C)^2 --> H #= Y^2, X #= H + 2*C*Y + C^2
% where C is an integer and X,Y variables
Rule #2:
X #= Z^2, Y #= Z^2 --> X #= Z^2, X #= Y.
% where X,Y,Z are variables
The above rules will reduce the equation to a linear equation, which is anyway directly solved by a decent CLP(FD) system. Here you see the difference between a system with and without rule #2:
Without Rule #2:
?- (L+7)*(L+7) #= L*L+189, stored.
_C #= 140+_B-14*L.
_B #>= 0.
_C #>= 0.
_B #= L*L.
_C #= L*L.
Yes
With Rule #2:
?- (L+7)*(L+7) #= L*L+189, stored.
L = 10
?- statistics(time, S), (L+7)*(L+7) #= L*L+189, statistics(time, T), U is T-S.
U = 3
But rule #2 looks a little ad hoc to me. Not yet sure whether one should keep it.
Bye
clpBNR fares better:
% *** clpBNR v0.9.13alpha ***.
Welcome to SWI-Prolog (threaded, 64 bits, version 8.4.3)
?- time(( { (L+7)**2 == L**2+189 }, L::integer(0, 10_000_000), solve(L) )).
% 1,269,854 inferences, 0.163 CPU in 0.164 seconds (100% CPU, 7770826 Lips)
L = 10 ;
% 271,125,405 inferences, 40.874 CPU in 40.957 seconds (100% CPU, 6633143 Lips)
false.
Comparing clpfd and clpBNR using a range that succeeds in a reasonable timescale:
?- use_module(library(clpfd)).
?- time( ((L+7)^2#=L^2+189, L in 0..90_000) ).
% 13,609,198 inferences, 11.482 CPU in 11.497 seconds (100% CPU, 1185274 Lips)
L = 10.
?- time(( { (L+7)**2 == L**2+189 }, L::integer(0, 90_000), solve(L) )).
% 540,550 inferences, 0.107 CPU in 0.107 seconds (100% CPU, 5058454 Lips)
L = 10 ;
% 2,105,638 inferences, 0.320 CPU in 0.321 seconds (100% CPU, 6578814 Lips)
false.