Related
I want to find the minimum value of all permutations called from main predicate. For simplicity, I have removed my entire code, assume that I just want to find the minimum of head elements of all permutations.
appendlist([], X, X).
appendlist([T|H], X, [T|L]) :- appendlist(H, X, L).
permutation([], []).
permutation([X], [X]) :-!.
permutation([T|H], X) :- permutation(H, H1), appendlist(L1, L2, H1), appendlist(L1, [T], X1), appendlist(X1, L2, X).
%min(X, A, B) X is the minimum of A, B
min(X, X, Y) :- X =< Y.
min(Y, X, Y) :- Y < X.
solve([Head|Rest], Head):-
writeln([Head|Rest]).
main :-
Sort = [1, 2, 3],
PrvAns is 1000,
permutation(Sort, X),
solve(X, Here),
min(Ans, Here, PrvAns),
writeln(Ans),
PrvAns = Ans,
!, fail;
true,
writeln(PrvAns).
I want to calculate the minimum on fly for each permutation. Now, permute is working fine, and you can see that solve prints all permutations and even returns the first value Head properly, but PrvAns = Ans is wrong.
Expected output PrvAns : 1
I'm sorry if I didn't understand properly (and tell me, so I can help you), but, you mean something like this?
findMinHead(X,Z):-
findall( Y, ( permutation(X,[Y|_]) ), Z1 ),
min_list(Z1,Z).
in this predicate we find all the Y values where Y is the head of a permutation of X, put all that values in a bag, and then find the min.
Here is my code.
equals2(X,Y,N,I):- X is Y,I is N+1; I is N.
elemNum(X,[],0).
elemNum(X,[Y|Ys],N) :- elemNum(X,Ys,N1),equals2(X,Y,N1,I),N is I.
lemNum first argument is element from array, second is array. It counts the number of elements in array.
Then in console
| ?- elemNum(1,[1,2,3,1,1],N),N<2.
N = 1 ?
yes
I am sure than my function elemNum works just fine. How its possible that in console this assertion returns 1?
Thanks for help
Non sure to understand what do you want ... but I suppose that you want count the number of element in the list (second argument of elemNum/3) that are equals to the first argument.
If so, you should modify equals2/4 as follows
equals2(X,Y,N,I):- X is Y,I is N+1; X \== Y, I is N.
or better (IMHO) split it in 2 different clauses
equals2(X,X,N,I):- I is N+1.
equals2(X,Y,N,N):- X \== Y.
With your equal2/4, the second or case (I is N) is executed (in backtracking) even when X is equal to Y so elemNum(1,[1,2,3,1,1],N) unifiy N with 3, 2, 2 again, 1, 2, 1, 1 again and 0.
Regarding elemNum/3, works but you can semplify it (avoiding a warning) as
elemNum(_,[],0).
elemNum(X,[Y|Ys],I) :- elemNum(X,Ys,N1), equals2(X,Y,N1,I).
or you can rewrite it, avoiding the use of equals2/4 as
elemNum(_, [], 0).
elemNum(X, [X | Ys], I) :- elemNum(X, Ys, I0), I is I0+1.
elemNum(X, [Y | Ys], I) :- X \== Y, elemNum(X, Ys, I).
Me and a friend are writing a program which is supposed to solve a CLP problem. We want to use minimize to optimize the solution but it won't work, because it keeps saying that the number we get from sum(P,#=,S) is between two numbers (for example 5..7). We haven't been able to find a good way to extract any number from this or manipulate it in any way and are therefore looking for your help.
The problem seems to arise from our gen_var method which says that each element of a list must be between 0 and 1, so some numbers come out as "0..1" instead of being set properly.
Is there any way to use minimize even though we get a number like "5..7" or any way to manipulate that number so that we only get 5? S (the sum of the elements in a list) is what we're trying to minimize.
gen_var(0, []).
gen_var(N, [X|Xs]) :-
N > 0,
M is N-1,
gen_var(M, Xs),
domain([X],0,1).
find([],_).
find([H|T],P):- match(H,P),find(T,P).
match(pri(_,L),P):-member(X,L), nth1(X,P,1).
main(N,L,P,S) :- gen_var(N,P), minimize(findsum(L,P,S),S).
findsum(L,P,S):- find(L,P), sum(P,#=,S).
I've slightly modified your code, to adapt to SWI-Prolog CLP(FD), and it seems to work (kind of). But I think the minimum it's always 0!
:- use_module(library(clpfd)).
gen_var(0, []).
gen_var(N, [X|Xs]) :-
N > 0,
M is N-1,
gen_var(M, Xs),
X in 0..1 .
find([], _).
find([H|T], P):-
match(H, P),
find(T, P).
match(pri(_,L),P):-
member(X, L),
nth1(X, P, 1).
findsum(L,P,S) :-
find(L, P),
sum(P, #=, S).
main(N, L, P, S) :-
gen_var(N, P),
findsum(L, P, S),
labeling([min(S)], P).
Is this output sample a correct subset of the expected outcome?
?- main(3,A,B,C).
A = [],
B = [0, 0, 0],
C = 0 ;
A = [],
B = [0, 0, 1],
C = 1 ;
I have made two programs in Prolog for the nqueens puzzle using hill climbing and beam search algorithms.
Unfortunately I do not have the experience to check whether the programs are correct and I am in dead end.
I would appreciate if someone could help me out on that.
Unfortunately the program in hill climbing is incorrect. :(
The program in beam search is:
queens(N, Qs) :-
range(1, N, Ns),
queens(Ns, [], Qs).
range(N, N, [N]) :- !.
range(M, N, [M|Ns]) :-
M < N,
M1 is M+1,
range(M1, N, Ns).
queens([], Qs, Qs).
queens(UnplacedQs, SafeQs, Qs) :-
select(UnplacedQs, UnplacedQs1,Q),
not_attack(SafeQs, Q),
queens(UnplacedQs1, [Q|SafeQs], Qs).
not_attack(Xs, X) :-
not_attack(Xs, X, 1).
not_attack([], _, _) :- !.
not_attack([Y|Ys], X, N) :-
X =\= Y+N,
X =\= Y-N,
N1 is N+1,
not_attack(Ys, X, N1).
select([X|Xs], Xs, X).
select([Y|Ys], [Y|Zs], X) :- select(Ys, Zs, X).
I would like to mention this problem is a typical constraint satisfaction problem and can be efficiency solved using the CSP module of SWI-Prolog. Here is the full algorithm:
:- use_module(library(clpfd)).
queens(N, L) :-
N #> 0,
length(L, N),
L ins 1..N,
all_different(L),
applyConstraintOnDescDiag(L),
applyConstraintOnAscDiag(L),
label(L).
applyConstraintOnDescDiag([]) :- !.
applyConstraintOnDescDiag([H|T]) :-
insertConstraintOnDescDiag(H, T, 1),
applyConstraintOnDescDiag(T).
insertConstraintOnDescDiag(_, [], _) :- !.
insertConstraintOnDescDiag(X, [H|T], N) :-
H #\= X + N,
M is N + 1,
insertConstraintOnDescDiag(X, T, M).
applyConstraintOnAscDiag([]) :- !.
applyConstraintOnAscDiag([H|T]) :-
insertConstraintOnAscDiag(H, T, 1),
applyConstraintOnAscDiag(T).
insertConstraintOnAscDiag(_, [], _) :- !.
insertConstraintOnAscDiag(X, [H|T], N) :-
H #\= X - N,
M is N + 1,
insertConstraintOnAscDiag(X, T, M).
N is the number of queens or the size of the board (), and , where , being the position of the queen on the line .
Let's details each part of the algorithm above to understand what happens.
:- use_module(library(clpfd)).
It indicates to SWI-Prolog to load the module containing the predicates for constraint satisfaction problems.
queens(N, L) :-
N #> 0,
length(L, N),
L ins 1..N,
all_different(L),
applyConstraintOnDescDiag(L),
applyConstraintOnAscDiag(L),
label(L).
The queens predicate is the entry point of the algorithm and checks if the terms are properly formatted (number range, length of the list). It checks if the queens are on different lines as well.
applyConstraintOnDescDiag([]) :- !.
applyConstraintOnDescDiag([H|T]) :-
insertConstraintOnDescDiag(H, T, 1),
applyConstraintOnDescDiag(T).
insertConstraintOnDescDiag(_, [], _) :- !.
insertConstraintOnDescDiag(X, [H|T], N) :-
H #\= X + N,
M is N + 1,
insertConstraintOnDescDiag(X, T, M).
It checks if there is a queen on the descendant diagonal of the current queen that is iterated.
applyConstraintOnAscDiag([]) :- !.
applyConstraintOnAscDiag([H|T]) :-
insertConstraintOnAscDiag(H, T, 1),
applyConstraintOnAscDiag(T).
insertConstraintOnAscDiag(_, [], _) :- !.
insertConstraintOnAscDiag(X, [H|T], N) :-
H #\= X - N,
M is N + 1,
insertConstraintOnAscDiag(X, T, M).
Same as previous, but it checks if there is a queen on the ascendant diagonal.
Finally, the results can be found by calling the predicate queens/2, such as:
?- findall(X, queens(4, X), L).
L = [[2, 4, 1, 3], [3, 1, 4, 2]]
If I read your code correctly, the algorithm you're trying to implement is a simple depth-first search rather than beam search. That's ok, because it should be (I don't see how beam search will be effective for this problem and it can be hard to program).
I'm not going to debug this code for you, but I will give you a suggestion: build the chess board bottom-up with
queens(0, []).
queens(N, [Q|Qs]) :-
M is N-1,
queens(M, Qs),
between(1, N, Q),
safe(Q, Qs).
where safe(Q,Qs) is true iff none of Qs attack Q. safe/2 is then the conjunction of a simple memberchk/2 check (see SWI-Prolog manual) and your not_attack/2 predicate, which on first sight seems to be correct.
A quick check on Google has found a few candidates for you to compare with your code and find what to change.
My favoured solution for sheer clarity would be the second of the ones linked to above:
% This program finds a solution to the 8 queens problem. That is, the problem of placing 8
% queens on an 8x8 chessboard so that no two queens attack each other. The prototype
% board is passed in as a list with the rows instantiated from 1 to 8, and a corresponding
% variable for each column. The Prolog program instantiates those column variables as it
% finds the solution.
% Programmed by Ron Danielson, from an idea by Ivan Bratko.
% 2/17/00
queens([]). % when place queen in empty list, solution found
queens([ Row/Col | Rest]) :- % otherwise, for each row
queens(Rest), % place a queen in each higher numbered row
member(Col, [1,2,3,4,5,6,7,8]), % pick one of the possible column positions
safe( Row/Col, Rest). % and see if that is a safe position
% if not, fail back and try another column, until
% the columns are all tried, when fail back to
% previous row
safe(Anything, []). % the empty board is always safe
safe(Row/Col, [Row1/Col1 | Rest]) :- % see if attack the queen in next row down
Col =\= Col1, % same column?
Col1 - Col =\= Row1 - Row, % check diagonal
Col1 - Col =\= Row - Row1,
safe(Row/Col, Rest). % no attack on next row, try the rest of board
member(X, [X | Tail]). % member will pick successive column values
member(X, [Head | Tail]) :-
member(X, Tail).
board([1/C1, 2/C2, 3/C3, 4/C4, 5/C5, 6/C6, 7/C7, 8/C8]). % prototype board
The final link, however, solves it in three different ways so you can compare against three known solutions.
hi is there anybody know how can i do the pascal nth row
when i ask for
:? pascal(2,Row).
i get Row=[1,2,1]
??
please help me
Here is the code to compute the nth row.
The first part scans a row, to compute the next row. The first row must be prefixed with a 0, so that the first "1" in the next row is a sum, like the other elements. It recurses on the 2 lists:
pascal_next_row([X],[X]).
pascal_next_row([H,H2|T],[A|B]):-
pascal_next_row([H2|T],B),
A is H + H2.
The second part computes all the rows until the one which was asked. It recurses on N:
pascal(0, [1]) :- !.
pascal(N, R) :-
N1 is N-1,
pascal(N1, R1),
pascal_next_row([0|R1], R).
If you need the full triangle, all you have to do is change the second parameter to handle a list of rows, and collect them:
pascal(0, [[1]]) :- !.
pascal(N, [R, R1 | RN]) :-
N1 is N-1,
pascal(N1, [R1 | RN]),
pascal_next_row([0|R1], R).
This answer to a code golf has the implementation in prolog, just expand the names:
The Pascal Triangle is also known as the Tartaglia Triangle:
sumC([X,Y],[Z]) :- Z is X + Y.
sumC([X,Y|L], Z):- H is X + Y,
sumC([Y|L],L2),
Z = [H|L2].
tartaglia(1,[1]) :- ! .
tartaglia(2,[1,1]) :- !.
tartaglia(N, L) :- Ant is N - 1,
tartaglia(Ant,L2),
sumC(L2,R),
append([1|R],[1],L), !.
Using the helper predicate sumC, you can get it easily:
?- tartaglia(3,R).
R = [1, 2, 1].
?- tartaglia(2,R).
R = [1, 1].
?- tartaglia(1,R).
R = [1].
?- tartaglia(6,R).
R = [1, 5, 10, 10, 5, 1].
As said in my comment. You ask for the nth row. [1,2,1] from your example is the 3rd row.