Is there an extensible, efficient way to write existential statements in Haskell without implementing an embedded logic programming language? Oftentimes when I'm implementing algorithms, I want to express existentially quantified first-order statements like
∃x.∃y.x,y ∈ xs ∧ x ≠ y ∧ p x y
where ∈ is overloaded on lists. If I'm in a hurry, I might write perspicuous code that looks like
find p [] = False
find p (x:xs) = any (\y -> x /= y && (p x y || p y x)) xs || find p xs
or
find p xs = or [ x /= y && (p x y || p y x) | x <- xs, y <- xs]
But this approach doesn't generalize well to queries returning values or predicates or functions of multiple arities. For instance, even a simple statement like
∃x.∃y.x,y,z ∈ xs ∧ x ≠ y ≠ z ∧ f x y z = g x y z
requires writing another search procedure. And this means a considerable amount of boilerplate code. Of course, languages like Curry or Prolog that implement narrowing or a resolution engine allow the programmer to write statements like:
find(p,xs,z) = x ∈ xs & y ∈ xs & x =/= y & f x y =:= g x y =:= z
to abuse the notation considerably, which performs both a search and returns a value. This problem arises often when implementing formally specified algorithms, and is often solved by combinations of functions like fmap, foldr, and mapAccum, but mostly explicit recursion. Is there a more general and efficient, or just general and expressive, way to write code like this in Haskell?
There's a standard transformation that allows you to convert
∃x ∈ xs : P
to
exists (\x -> P) xs
If you need to produce a witness you can use find instead of exists.
The real nuisance of doing this kind of abstraction in Haskell as opposed to a logic language is that you really must pass the "universe" set xs as a parameter. I believe this is what brings in the "fuss" to which you refer in your title.
Of course you can, if you prefer, stuff the universal set (through which you are searching) into a monad. Then you can define your own versions of exists or find to work with the monadic state. To make it efficient, you can try Control.Monad.Logic, but it may involve breaking your head against Oleg's papers.
Anyway, the classic encoding is to replace all binding constructs, including existential and universal quantifiers, with lambdas, and proceed with appropriate function calls. My experience is that this encoding works even for complex nested queries with a lot of structure, but that it always feels clunky.
Maybe I don't understand something, but what's wrong with list comprehensions? Your second example becomes:
[(x,y,z) | x <- xs, y <- xs, z <- xs
, x /= y && y /= z && x /= z
, (p1 x y z) == (p2 x y z)]
This allows you to return values; to check if the formula is satisfied, just use null (it won't evaluate more than needed because of laziness).
Related
I have started playing around with Cubical Agda. Last thing I tried doing was building the type of integers (assuming the type of naturals is already defined) in a way similar to how it is done in classical mathematics (see the construction of integers on wikipedia). This is
data dInt : Set where
_⊝_ : ℕ → ℕ → dInt
canc : ∀ a b c d → a + d ≡ b + c → a ⊝ b ≡ c ⊝ d
trunc : isSet (dInt)
After doing that, I wanted to define addition
_++_ : dInt → dInt → dInt
(x ⊝ z) ++ (u ⊝ v) = (x + u) ⊝ (z + v)
(x ⊝ z) ++ canc a b c d u i = canc (x + a) (z + b) (x + c) (z + d) {! !} i
...
I am now stuck on the part between the two braces. A term of type x + a + (z + d) ≡ z + b + (x + c) is asked. Not wanting to prove this by hand, I wanted to use the ring solver made in Cubical Agda. But I could never manage to make it work, even trying to set it up for simple ring equalities like x + x + x ≡ 3 * x.
How can I make it work ? Is there a minimal example to make it work for naturals ? There is a file NatExamples.agda in the library, but it makes you have to rewrite your equalities in a convoluted way.
You can see how the solver for natural numbers is supposed to be used in this file in the cubical library:
Cubical/Tactics/NatSolver/Examples.agda
Note that this solver is different from the solver for commutative rings, which is designed for proving equations in abstract rings and is explained here:
Cubical/Tactics/CommRingSolver/Examples.agda
However, if I read your problem correctly, the equality you want to prove requires the use of other propositional equalities in Nat. This is not supported by any solver in the cubical library (as far as I know, also the standard library doesn't support it). But of course, you can use the solver for all the steps that don't use other equalities.
Just in case you didn't spot this: here is a definition of the integers in math-style using the SetQuotients of the cubical library. SetQuotients help you to avoid the work related to your third constructor trunc. This means you basically just need to show some constructions are well defined as you would in 'normal' math.
I've successfully used the ring solver for exactly the same problem: defining Int as a quotient of ℕ ⨯ ℕ. You can find the complete file here, the relevant parts are the following:
Non-cubical propositional equality to path equality:
open import Cubical.Core.Prelude renaming (_+_ to _+̂_)
open import Relation.Binary.PropositionalEquality renaming (refl to prefl; _≡_ to _=̂_) using ()
fromPropEq : ∀ {ℓ A} {x y : A} → _=̂_ {ℓ} {A} x y → x ≡ y
fromPropEq prefl = refl
An example of using the ring solver:
open import Function using (_$_)
import Data.Nat.Solver
open Data.Nat.Solver.+-*-Solver
using (prove; solve; _:=_; con; var; _:+_; _:*_; :-_; _:-_)
reorder : ∀ x y a b → (x +̂ a) +̂ (y +̂ b) ≡ (x +̂ y) +̂ (a +̂ b)
reorder x y a b = fromPropEq $ solve 4 (λ x y a b → (x :+ a) :+ (y :+ b) := (x :+ y) :+ (a :+ b)) prefl x y a b
So here, even though the ring solver gives us a proof of _=̂_, we can use _=̂_'s K and _≡_'s reflexivity to turn that into a path equality which can be used further downstream to e.g. prove that Int addition is representative-invariant.
I'm currently learning SWI-Prolog. I want to implement a function factorable(X) which is true if X can be written as X = n*b.
This is what I've gotten so far:
isTeiler(X,Y) :- Y mod X =:= 0.
hatTeiler(X,X) :- fail,!.
hatTeiler(X,Y) :- isTeiler(Y,X), !; Z is Y+1, hatTeiler(X,Z),!.
factorable(X) :- hatTeiler(X,2).
My problem is now that I don't understand how to end the recursion with a fail without backtracking. I thought the cut would do the job but after hatTeilerfails when both arguments are equal it jumps right to isTeiler which is of course true if both arguments are equal. I also tried using \+ but without success.
It looks like you add cuts to end a recursion but this is usually done by making rule heads more specific or adding guards to a clause.
E.g. a rule:
x_y_sum(X,succ(Y,1),succ(Z,1)) :-
x_y_sum(X,Y,Z).
will never be matched by x_y_sum(X,0,Y). A recursion just ends in this case.
Alternatively, a guard will prevent the application of a rule for invalid cases.
hatTeiler(X,X) :- fail,!.
I assume this rule should prevent matching of the rule below with equal arguments. It is much easier just to add the inequality of X and Y as a conditon:
hatTeiler(X,Y) :-
Y>X,
isTeiler(Y,X),
!;
Z is Y+1,
hatTeiler(X,Z),
!.
Then hatTeiler(5,5) fails automatically. (*)
You also have a disjunction operator ; that is much better written as two clauses (i drop the cuts or not all possibilities will be explored):
hatTeiler(X,Y) :- % (1)
Y > X,
isTeiler(Y,X).
hatTeiler(X,Y) :- % (2)
Y > X,
Z is Y+1,
hatTeiler(X,Z).
Now we can read the rules declaratively:
(1) if Y is larger than X and X divides Y without remainder, hatTeiler(X,Y) is true.
(2) if Y is larger than X and (roughly speaking) hatTeiler(X,Y+1) is true, then hatTeiler(X, Y) is also true.
Rule (1) sounds good, but (2) sounds fishy: for specific X and Y we get e.g.: hatTeiler(4,15) is true when hatTeiler(4,16) is true. If I understand correctly, this problem is about divisors so I would not expect this property to hold. Moreover, the backwards reasoning of prolog will then try to deduce hatTeiler(4,17), hatTeiler(4,18), etc. which leads to non-termination. I guess you want the cut to stop the recursion but it looks like you need a different property.
Coming from the original property, you want to check if X = N * B for some N and B. We know that 2 <= N <= X and X mod N = 0. For the first one there is even a built-in called between/2 that makes the whole thing a two-liner:
hT(X,B) :-
between(2, X, B),
0 is (X mod B).
?- hT(12,X).
X = 2 ;
X = 3 ;
X = 4 ;
X = 6 ;
X = 12.
Now you only need to write your own between and you're done - all without cuts.
(*) The more general hasTeiler(X,X) fails because is (and <) only works when the right hand side (both sides) is variable-free and contains only arithmetic terms (i.e. numbers, +, -, etc).
If you put cut before the fail, it will be freeze the backtracking.
The cut operation freeze the backtracking , if prolog cross it.
Actually when prolog have failed, it backtracks to last cut.
for example :
a:- b,
c,!,
d,
e,!,
f.
Here, if b or c have failed, backtrack do not freeze.
if d or f have failed, backtrack Immediately freeze, because before it is a cut
if e have failed , it can backtrack just on d
I hope it be useful
We try to translate a very simple program in pseudo-code to Predicate Logic.
The program is straightforward and does not contain loops. (sequential)
It only consists of assignments of variables and if-else statements.
Unfortunately we do not have any good information provided to solve the problem. It would be great if someone has some
examples "conversions" of simple 5liner code snippets or
links to sources for free information, which describe the topic on the surface level. ( We only do predicate and prepositional logic and do not want to dive much deeper in the logic space. )
Kind regards
UPDATE:
After enough research I found the solution and can share it inc. examples.
The trick is to think of the program state as a set of all our arbitrary variables inc. a program counter which stands for the current instruction to be executed.
x = input;
x = x*2;
if (y>0)
x = x∗y ;
else
x = y;
We will form the Predicate P(x,i,y,pc).
From here we can build promises e.g.:
∀i∀x∀y(P (x, i, y, 1) => P (i, i, y, 2))
∀i∀x∀y(P (x, i, y, 2) => P (x ∗ 2, i, y, 3))
∀i∀x∀y(P (x, i, y, 3) ∧ y > 0 =⇒ P (x ∗ y, i, y, 4))
∀i∀x∀y(P (x, i, y, 3) ∧ ¬y > 0 =⇒ P (y, i, y, 4))
By incrementing the Program counter we make sure that the promises follow in order. Now we are able to define make a proof when given a premise for the Input e.g. P(x,4,7,1).
Let's suppose I use a BDD to represent the set of tuples in a relation. For simplicity consider tuples with 0 or 1 values. For instance:
R = {<0,0,1>, <1,0,1>, <0,0,0>} represent a ternary relation in a BDD with three variables, say x, y and z (one for each tuple's element). What I want is to implement an operation that given a bdd like for R and a cube C returns the subset of R that contains unique tuples when the variables in C are abstracted.
For instance, if C contains the variable x (which represents the first element in each tuple) the result of the function must be {<0,0,0>}. Notice that when x is abstracted away tuples <0,0,1> and <1,0,1> become "the same".
Now suppose R = {<0,0,1>, <1,0,1>, <0,0,0>, <1,0,0>} and we want to abstract x again. In this case I would expect the constant false as result because there is no unique tuple in R after abstracting x.
Any help is highly appreciated.
This could be done in three simple steps:
Make two BDDs with restricted value of the variable you want to abstract:
R[x=0] = restrict R with x = 0
R[x=1] = restrict R with x = 1
Apply XOR operation to this new BDDs
Q = R[x=0] xor R[x=1]
Enumerate all models of Q
Applying this to your examples:
R = {<0,0,1>, <1,0,1>, <0,0,0>} = (¬x ∧ ¬y ∧ z) ∨ (x ∧ ¬y ∧ z) ∨ (¬x ∧ ¬y ∧ ¬z)
R[x=1] = {<0,1>} = (¬y ∧ z)
R[x=0] = {<0,1>,<0,0>} = (¬y ∧ z) ∨ (¬y ∧ ¬z)
Q = R[x=1] xor R[x=0] = (¬y ∧ ¬z)
Intuition here is that XOR will cancel entries that occur in both BDDs.
This is easily (but with exponential complexity) generalized to the case with several abstracted variables.
I'm a beginner to functional languages, and I'm trying to get the whole thing down in Haskell. Here's a quick-and-dirty function that finds all the factors of a number:
factors :: (Integral a) => a -> [a]
factors x = filter (\z -> x `mod` z == 0) [2..x `div` 2]
Works fine, but I found it to be unbearably slow for large numbers. So I made myself a better one:
factorcalc :: (Integral a) => a -> a -> [a] -> [a]
factorcalc x y z
| y `elem` z = sort z
| x `mod` y == 0 = factorcalc x (y+1) (z ++ [y] ++ [(x `div` y)])
| otherwise = factorcalc x (y+1) z
But here's my problem: Even though the code works, and can cut literally hours off the execution time of my programs, it's hideous!
It reeks of ugly imperative thinking: It constantly updates a counter and a data structure in a loop until it finishes. Since you can't change state in purely functional programming, I cheated by holding the data in the parameters, which the function simply passes to itself over and over again.
I may be wrong, but there simply must be a better way of doing the same thing...
Note that the original question asked for all the factors, not for only the prime factors. There being many fewer prime factors, they can probably be found more quickly. Perhaps that's what the OQ wanted. Perhaps not. But let's solve the original problem and put the "fun" back in "functional"!
Some observations:
The two functions don't produce the same output---if x is a perfect square, the second function includes the square root twice.
The first function enumerates checks a number of potential factors proportional to the size of x; the second function checks only proportional to the square root of x, then stops (with the bug noted above).
The first function (factors) allocates a list of all integers from 2 to n div 2, where the second function never allocates a list but instead visits fewer integers one at a time in a parameter. I ran the optimizer with -O and looked at the output with -ddump-simpl, and GHC just isn't smart enough to optimize away those allocations.
factorcalc is tail-recursive, which means it compiles into a tight machine-code loop; filter is not and does not.
Some experiments show that the square root is the killer:
Here's a sample function that produces the factors of x from z down to 2:
factors_from x 1 = []
factors_from x z
| x `mod` z == 0 = z : factors_from x (z-1)
| otherwise = factors_from x (z-1)
factors'' x = factors_from x (x `div` 2)
It's a bit faster because it doesn't allocate, but it's still not tail-recursive.
Here's a tail-recursive version that is more faithful to the original:
factors_from' x 1 l = l
factors_from' x z l
| x `mod` z == 0 = factors_from' x (z-1) (z:l)
| otherwise = factors_from' x (z-1) l
factors''' x = factors_from x (x `div` 2)
This is still slower than factorcalc because it enumerates all the integers from 2 to x div 2, whereas factorcalc stops at the square root.
Armed with this knowledge, we can now create a more functional version of factorcalc which replicates both its speed and its bug:
factors'''' x = sort $ uncurry (++) $ unzip $ takeWhile (uncurry (<=)) $
[ (z, x `div` z) | z <- [2..x], x `mod` z == 0 ]
I didn't time it exactly, but given 100 million as an input, both it and factorcalc terminate instantaneously, where the others all take a number of seconds.
How and why the function works is left as an exercise for the reader :-)
ADDENDUM: OK, to mitigate the eyeball bleeding, here's a slightly saner version (and without the bug):
saneFactors x = sort $ concat $ takeWhile small $
[ pair z | z <- [2..], x `mod` z == 0 ]
where pair z = if z * z == x then [z] else [z, x `div` z]
small [z, z'] = z < z'
small [z] = True
Okay, take a deep breath. It'll be all right.
First of all, why is your first attempt slow? How is it spending its time?
Can you think of a recursive definition for the prime factorization that doesn't have that property?
(Hint.)
Firstly, although factorcalc is "ugly", you could add a wrapper function factors' x = factorscalc x 2 [], add a comment, and move on.
If you want to make a 'beautiful' factors fast, you need to find out why it is slow. Looking at your two functions, factors walks the list about n/2 elements long, but factorcalc stops after around sqrt n iterations.
Here is another factors that also stops after about sqrt n iterations, but uses a fold instead of explicit iteration. It also breaks the problem into three parts: finding the factors (factor); stopping at the square root of x (small) and then computing pairs of factors (factorize):
factors' :: (Integral a) => a -> [a]
factors' x = sort (foldl factorize [] (takeWhile small (filter factor [2..])))
where
factor z = x `mod` z == 0
small z = z <= (x `div` z)
factorize acc z = z : (if z == y then acc else y : acc)
where y = x `div` z
This is marginally faster than factorscalc on my machine. You can fuse factor and factorize and it is about twice as fast as factorscalc.
The Profiling and Optimization chapter of Real World Haskell is a good guide to the GHC suite's performance tools for tackling tougher performance problems.
By the way, I have a minor style nitpick with factorscalc: it is much more efficient to prepend single elements to the front of a list O(1) than it is to append to the end of a list of length n O(n). The lists of factors are typically small, so it is not such a big deal, but factorcalc should probably be something like:
factorcalc :: (Integral a) => a -> a -> [a] -> [a]
factorcalc x y z
| y `elem` z = sort z
| x `mod` y == 0 = factorcalc x (y+1) (y : (x `div` y) : z)
| otherwise = factorcalc x (y+1) z
Since you can't change state in purely
functional programming, I cheated by
holding the data in the parameters,
which the function simply passes to
itself over and over again.
Actually, this is not cheating; this is a—no, make that the—standard technique! That sort of parameter is usually known as an "accumulator," and it's generally hidden within a helper function that does the actual recursion after being set up by the function you're calling.
A common case is when you're doing list operations that depend on the previous data in the list. The two problems you need to solve are, where do you get the data about previous iterations, and how do you deal with the fact that your "working area of interest" for any particular iteration is actually at the tail of the result list you're building. For both of these, the accumulator comes to the rescue. For example, to generate a list where each element is the sum of all of the elements of the input list up to that point:
sums :: Num a => [a] -> [a]
sums inp = helper inp []
where
helper [] acc = reverse acc
helper (x:xs) [] = helper xs [x]
helper (x:xs) acc#(h:_) = helper xs (x+h : acc)
Note that we flip the direction of the accumulator, so we can operate on the head of that, which is much more efficient (as Dominic mentions), and then we just reverse the final output.
By the way, I found reading The Little Schemer to be a useful introduction and offer good practice in thinking recursively.
This seemed like an interesting problem, and I hadn't coded any real Haskell in a while, so I gave it a crack. I've run both it and Norman's factors'''' against the same values, and it feels like mine's faster, though they're both so close that it's hard to tell.
factors :: Int -> [Int]
factors n = firstFactors ++ reverse [ n `div` i | i <- firstFactors ]
where
firstFactors = filter (\i -> n `mod` i == 0) (takeWhile ( \i -> i * i <= n ) [2..n])
Factors can be paired up into those that are greater than sqrt n, and those that are less than or equal to (for simplicity's sake, the exact square root, if n is a perfect square, falls into this category. So if we just take the ones that are less than or equal to, we can calculate the others later by doing div n i. They'll be in reverse order, so we can either reverse firstFactors first or reverse the result later. It doesn't really matter.
This is my "functional" approach to the problem. ("Functional" in quotes, because I'd approach this problem the same way even in non-functional languages, but maybe that's because I've been tainted by Haskell.)
{-# LANGUAGE PatternGuards #-}
factors :: (Integral a) => a -> [a]
factors = multiplyFactors . primeFactors primes 0 [] . abs where
multiplyFactors [] = [1]
multiplyFactors ((p, n) : factors) =
[ pn * x
| pn <- take (succ n) $ iterate (* p) 1
, x <- multiplyFactors factors ]
primeFactors _ _ _ 0 = error "Can't factor 0"
primeFactors (p:primes) n list x
| (x', 0) <- x `divMod` p
= primeFactors (p:primes) (succ n) list x'
primeFactors _ 0 list 1 = list
primeFactors (_:primes) 0 list x = primeFactors primes 0 list x
primeFactors (p:primes) n list x
= primeFactors primes 0 ((p, n) : list) x
primes = sieve [2..]
sieve (p:xs) = p : sieve [x | x <- xs, x `mod` p /= 0]
primes is the naive Sieve of Eratothenes. There's better, but this is the shortest method.
sieve [2..]
=> 2 : sieve [x | x <- [3..], x `mod` 2 /= 0]
=> 2 : 3 : sieve [x | x <- [4..], x `mod` 2 /= 0, x `mod` 3 /= 0]
=> 2 : 3 : sieve [x | x <- [5..], x `mod` 2 /= 0, x `mod` 3 /= 0]
=> 2 : 3 : 5 : ...
primeFactors is the simple repeated trial-division algorithm: it walks through the list of primes, and tries dividing the given number by each, recording the factors as it goes.
primeFactors (2:_) 0 [] 50
=> primeFactors (2:_) 1 [] 25
=> primeFactors (3:_) 0 [(2, 1)] 25
=> primeFactors (5:_) 0 [(2, 1)] 25
=> primeFactors (5:_) 1 [(2, 1)] 5
=> primeFactors (5:_) 2 [(2, 1)] 1
=> primeFactors _ 0 [(5, 2), (2, 1)] 1
=> [(5, 2), (2, 1)]
multiplyPrimes takes a list of primes and powers, and explodes it back out to a full list of factors.
multiplyPrimes [(5, 2), (2, 1)]
=> [ pn * x
| pn <- take (succ 2) $ iterate (* 5) 1
, x <- multiplyPrimes [(2, 1)] ]
=> [ pn * x | pn <- [1, 5, 25], x <- [1, 2] ]
=> [1, 2, 5, 10, 25, 50]
factors just strings these two functions together, along with an abs to prevent infinite recursion in case the input is negative.
I don't know much about Haskell, but somehow I think this link is appropriate:
http://www.willamette.edu/~fruehr/haskell/evolution.html
Edit: I'm not entirely sure why people are so aggressive about the downvoting on this. The original poster's real problem was that the code was ugly; while it's funny, the point of the linked article is, to some extent, that advanced Haskell code is, in fact, ugly; the more you learn, the uglier your code gets, to some extent. The point of this answer was to point out to the OP that apparently, the ugliness of the code that he was lamenting is not uncommon.