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I'm in the process of coding the knight's tour function, and I'm as far as this where I'm getting an infinte loop in my ghci:
type Field = (Int, Int)
nextPositions:: Int -> Field -> [Field]
nextPositions n (x,y) = filter onBoard
[(x+2,y-1),(x+2,y+1),(x-2,y-1),(x-2,y+1),(x+1,y-2),(x+1,y+2),(x-1,y-2),(x-1,y+2)]
where onBoard (x,y) = x `elem` [1..n] && y `elem` [1..n]
type Path = [Field]
knightTour :: Int -> Field -> [Path]
knightTour n start = [posi:path | (posi,path) <- tour (n*n)]
where tour 1 = [(start, [])]
tour k = [(posi', posi:path) | (posi, path) <- tour (k-1), posi' <- (filter (`notElem` path) (nextPositions n posi))]
F.e. knightTour 10 (4,4) does not give an output!
Any advise?
I think one of the main problems is checking if you have visited a square. This takes too much time. You should look for a data structure that makes that more efficient.
For small boards, for example up to 8×8, you can make use of a 64-bit integer for that. A 64-bit can be seen as 64 booleans that each can represent whether the knight already has visited that place.
we thus can implement this with:
{-# LANGUAGE BangPatterns #-}
import Data.Bits(testBit, setBit)
import Data.Word(Word64)
testPosition :: Int -> Word64 -> (Int, Int) -> Bool
testPosition !n !w (!r, !c) = testBit w (n*r + c)
setPosition :: Int -> (Int, Int) -> Word64 -> Word64
setPosition !n (!r, !c) !w = setBit w (n*r + c)
nextPositions :: Int -> Word64 -> (Int, Int) -> [(Int, Int)]
nextPositions !n !w (!x, !y) = [ c
| c#(x', y') <- [(x-1,y-2), (x-1,y+2), (x+1,y-2), (x+1,y+2), (x-2,y-1), (x-2,y+1), (x+2,y-1), (x+2,y+1)]
, x' >= 0
, y' >= 0
, x' < n
, y' < n
, not (testPosition n w c)
]
knightTour :: Int -> (Int, Int) -> [[(Int, Int)]]
knightTour n p0 = go (n*n-1) (setPosition n p0 0) p0
where go 0 _ _ = [[]]
go !k !w !ps = [
(ps':rs)
| ps' <- nextPositions n w ps
, rs <- go (k-1) (setPosition n ps' w) ps'
]
main = print (knightTour 6 (1,1))
If I compile this with the -O2 flag and run this locally for a 5×5 board where the knight starts at (1,1), all the solutions are generated in 0.32 seconds. For a 6×6 board, it takes 2.91 seconds to print the first solution, but it takes forever to find all solutions that start at (1,1). For an 8×8 board, the first solution was found in 185.76 seconds:
[(0,3),(1,5),(0,7),(2,6),(1,4),(0,2),(1,0),(2,2),(3,0),(4,2),(3,4),(4,6),(5,4),(6,2),(5,0),(3,1),(2,3),(3,5),(2,7),(0,6),(2,5),(1,3),(0,1),(2,0),(3,2),(2,4),(0,5),(1,7),(3,6),(4,4),(5,6),(7,7),(6,5),(7,3),(6,1),(4,0),(5,2),(7,1),(6,3),(7,5),(6,7),(5,5),(4,7),(6,6),(7,4),(5,3),(7,2),(6,0),(4,1),(3,3),(2,1),(0,0),(1,2),(0,4),(1,6),(3,7),(4,5),(5,7),(7,6),(6,4),(4,3),(5,1),(7,0)]
It is however not a good idea to solve this with a brute force approach. If we assume an average branching factor of ~6 moves, then for a 6×6 board, we have already 1.031×1028 possible sequences we have to examine for a 6×6 board.
It is better to work with a divide and conquer approach. It is easy to split a board like 8×8 into four 4×4 boards. Then you determine places where you can hop from one board to another, and then you solve the subproblems for a 4×4 board. For small boards, you can easily store the solutions to go from any square to any other square on a 4×4 board, and then reuse these for all quadrants, so you save computational effort, by not calculating this a second time, especially since you do not need to store symmetrical queries multiple times. If you know how to go from (1,0) to (2,3) on a 4×4 board, you can easily use this to go from (3,0) to (2,3) on the same board, just by mirroring this.
I am currently working on populating a grid in Haskell defined as type Grid = UArray (Int, Int) Bool where the two Ints are the x and y coordinates. I am trying to turn this into a 10 by 10 data structure where the data types are Boolean. I can get the values as such the listArray is part of the Data.Array.Unboxed package
import System.IO
import Control.Monad
import Data.Array.Unboxed
import Data.List (unfoldr)
type Board = UArray (Int, Int) Bool
progress :: Int -> Int -> Board -> Board
progress width height previous =
listArray b (map f (range b))
where b#((y1,x1),(y2,x2)) = bounds previous
-- some basic math functions performed here
total :: [Bool] -> Int
total = length . filter id
board :: [String] -> (Int, Int, Board)
board l = (width, height, a)
where (width, height) = (length $ head l, length l)
a = listArray ((1, 1), (height, width)) $ concatMap f l
f = map g
g 'f' = False
g _ = True
printGrid :: Int -> Board -> IO ()
printGrid width = mapM_ f . split width . elems
where f = putStrLn . map g
g False = 'f'
g _ = 't'
split :: Int -> [a] -> [[a]]
split n = takeWhile (not . null) . unfoldr (Just . splitAt n)
-- used only as a test trying to create same structure programatically
testBoard = board
["ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff",
"ffffffffff"]
printProgress :: Int -> (Int, Int, Board) -> IO ()
printProgress n (width, height, g) = mapM_ f $ take n $ iterate (progress width height) g
where f g = do
putStrLn "------------------------------"
printGrid width g
main :: IO ()
main = do
printProgress times testBoard
You can make a UArray using the array function or the listArray function. The first expects a list of indices and elements while the second expects a tuple indicating the index range and a list of elements that are paired with an index automatically (lexicographic order).
Data.Array.Unboxed> listArray ((0,0), (9,9)) (replicate 100 False) :: UArray (Int,Int) Bool
array ((0,0),(9,9)) [((0,0),False),((0,1),False),((0,2),False),((0,3),False),((0,4),False),((0,5),False),((0,6),False),((0,7),False),((0,8),False),((0,9),False),((1,0),False),((1,1),False),((1,2),False),((1,3),False),((1,4),False),((1,5),False),((1,6),False),((1,7),False),((1,8),False),((1,9),False),((2,0),False),((2,1),False),((2,2),False),((2,3),False),((2,4),False),((2,5),False),((2,6),False),((2,7),False),((2,8),False),((2,9),False),((3,0),False),((3,1),False),((3,2),False),((3,3),False),((3,4),False),((3,5),False),((3,6),False),((3,7),False),((3,8),False),((3,9),False),((4,0),False),((4,1),False),((4,2),False),((4,3),False),((4,4),False),((4,5),False),((4,6),False),((4,7),False),((4,8),False),((4,9),False),((5,0),False),((5,1),False),((5,2),False),((5,3),False),((5,4),False),((5,5),False),((5,6),False),((5,7),False),((5,8),False),((5,9),False),((6,0),False),((6,1),False),((6,2),False),((6,3),False),((6,4),False),((6,5),False),((6,6),False),((6,7),False),((6,8),False),((6,9),False),((7,0),False),((7,1),False),((7,2),False),((7,3),False),((7,4),False),((7,5),False),((7,6),False),((7,7),False),((7,8),False),((7,9),False),((8,0),False),((8,1),False),((8,2),False),((8,3),False),((8,4),False),((8,5),False),((8,6),False),((8,7),False),((8,8),False),((8,9),False),((9,0),False),((9,1),False),((9,2),False),((9,3),False),((9,4),False),((9,5),False),((9,6),False),((9,7),False),((9,8),False),((9,9),False)]
Going one step further, you can parse your input [String] into a board by simply concatenating and mapping a test for == 't':
stringsToGrid :: [String] -> Grid
stringsToGrid = listArray ((0,0), (9,9)) . map (== 't') . concat
A Pipe can be broken into two parts: the generator part (yield) and the consumer part (await).
If you have a Pipe that only uses it's generator half, and only returns () (or never returns), then it can be represented as a "ListT done right". It turns out that MonadPlus can be used to represent anything like ListT-done-right. Quoting Gabriel Gonzalez:
Note that you can build any ListT (not just the one in pipes) with only a transformers dependency. For example, here is how you would implement a ListT analog of Pipes.Prelude.stdinLn:
-- stdinLn :: ListT IO String
stdinLn :: (MonadTrans t, MonadPlus (t IO)) => t IO String
stdinLn = do
eof <- lift isEOF
if eof
then mzero
else do
str <- lift getLine
return str `mplus` stdinLn
That will type check as any ListT out there and do the right thing for all of them.
So my question is this: Is there a dual to ListT and to MonadPlus for the consumer portion of Pipes?
Requirements:
A pipe which never uses yield, and only returns () (or never returns), but does use await can be represented as this "dual to ListT".
The "dual to ListT" can be generalized to the "dual of MonadPlus"
I think the answer is not to dualize the "generator-like" type-class, but rather to extend it with a simple Category instance equivalent to the await/(>~) category of pipes.
Unfortunately, there is no way to arrange the type variables to make this satisfy all three type classes (MonadPlus, MonadTrans, and Category), so I will define a new type class:
{-# LANGUAGE KindSignatures #-}
import Control.Monad
import Control.Monad.Trans.Class
class Consumer (t :: * -> (* -> *) -> * -> *) where
await :: t a m a
(>~) :: t a m b -> t b m c -> t a m c
The laws for this type class are the category laws:
await >~ f = f
f >~ await = f
(f >~ g) >~ h = f >~ (g >~ h)
Then you can implement both Consumers and Pipes once you have this additional type class:
printer :: (Show a, Monad (t a IO), MonadTrans (t a), Consumer t) => t a IO r
printer = do
a <- await
lift (print a)
printer
{-
printer :: Show a => Consumer a IO r
printer = do
a <- await
lift (print a)
printer
-}
cat :: (MonadPlus (t a m), Consumer t) => t a m a
cat = await `mplus` cat
{-
cat :: Monad m => Pipe a a m r
cat = do
a <- await
yield a
cat
-}
debug :: (Show a, MonadPlus (t a IO), MonadTrans (t a), Consumer t) => t a IO a
debug = do
a <- await
lift (print a)
return a `mplus` debug
{-
debug :: Show a => Pipe a a IO r
debug = do
a <- await
lift (print a)
yield a
debug
-}
taker :: (Consumer t, MonadPlus (t a m)) => Int -> t a m a
taker 0 = mzero
taker n = do
a <- await
return a `mplus` taker (n - 1)
{-
taker :: Monad m => Int -> Pipe a a m ()
taker 0 = return ()
taker n = do
a <- await
yield a
taker (n - 1)
-}
The hard part is figuring out how to do this without adding a new type class to base. I'd prefer to reuse the original Category type class if possible, possibly having await and (>~) just be functions that wrap your type in a newtype, use the Category instance, and then unwrap it, but I'm still working out the specifics of how to do that.
Edit: I found the solution. Just define the following newtype:
{-# LANGUAGE KindSignatures, FlexibleContexts #-}
import Control.Category
import Prelude hiding ((.), id)
newtype Consumer t m a b = Consumer { unConsumer :: t a m b }
await :: Category (Consumer t m) => t a m a
await = unConsumer id
(>~) :: Category (Consumer t m) => t a m b -> t b m c -> t a m c
f >~ g = unConsumer (Consumer f >>> Consumer g)
Then any library can just implement a Category instance for their type wrapped in the Consumer newtype.
Then you would get a constraint like this any time you used await or (>~):
cat :: (MonadPlus (t a m), Category (Consumer t m)) => t a m a
cat = await `mplus` cat
EDIT3: I'm writing a code to process very long input list of Ints with only few hundred non-duplicates. I use two auxiliary lists to maintain cumulative partial sums to calculate some accumulator value, the how's and why's are non-important. I want to ditch all lists here and turn it into nice destructive loop, and I don't know how. I don't need the whole code, just a skeleton code would be great, were read/write is done to two auxiliary arrays and some end result is returned. What I have right now would run 0.5 hour for the input. I've coded this now in C++, and it runs in 90 seconds for the same input.
I can't understand how to do this, at all. This is the list-based code that I have right now:(but the Map-based code below is clearer)
ins :: (Num b, Ord a) => a -> b -> [(a, b)] -> ([(a, b)], b)
ins n x [] = ( [(n,x)], 0)
ins n x l#((v, s):t) =
case compare n v of
LT -> ( (n,s+x) : l , s )
EQ -> ( (n,s+x) : t , if null t then 0 else snd (head t))
GT -> let (u,z) = ins n x t
in ((v,s+x):u,z)
This is used in a loop, to process a list of numbers of known length, (changed it to foldl now)
scanl g (0,([],[])) ns -- ns :: [Int]
g ::
(Num t, Ord t, Ord a) =>
(t, ([(a, t)], [(a, t)])) -> a -> (t, ([(a, t)], [(a, t)]))
g (c,( a, b)) n =
let
(a2,x) = ins n 1 a
(b2,y) = if x>0 then ins n x b else (b,0)
c2 = c + y
in
(c2,( a2, b2))
This works, but I need to speed it up. In C, I would keep the lists (a,b) as arrays; use binary search to find the element with the key just above or equal to n (instead of the sequential search used here); and use in-place update to change all the preceding entries.
I'm only really interested in final value. How is this done in Haskell, with mutable arrays?
I tried something, but I really don't know what I'm doing here, and am getting strange and very long error messages (like "can not deduce ... from context ..."):
goarr top = runSTArray $ do
let sz = 10000
a <- newArray (1,sz) (0,0) :: ST s (STArray s Int (Integer,Integer))
b <- newArray (1,sz) (0,0) :: ST s (STArray s Int (Integer,Integer))
let p1 = somefunc 2 -- somefunc :: Integer -> [(Integer, Int)]
go1 p1 2 0 top a b
go1 p1 i c top a b =
if i >= top
then
do
return c
else
go2 p1 i c top a b
go2 p1 i c top a b =
do
let p2 = somefunc (i+1) -- p2 :: [(Integer, Int)]
let n = combine p1 p2 -- n :: Int
-- update arrays and calc new c
-- like the "g" function is doing:
-- (a2,x) = ins n 1 a
-- (b2,y) = if x>0 then ins n x b else (b,0)
-- c2 = c + y
go1 p2 (i+1) c2 top a b -- a2 b2??
This doesn't work at all. I don't even know how to encode loops in do notation. Please help.
UPD: the Map based code that runs 3 times slower:
ins3 :: (Ord k, Num a) => k -> a -> Map.Map k a -> (Map.Map k a, a)
ins3 n x a | Map.null a = (Map.insert n x a , 0)
ins3 n x a = let (p,q,r) = Map.splitLookup n a in
case q of
Nothing -> (Map.union (Map.map (+x) p)
(Map.insert n (x+leftmost r) r) , leftmost r)
Just s -> (Map.union (Map.map (+x) p)
(Map.insert n (x+s) r) , leftmost r)
leftmost r | Map.null r = 0
| otherwise = snd . head $ Map.toList r
UPD2: The error message is " Could not deduce (Num (STArray s1 i e)) from the context () arising from the literal `0' at filename.hs:417:11"
that's where it says return c in go1 function. Perhaps c is expected to be an array, but I want to return the accumulator value that is built while using the two auxiliary arrays.
EDIT3: I've replaced scanl and (!!) with foldl and take as per Chris's advice, and now it runs in constant space with sane empirical complexity and is actually projected to finish in under 0.5 hour - a.o.t. ... 3 days ! I knew about it of course but was so sure GHC optimizes the stuff away for me, surely it wouldn't make that much of a difference, I thought! And so felt only mutable arrays could help... Bummer.
Still, C++ does same in 90 sec, and I would very much appreciate help in learning how to code this with mutable arrays, in Haskell.
Are the input values ever EQ? If they are not EQ then the way scanl g (0,([],[])) ns is used means that the first [(,)] array, call it a always has map snd a == reverse [1..length a] at each stage of g. For example, in a length 10 list the value of snd (a !! 4) is going to be 10-4. Keeping these reversed index values by mutating the second value of each preceding entry in a is quite wasteful. If you need speed then this is one place to make a better algorithm.
None of this applies to the second [(,)] whose purpose is still mysterious to me. It records all insertions that were not done at the end of a, so perhaps it allows one to reconstruct the initial sequence of values.
You said "I'm only really interested in final value." Do you mean you only care about the last value in list output by the scanl .. line? If so then you need a foldl instead of scanl.
Edit: I am adding a non-mutable solution using a custom Finger Tree. It passes my ad hoc testing (at bottom of code):
{-# LANGUAGE MultiParamTypeClasses #-}
import Data.Monoid
import Data.FingerTree
data Entry a v = E !a !v deriving Show
data ME a v = NoF | F !(Entry a v) deriving Show
instance Num v => Monoid (ME a v) where
mempty = NoF
NoF `mappend` k = k
k `mappend` NoF = k
(F (E _a1 v1)) `mappend` (F (E a2 v2)) = F (E a2 (v1 + v2))
instance Num v => Measured (ME a v) (Entry a v) where
measure = F
type M a v = FingerTree (ME a v) (Entry a v)
getV NoF = 0
getV (F (E _a v)) = v
expand :: Num v => M a v -> [(a, v)]
expand m = case viewl m of
EmptyL -> []
(E a _v) :< m' -> (a, getV (measure m)) : expand m'
ins :: (Ord a, Num v) => a -> v -> M a v -> (M a v, v)
ins n x m =
let comp (F (E a _)) = n <= a
comp NoF = False
(lo, hi) = split comp m
in case viewl hi of
EmptyL -> (lo |> E n x, 0)
(E v s) :< higher | n < v ->
(lo >< (E n x <| hi), getV (measure hi))
| otherwise ->
(lo >< (E n (s+x) <| higher), getV (measure higher))
g :: (Num t, Ord t, Ord a) =>
(t, (M a t, M a t)) -> a -> (t, (M a t, M a t))
g (c, (a, b)) n =
let (a2, x) = ins n 1 a
(b2, y) = if x>0 then ins n x b else (b, 0)
in (c+y, (a2, b2))
go :: (Ord a, Num v, Ord v) => [a] -> (v, ([(a, v)], [(a, v)]))
go ns = let (t, (a, b)) = foldl g (0, (mempty, mempty)) ns
in (t, (expand a, expand b))
up = [1..6]
down = [5,4..1]
see'tests = map go [ up, down, up ++ down, down ++ up ]
main = putStrLn . unlines . map show $ see'test
Slightly unorthodox, I am adding a second answer using a mutable technique. Since user1308992 mentioned Fenwick trees, I have used them to implement the algorithm. Two STUArray are allocated and mutated during the run. The basic Fenwick tree keeps totals for all smaller indices and the algorithm here needs totals for all larger indices. This change is handled by the (sz-x) subtraction.
import Control.Monad.ST(runST,ST)
import Data.Array.ST(STUArray,newArray)
import Data.Array.Base(unsafeRead, unsafeWrite)
import Data.Bits((.&.))
import Debug.Trace(trace)
import Data.List(group,sort)
{-# INLINE lsb #-}
lsb :: Int -> Int
lsb i = (negate i) .&. i
go :: [Int] -> Int
go xs = compute (maximum xs) xs
-- Require "top == maximum xs" and "all (>=0) xs"
compute :: Int -> [Int] -> Int
compute top xs = runST mutating where
-- Have (sz - (top+1)) > 0 to keep algorithm simple
sz = top + 2
-- Reversed Fenwick tree (no bounds checking)
insert :: STUArray s Int Int -> Int -> Int -> ST s ()
insert arr x v = loop (sz-x) where
loop i | i > sz = return ()
| i <= 0 = error "wtf"
| otherwise = do
oldVal <- unsafeRead arr i
unsafeWrite arr i (oldVal + v)
loop (i + lsb i)
getSum :: STUArray s Int Int -> Int -> ST s Int
getSum arr x = loop (sz - x) 0 where
loop i acc | i <= 0 = return acc
| otherwise = do
val <- unsafeRead arr i
loop (i - lsb i) $! acc + val
ins n x arr = do
insert arr n x
getSum arr (succ n)
mutating :: ST s Int
mutating = do
-- Start index from 0 to make unsafeRead, unsafeWrite easy
a <- newArray (0,sz) 0 :: ST s (STUArray s Int Int)
b <- newArray (0,sz) 0 :: ST s (STUArray s Int Int)
let loop [] c = return c
loop (n:ns) c = do
x <- ins n 1 a
y <- if x > 0
then
ins n x b
else
return 0
loop ns $! c + y
-- Without debugging use the next line
-- loop xs 0
-- With debugging use the next five lines
c <- loop xs 0
a' <- see a
b' <- see b
trace (show (c,(a',b'))) $ do
return c
-- see is only used in debugging
see arr = do
let zs = map head . group . sort $ xs
vs <- sequence [ getSum arr z | z <- zs ]
let ans = filter (\(a,v) -> v>0) (zip zs vs)
return ans
up = [1..6]
down = [5,4..1]
see'tests = map go [ up, down, up ++ down, down ++ up ]
main = putStrLn . unlines . map show $ see'tests
let me call the function accumrArray.
accumrArray ::
(e' -> e -> e) An accumulating function
-> e A default element
-> (i, i) The bounds of the array
-> [(i, e')] List of associations
-> a i e The array
accumrArray (:) [] (1,2) [(1,1),(2,2),(2,3)] === array [(1,[1]), (2,[2,3])]
head $ (accumrArray (:) [] (1,1) [(1,x)|x<-[4..]]) ! 1 === 4
How strange... I wrote this function a few days ago for someone else. The function first appeared in LML (I believe), but never made it into the Haskell array library.
Here you go:
{-# LANGUAGE ScopedTypeVariables #-}
import Data.Array
import System.IO.Unsafe
import Data.IORef
import Data.Array.MArray
import Data.Array.Base
import Control.Monad
import Data.Array.IO
accumArrayR :: forall a e i. Ix i => (a -> e -> e) -> e -> (i,i) -> [(i,a)] -> Array i e
accumArrayR f e bounds#(l,u) assocs = unsafePerformIO $ do
ref <- newIORef assocs
arr <- newArray_ bounds
let _ = arr :: IOArray i e
let n = safeRangeSize (l,u)
let elem x = unsafePerformIO $ do
ass <- readIORef ref
let loop [] = writeIORef ref [] >> return e
loop ((y,a):rest) = do
let ix = safeIndex bounds n y
let r = f a (elem x)
unsafeWrite arr ix r
if (ix == x)
then writeIORef ref rest >> return r
else loop rest
loop ass
forM_ [0..n] $ \ix -> unsafeWrite arr ix (elem ix)
unsafeFreeze arr
A challenge for the reader: use accumArrayR to implement linear-time depth-first-search of a graph.
Edit I should mention that the function isn't thread-safe as written. Turning the IORef into an MVar would fix it, but there might be better ways.
Not the most efficient, but...
accumrArray f x b l = accumArray (flip f) x b (reverse l)
I would argue that
accumrArray f x b l = accumArray (flip f) x b (reverse l)
is indeed the best solution (credits to sclv's answer).
Its supposed "inefficiency" comes from fact that foldr applies the function f from right to left.
However, since accumArray is strict, l can never be infinite, otherwise the program would be incorrect. It would never terminate.
Therefore, foldl (flip f) is just as good as a foldr.