I'm trying to parse a list using a pattern string that indicated types of values (annual and quarterly). I need to accumulate quarter numbers in the resulting output. So far I came up with this:
row = [100, 10, 40, 25, 25]
fmt = "aqqqq"
expected = [('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25)]
count :: Char -> String -> Int
count letter str = length $ filter (== letter) str
split :: String -> [a] -> [(Char, Int, a)]
split fmt row = [(freq, count freq (fmt' i), x)
| (freq, x, i) <- zip3 fmt row [0..]]
where fmt' i = take (i+1) fmt
-- split "aqqqq" [100, 10, 40, 25, 25]
-- [('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25)]
I imagine there should be something more readable and performant that this code, or even a terrific one liner.
I also experimented with expanding "aqqqq" into list of tuples [('a',1),('q',1),('q',2),('q',3),('q',4)] and later adding values; maybe this is a better way as I would need specify the format once for several rows.
If you already have a function expand to expand "aqqqq" into list of tuples, you can accomplish the rest with zipWith:
Prelude> zipWith (\(p, ix) x -> (p, ix, x)) (expand fmt) row
[('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25)]
The expand function produces tuples of the type Num t => (Char, t). I called the values inside that tuple p (for period) and ix (for index). Zipping that list of tuples with row also produces values, that I, in the lambda expression, simply called x.
The main problem here is how to convert the string, say "aqqqq" to the list of frequency of characters appear in the string. i.e. we want:
"aqqqq" => [1, 1, 2, 3, 4]
Once the list of frequency is constructed, we can use zip3 to product the expected list of tuples as:
[('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25)]
Obviously, we can't use map to product desired frequency list since the value need to be accumulated. To solve it, I recommend to use Data.Map in order to improve the computational complexity from O(n) to O(log n).
It is simple to count the frequency using insertWith as:
countFreq c m = insertWith (+) c 1 m
and get back the accumulated value using lookup as:
accumValue c m = fromMaybe 0 (Map.lookup c m) + 1
now, it is straight forward to build desired list as:
mkAccumList (c:cs) m = accumValue c m : mkAccumList cs (countFreq c m)
put all together:
import Data.Map as Map (empty, lookup, insertWith)
import Data.Maybe (fromMaybe)
countFreq c m = insertWith (+) c 1 m
accumValue c m = fromMaybe 0 (Map.lookup c m) + 1
split::String -> [a] -> [(Char, Int, a)]
split fmt row = zip3 fmt (mkAccumList fmt Map.empty) row
where mkAccumList (c:cs) m = accumValue c m : mkAccumList cs (countFreq c m)
mkAccumList [] _ = []
To work with infinite list:
take 8 $ split (cycle "aqqqq") (cycle [100, 10, 40, 25, 25])
gives
[('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25),('a',2,100),('q',5,10),
('q',6,40)]
Based on #Mark Seemann suggestion, here is a complete listing with a solution. I changed lambda to a named function for a bit more readability and introduced a type for row format.
count :: Char -> String -> Int
count letter str = length $ filter (== letter) str
type RowFormat = [Char]
expand :: RowFormat -> [(Char, Int)]
expand pat = [(c, count c (take (i+1) pat)) | (c, i) <- zip pat [0..]]
split' :: RowFormat -> [a] -> [(Char, Int, a)]
split' fmt values = zipWith merge (expand fmt) values
where merge (freq, period) value = (freq, period, value)
The result is as expected:
*Main> split' "aqqqq" [100, 10, 40, 25, 25]
[('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25)]
An afterthought - I still expand the format string each time I parse row, probably even currying parse = split' "aqqqq" will just delay computation.
Here is my try to make a dedicated reader function:
makeSplitter fmt = \values -> zipWith merge pos values
where
merge (freq, period) value = (freq, period, value)
pos = expand fmt
splitRow = makeSplitter "aqqqq"
a = splitRow [100, 10, 40, 25, 25]
a is expected result, same as above
[('a',1,100),('q',1,10),('q',2,40),('q',3,25),('q',4,25)]
Related
I'm interested in writing an efficient Haskell function triangularize :: [a] -> [[a]] that takes a (perhaps infinite) list and "triangularizes" it into a list of lists. For example, triangularize [1..19] should return
[[1, 3, 6, 10, 15]
,[2, 5, 9, 14]
,[4, 8, 13, 19]
,[7, 12, 18]
,[11, 17]
,[16]]
By efficient, I mean that I want it to run in O(n) time where n is the length of the list.
Note that this is quite easy to do in a language like Python, because appending to the end of a list (array) is a constant time operation. A very imperative Python function which accomplishes this is:
def triangularize(elements):
row_index = 0
column_index = 0
diagonal_array = []
for a in elements:
if row_index == len(diagonal_array):
diagonal_array.append([a])
else:
diagonal_array[row_index].append(a)
if row_index == 0:
(row_index, column_index) = (column_index + 1, 0)
else:
row_index -= 1
column_index += 1
return diagonal_array
This came up because I have been using Haskell to write some "tabl" sequences in the On-Line Encyclopedia of Integer Sequences (OEIS), and I want to be able to transform an ordinary (1-dimensional) sequence into a (2-dimensional) sequence of sequences in exactly this way.
Perhaps there's some clever (or not-so-clever) way to foldr over the input list, but I haven't been able to sort it out.
Make increasing size chunks:
chunks :: [a] -> [[a]]
chunks = go 0 where
go n [] = []
go n as = b : go (n+1) e where (b,e) = splitAt n as
Then just transpose twice:
diagonalize :: [a] -> [[a]]
diagonalize = transpose . transpose . chunks
Try it in ghci:
> diagonalize [1..19]
[[1,3,6,10,15],[2,5,9,14],[4,8,13,19],[7,12,18],[11,17],[16]]
This appears to be directly related to the set theory argument proving that the set of integer pairs are in one-to-one correspondence with the set of integers (denumerable). The argument involves a so-called Cantor pairing function.
So, out of curiosity, let's see if we can get a diagonalize function that way.
Define the infinite list of Cantor pairs recursively in Haskell:
auxCantorPairList :: (Integer, Integer) -> [(Integer, Integer)]
auxCantorPairList (x,y) =
let nextPair = if (x > 0) then (x-1,y+1) else (x+y+1, 0)
in (x,y) : auxCantorPairList nextPair
cantorPairList :: [(Integer, Integer)]
cantorPairList = auxCantorPairList (0,0)
And try that inside ghci:
λ> take 15 cantorPairList
[(0,0),(1,0),(0,1),(2,0),(1,1),(0,2),(3,0),(2,1),(1,2),(0,3),(4,0),(3,1),(2,2),(1,3),(0,4)]
λ>
We can number the pairs, and for example extract the numbers for those pairs which have a zero x coordinate:
λ>
λ> xs = [1..]
λ> take 5 $ map fst $ filter (\(n,(x,y)) -> (x==0)) $ zip xs cantorPairList
[1,3,6,10,15]
λ>
We recognize this is the top row from the OP's result in the text of the question.
Similarly for the next two rows:
λ>
λ> makeRow xs row = map fst $ filter (\(n,(x,y)) -> (x==row)) $ zip xs cantorPairList
λ> take 5 $ makeRow xs 1
[2,5,9,14,20]
λ>
λ> take 5 $ makeRow xs 2
[4,8,13,19,26]
λ>
From there, we can write our first draft of a diagonalize function:
λ>
λ> printAsLines xs = mapM_ (putStrLn . show) xs
λ> diagonalize xs = takeWhile (not . null) $ map (makeRow xs) [0..]
λ>
λ> printAsLines $ diagonalize [1..19]
[1,3,6,10,15]
[2,5,9,14]
[4,8,13,19]
[7,12,18]
[11,17]
[16]
λ>
EDIT: performance update
For a list of 1 million items, the runtime is 18 sec, and 145 seconds for 4 millions items. As mentioned by Redu, this seems like O(n√n) complexity.
Distributing the pairs among the various target sublists is inefficient, as most filter operations fail.
To improve performance, we can use a Data.Map structure for the target sublists.
{-# LANGUAGE ExplicitForAll #-}
{-# LANGUAGE ScopedTypeVariables #-}
import qualified Data.List as L
import qualified Data.Map as M
type MIL a = M.Map Integer [a]
buildCantorMap :: forall a. [a] -> MIL a
buildCantorMap xs =
let ts = zip xs cantorPairList -- triplets (a,(x,y))
m0 = (M.fromList [])::MIL a
redOp m (n,(x,y)) = let afn as = case as of
Nothing -> Just [n]
Just jas -> Just (n:jas)
in M.alter afn x m
m1r = L.foldl' redOp m0 ts
in
fmap reverse m1r
diagonalize :: [a] -> [[a]]
diagonalize xs = let cm = buildCantorMap xs
in map snd $ M.toAscList cm
With that second version, performance appears to be much better: 568 msec for the 1 million items list, 2669 msec for the 4 millions item list. So it is close to the O(n*Log(n)) complexity we could have hoped for.
It might be a good idea to craete a comb filter.
So what does comb filter do..? It's like splitAt but instead of splitting at a single index it sort of zips the given infinite list with the given comb to separate the items coressponding to True and False in the comb. Such that;
comb :: [Bool] -- yields [True,False,True,False,False,True,False,False,False,True...]
comb = iterate (False:) [True] >>= id
combWith :: [Bool] -> [a] -> ([a],[a])
combWith _ [] = ([],[])
combWith (c:cs) (x:xs) = let (f,s) = combWith cs xs
in if c then (x:f,s) else (f,x:s)
λ> combWith comb [1..19]
([1,3,6,10,15],[2,4,5,7,8,9,11,12,13,14,16,17,18,19])
Now all we need to do is to comb our infinite list and take the fst as the first row and carry on combing the snd with the same comb.
Lets do it;
diags :: [a] -> [[a]]
diags [] = []
diags xs = let (h,t) = combWith comb xs
in h : diags t
λ> diags [1..19]
[ [1,3,6,10,15]
, [2,5,9,14]
, [4,8,13,19]
, [7,12,18]
, [11,17]
, [16]
]
also seems to be lazy too :)
λ> take 5 . map (take 5) $ diags [1..]
[ [1,3,6,10,15]
, [2,5,9,14,20]
, [4,8,13,19,26]
, [7,12,18,25,33]
, [11,17,24,32,41]
]
I think the complexity could be like O(n√n) but i can not make sure. Any ideas..?
This is a followup to a recent question that wasn't asked clearly. The poster Aditi Jain's clarifications invalidate the answer somewhat that's already posted there, hence this new post.
The objective is to check whether there's no diagonally adjacent pair of elements in the nested lists which are negative of one another. The poster is new to Haskell programming.
The function signature is:
checkNegation :: [[Int]] -> Bool
Examples:
checkNegation [[1,2], [-2,3]] will return False:
[ [ 1 , 2], -- 2, -2 are diagonally adjacent
[-2 , 3] ]
checkNegation [[1,2], [3,-1]] will return False:
[ [ 1 , 2], -- 1, -1 are diagonally adjacent
[ 3 , -1] ]
checkNegation [[1,2], [-1,3]] will return True:
[ [ 1 , 2], -- no diagonally adjacent negatives
[-1 , 3] ]
checkNegation [[0,2,1], [3,1,-2], [3,-1,3]] will return False:
[ [ 0 , 2, 1], -- 2, -2 are diagonally adjacent
[ 3 , 1, -2],
[ 3 , -1, 3] ]
No coding attempts were provided in the original post.
(I'm not marking this as CW so as not to prevent the answerers getting reputation points for their efforts)
It's a little easier to do things if we take the matrix row-by-row. For the following, for instance:
[a,b,c],
[d,e,f],
We only want to compare the pairs:
[(a,e),(b,f),(b,d),(c,e)]
So the first step is to write a function which constructs that list from two adjacent rows.
diags xs ys = zip xs (drop 1 ys) ++ zip (drop 1 xs) ys
We're using drop 1 rather than tail because it doesn't error on the empty list, and the way I'm going to use this function later will use empty lists.
If we use this in a fold, then, it looks like the following:
anyDiags :: (a -> a -> Bool) -> [[a]] -> Bool
anyDiags p = fst . foldr f (False, [])
where
f xs (a, ys) = (a || or (zipWith p xs (drop 1 ys)) || or (zipWith p (drop 1 xs) ys), xs)
We've also made it generic over any relation.
Next we will want to figure out how to check if two numbers are negations of each other.
negEachOther x y = negate x == y
And then our check negation function is as follows:
checkNegation = anyDiags negEachOther
There are some fun things we can do with the anyDiags function here. There's actually a use of the writer monad hidden in it. With that, we can rewrite the fold to use that fact:
anyDiags :: (a -> a -> Bool) -> [[a]] -> Bool
anyDiags p = getAny . fst . foldrM f []
where
f xs ys = (Any (or (zipWith p xs (drop 1 ys)) || or (zipWith p (drop 1 xs) ys)), xs)
Though I'm not sure if it's any clearer.
Alternatively, we could do the whole thing using the zip xs (tail xs) trick:
anyDiags :: (a -> a -> Bool) -> [[a]] -> Bool
anyDiags p xs = or (zipWith f xs (tail xs))
where
f xs ys = or (zipWith p xs (drop 1 ys)) || or (zipWith p (drop 1 xs) ys)
We can use the diagonals utility from Data.Universe.Helpers package. Such that
λ> diagonals [[0,2,1], [3,1,-2], [3,-1,3]]
[[0],[3,2],[3,1,1],[-1,-2],[3]]
which is only half of what we need. So lets flip our 2D list and apply diagonals once more. Flipping a list would take reverse . transpose operation such that
λ> (reverse . transpose) [[0,2,1], [3,1,-2], [3,-1,3]]
[[1,-2,3],[2,1,-1],[0,3,3]]
now we can use diagonals on this flipped list to obtain the remaining diagonals.
λ> (diagonals . reverse . transpose) [[0,2,1], [3,1,-2], [3,-1,3]]
[[1],[2,-2],[0,1,3],[3,-1],[3]]
For all diagonals we need to concatenate them. So altogether we may do like;
allDiags = (++) <$> diagonals . reverse . transpose <*> diagonals
The rest is applying necessary boolean test.
import Data.List (transpose)
import Data.Universe.Helpers (diagonals)
checkNegation :: Num a => Eq a => [[a]] -> Bool
checkNegation = and . map (and . (zipWith (\x y -> 0 /= (x + y)) <*> tail)) . allDiags
where
allDiags = (++) <$> diagonals . reverse . transpose <*> diagonals
λ> checkNegation [[0,2,1], [3,1,-2], [3,-1,3]]
False
λ> checkNegation [[1,2], [-1,3]]
True
If you have a matrix like this and want to compare adjacent diagonal elements:
m = [[ 1, 2, 3, 4]
,[ 5, 6, 7, 8]
,[ 9,10,11,12]]
then you want to make two comparisons. First, you want to compare, element by element, the sub-matrix you get by dropping the first row and first column (left) with the sub-matrix you get by dropping the last row and last column (right):
[[ 6, 7, 8] [[ 1, 2, 3]
,[10,11,12] ,[ 5, 6, 7]]
Second, you want to compare, element by element, the sub-matrix you get by dropping the first row and last column (left) with the sub-matrix you get by dropping the last row and first column (right):
[[ 5, 6, 7] [[ 2, 3, 4]
,[ 9,10,11]] ,[ 6, 7, 8]]
We can construct these submatrices using init, tail, and maps of these:
m1 = tail (map tail m) -- drop first row and first column
m2 = init (map init m) -- drop last row and last column
m3 = tail (map init m) -- drop first row and last column
m4 = init (map tail m) -- drop last row and first column
giving:
λ> m1
[[6,7,8],[10,11,12]]
λ> m2
[[1,2,3],[5,6,7]]
λ> m3
[[5,6,7],[9,10,11]]
λ> m4
[[2,3,4],[6,7,8]]
How do we compare two sub-matrices? Well, we can write a two-dimensional version of zipWith to apply a binary function (a comparison, say) element by element to two matrices, the same way zipWith applies a binary function element by element to two lists:
zipZipWith :: (a -> b -> c) -> [[a]] -> [[b]] -> [[c]]
zipZipWith f m1 m2 = zipWith zipRow m1 m2
where zipRow r1 r2 = zipWith f r1 r2
This works by zipping the matrices together, row by row, using the zipRow helper function. For each pair of rows, zipRow zips the rows together, element by element, with the function f. This definition can be simplified to the slightly less clear:
zipZipWith f m1 m2 = zipWith (zipWith f) m1 m2
Anyway, to check if corresponding pairs of elements in two matrices are negatives of each other, we can use zipZipWith isNeg where:
isNeg :: (Num a, Eq a) => a -> a -> Bool
isNeg x y = x == -y
Then, to check if any of these pairs are negatives, we can use concat to change the matrix of booleans into a long list and or to check for any True values:
anyNegPairs :: (Num a, Eq a) => [[a]] -> [[a]] -> Bool
anyNegPairs ma mb = or . concat $ zipZipWith isNeg ma mb
Finally, then, a complete function to perform the comparison would be:
noDiagNeg :: (Num a, Eq a) => [[a]] -> Bool
noDiagNeg m = not (anyNegPairs m1 m2 || anyNegPairs m3 m4)
Since zipZipWith, like zipWith, ignores "extra" elements when comparing arguments of different sizes, it's not actually necessary to trim off the last column/row, so the sub-matrix definitions can be simplified by removing all the inits:
m1 = tail (map tail m)
m2 = m
m3 = tail m
m4 = map tail m
We could actually write m1 in terms of m4 to save double-calculating map tail m:
m1 = tail m4
but the compiler is smart enough to figure this out on its own.
So, a reasonable final solution would be:
noDiagNeg :: (Num a, Eq a) => [[a]] -> Bool
noDiagNeg m = not (anyNegPairs m1 m2 || anyNegPairs m3 m4)
where
m1 = tail (map tail m)
m2 = m
m3 = tail m
m4 = map tail m
anyNegPairs ma mb = or . concat $ zipZipWith isNeg ma mb
isNeg x y = x == -y
zipZipWith :: (a -> b -> c) -> [[a]] -> [[b]] -> [[c]]
zipZipWith f m1 m2 = zipWith (zipWith f) m1 m2
and it seems to work as desired on the test cases:
λ> noDiagNeg [[1,2],[-2,3]]
False
λ> noDiagNeg [[1,2],[3,-1]]
False
λ> noDiagNeg [[1,2],[-1,3]]
True
λ> noDiagNeg [[0,2,1],[3,1,-2],[3,-1,3]]
False
This is quite similar to #oisdk's solution, though this version might be easier to understand if you aren't too familiar with folds yet.
It fails on (certain) matrices with no elements:
λ> noDiagNeg []
*** Exception: Prelude.tail: empty list
λ> noDiagNeg [[],[]]
*** Exception: Prelude.tail: empty list
so you could use #oisdk's technique of replacing tail with drop 1, if this is a problem. (Actually, I might define tail' = drop 1 as a helper and replace all tail calls with tail' calls, since that would look a little nicer.)
First we pair up the rows: first with second, then second with third, then third with fourth, and so on.
Then, for each pair of rows, we consider all wedge-shaped triples of cells, like this:
--*---
-*-*--
So that the bottom-row cells are diagonally adjacent to the top-row ones.
Then we just check if any of the bottom ones are a negative of the top.
Except this has (literally) an edge case: beginnings and ends of the rows. If we do this wedge-shaped triple thing, we're going to miss the first and the last elements of the top row. To get around this, we first wrap the whole matrix in Just and then extend each row with Nothings on left and right:
[a,b,c] ==> [Nothing, Just a, Just b, Just c, Nothing]
[d,e,f] ==> [Nothing, Just d, Just e, Just f, Nothing]
Now we can safely iterate in triples and not miss anything.
checkNegation :: [[Int]] -> Bool
checkNegation matrix = any rowPairHasNegation rowPairs
where
extendedMatrix = map extendRow matrix
extendRow row = [Nothing] ++ map Just row ++ [Nothing]
rowPairs = extendedMatrix `zip` drop 1 extendedMatrix
rowPairHasNegation (row, nextRow) =
any cellTripleHasNegation $
drop 1 row `zip` nextRow `zip` drop 2 nextRow
cellTripleHasNegation ((x1y0, x0y1), x2y1) =
isNegation x1y0 x0y1 || isNegation x1y0 x2y1
isNegation (Just a) (Just b) = a == -b
isNegation _ _ = False
As far as I understand, this will result in iterating over the whole matrix exactly thrice - once as top row and twice as bottom row, meaning O(n*m)
I have a simple function (used for some problems of project Euler, in fact). It turns a list of digits into a decimal number.
fromDigits :: [Int] -> Integer
fromDigits [x] = toInteger x
fromDigits (x:xs) = (toInteger x) * 10 ^ length xs + fromDigits xs
I realized that the type [Int] is not ideal. fromDigits should be able to take other inputs like e.g. sequences, maybe even foldables ...
My first idea was to replace the above code with sort of a "fold with state". What is the correct (= minimal) Haskell-category for the above function?
First, folding is already about carrying some state around. Foldable is precisely what you're looking for, there is no need for State or other monads.
Second, it'd be more natural to have the base case defined on empty lists and then the case for non-empty lists. The way it is now, the function is undefined on empty lists (while it'd be perfectly valid). And notice that [x] is just a shorthand for x : [].
In the current form the function would be almost expressible using foldr. However within foldl the list or its parts aren't available, so you can't compute length xs. (Computing length xs at every step also makes the whole function unnecessarily O(n^2).) But this can be easily avoided, if you re-thing the procedure to consume the list the other way around. The new structure of the function could look like this:
fromDigits' :: [Int] -> Integer
fromDigits' = f 0
where
f s [] = s
f s (x:xs) = f (s + ...) xs
After that, try using foldl to express f and finally replace it with Foldable.foldl.
You should avoid the use of length and write your function using foldl (or foldl'):
fromDigits :: [Int] -> Integer
fromDigits ds = foldl (\s d -> s*10 + (fromIntegral d)) 0 ds
From this a generalization to any Foldable should be clear.
A better way to solve this is to build up a list of your powers of 10. This is quite simple using iterate:
powersOf :: Num a => a -> [a]
powersOf n = iterate (*n) 1
Then you just need to multiply these powers of 10 by their respective values in the list of digits. This is easily accomplished with zipWith (*), but you have to make sure it's in the right order first. This basically just means that you should re-order your digits so that they're in descending order of magnitude instead of ascending:
zipWith (*) (powersOf 10) $ reverse xs
But we want it to return an Integer, not Int, so let's through a map fromIntegral in there
zipWith (*) (powersOf 10) $ map fromIntegral $ reverse xs
And all that's left is to sum them up
fromDigits :: [Int] -> Integer
fromDigits xs = sum $ zipWith (*) (powersOf 10) $ map fromIntegral $ reverse xs
Or for the point-free fans
fromDigits = sum . zipWith (*) (powersOf 10) . map fromIntegral . reverse
Now, you can also use a fold, which is basically just a pure for loop where the function is your loop body, the initial value is, well, the initial state, and the list you provide it is the values you're looping over. In this case, your state is a sum and what power you're on. We could make our own data type to represent this, or we could just use a tuple with the first element being the current total and the second element being the current power:
fromDigits xs = fst $ foldr go (0, 1) xs
where
go digit (s, power) = (s + digit * power, power * 10)
This is roughly equivalent to the Python code
def fromDigits(digits):
def go(digit, acc):
s, power = acc
return (s + digit * power, power * 10)
state = (0, 1)
for digit in digits:
state = go(digit, state)
return state[0]
Such a simple function can carry all its state in its bare arguments. Carry around an accumulator argument, and the operation becomes trivial.
fromDigits :: [Int] -> Integer
fromDigits xs = fromDigitsA xs 0 # 0 is the current accumulator value
fromDigitsA [] acc = acc
fromDigitsA (x:xs) acc = fromDigitsA xs (acc * 10 + toInteger x)
If you're really determined to use a right fold for this, you can combine calculating length xs with the calculation like this (taking the liberty of defining fromDigits [] = 0):
fromDigits xn = let (x, _) = fromDigits' xn in x where
fromDigits' [] = (0, 0)
fromDigits' (x:xn) = (toInteger x * 10 ^ l + y, l + 1) where
(y, l) = fromDigits' xn
Now it should be obvious that this is equivalent to
fromDigits xn = fst $ foldr (\ x (y, l) -> (toInteger x * 10^l + y, l + 1)) (0, 0) xn
The pattern of adding an extra component or result to your accumulator, and discarding it once the fold returns, is a very general one when you're re-writing recursive functions using folds.
Having said that, a foldr with a function that is always strict in its second parameter is a really, really bad idea (excessive stack usage, maybe a stack overflow on long lists) and you really should write fromDigits as a foldl as some of the other answers have suggested.
If you want to "fold with state", probably Traversable is the abstraction you're looking for. One of the methods defined in Traversable class is
traverse :: Applicative f => (a -> f b) -> t a -> f (t b)
Basically, traverse takes a "stateful function" of type a -> f b and applies it to every function in the container t a, resulting in a container f (t b). Here, f can be State, and you can use traverse with function of type Int -> State Integer (). It would build an useless data structure (list of units in your case), but you can just discard it. Here's a solution to your problem using Traversable:
import Control.Monad.State
import Data.Traversable
sumDigits :: Traversable t => t Int -> Integer
sumDigits cont = snd $ runState (traverse action cont) 0
where action x = modify ((+ (fromIntegral x)) . (* 10))
test1 = sumDigits [1, 4, 5, 6]
However, if you really don't like building discarded data structure, you can just use Foldable with somewhat tricky Monoid implementation: store not only computed result, but also 10^n, where n is count of digits converted to this value. This additional information gives you an ability to combine two values:
import Data.Foldable
import Data.Monoid
data Digits = Digits
{ value :: Integer
, power :: Integer
}
instance Monoid Digits where
mempty = Digits 0 1
(Digits d1 p1) `mappend` (Digits d2 p2) =
Digits (d1 * p2 + d2) (p1 * p2)
sumDigitsF :: Foldable f => f Int -> Integer
sumDigitsF cont = value $ foldMap (\x -> Digits (fromIntegral x) 10) cont
test2 = sumDigitsF [0, 4, 5, 0, 3]
I'd stick with first implementation. Although it builds unnecessary data structure, it's shorter and simpler to understand (as far as a reader understands Traversable).
I want to sum the squares of the even numbers from a list. I try this but show an error.
sumaDeCuadrados :: [Int] -> Int
sumaDeCuadrados (x:xs) = sumaListAux (map f l) 0
where l = filter even (x:xs)
f = x * x
sumaDeCuadrados _ = 0
and sumaListAux is a function defined as ..
sumaListAux :: [Int] -> Int -> Int
sumaListAux [] r = r
sumaListAux (x:xs) r = x + sumaListAux xs r
sum the squares of the even numbers from a list.
Haskell is a declarative language in some ways, so you can just declare what these things mean.
-- declare a list
> let list = [1..10]
-- declare what the even elements of a lsit are
> let evens xs = filter even xs
-- declare what the squares of a list are
> let squares xs = map (^2) xs
and the sum is already there, sum. So now your sentence:
sum the squares of the even numbers
can be transposed to:
> sum . squares . evens $ list
220
The actual problem is, map expects the first argument to be a function, which accepts an integer and returning an integer, but you are passing it an integer. That is why you are getting an error message like this
Couldn't match expected type `Int -> Int' with actual type `Int'
In the first argument of `map', namely `f'
In the first argument of `sumaListAux', namely `(map f l)'
In the expression: sumaListAux (map f l) 0
So, you need to define f as a separate function, so that map can apply that function to l. I would recommend naming the function with something appropriate, like squarer
squarer :: Int -> Int
squarer x = x * x
sumaDeCuadrados xs = sumaListAux (map squarer (filter even xs)) 0
And then you can call it like this
main = print $ sumaDeCuadrados [1, 2, 3, 4, 5]
-- 20
Building on the answers above, it's possible to do this entirely using higher order functions.
sumEvenSquares :: (Num a) => [a] -> a
sumEvenSquares xs = sum(map(^2)(filter even xs))
In this case, you're able to filter the list using the even predicate, and map the function (^2) onto it. From this returned list, you're then able to sum it.
I'm looking for the best way to partition a list (or seq) so that groups have a given size.
for ex. let's say I want to group with size 2 (this could be any other number though):
let xs = [(a,b,c); (a,b,d); (y,z,y); (w,y,z); (n,y,z)]
let grouped = partitionBySize 2 input
// => [[(a,b,c);(a,b,d)]; [(y,z,y);(w,y,z)]; [(n,y,z)]]
The obvious way to implement partitionBySize would be by adding the position to every tuple in the input list so that it becomes
[(0,a,b,c), (1,a,b,d), (2,y,z,y), (3,w,y,z), (4,n,y,z)]
and then use GroupBy with
xs |> Seq.ofList |> Seq.GroupBy (function | (i,_,_,_) -> i - (i % n))
However this solution doesn't look very elegant to me.
Is there a better way to implement this function (maybe with a built-in function)?
This seems to be a repeating pattern that's not captured by any function in the F# core library. When solving similar problems earlier, I defined a function Seq.groupWhen (see F# snippets) that turns a sequence into groups. A new group is started when the predicate holds.
You could solve the problem using Seq.groupWhen similarly to Seq.group (by starting a new group at even index). Unlike with Seq.group, this is efficient, because Seq.groupWhen iterates over the input sequence just once:
[3;3;2;4;1;2;8]
|> Seq.mapi (fun i v -> i, v) // Add indices to the values (as first tuple element)
|> Seq.groupWhen (fun (i, v) -> i%2 = 0) // Start new group after every 2nd element
|> Seq.map (Seq.map snd) // Remove indices from the values
Implementing the function directly using recursion is probably easier - the solution from John does exactly what you need - but if you wanted to see a more general approach then Seq.groupWhen may be interesting.
List.chunkBySize (hat tip: Scott Wlaschin) is now available and does exactly what you're talking about. It appears to be new with F# 4.0.
let grouped = [1..10] |> List.chunkBySize 3
// val grouped : int list list =
// [[1; 2; 3]; [4; 5; 6]; [7; 8; 9]; [10]]
Seq.chunkBySize and Array.chunkBySize are also now available.
Here's a tail-recursive function that traverses the list once.
let chunksOf n items =
let rec loop i acc items =
seq {
match i, items, acc with
//exit if chunk size is zero or input list is empty
| _, [], [] | 0, _, [] -> ()
//counter=0 so yield group and continue looping
| 0, _, _::_ -> yield List.rev acc; yield! loop n [] items
//decrement counter, add head to group, and loop through tail
| _, h::t, _ -> yield! loop (i-1) (h::acc) t
//reached the end of input list, yield accumulated elements
//handles items.Length % n <> 0
| _, [], _ -> yield List.rev acc
}
loop n [] items
Usage
[1; 2; 3; 4; 5]
|> chunksOf 2
|> Seq.toList //[[1; 2]; [3; 4]; [5]]
I like the elegance of Tomas' approach, but I benchmarked both our functions using an input list of 10 million elements. This one clocked in at 9 secs vs 22 for his. Of course, as he admitted, the most efficient method would probably involve arrays/loops.
What about a recursive approach? - only requires a single pass
let rec partitionBySize length inp dummy =
match inp with
|h::t ->
if dummy |> List.length < length then
partitionBySize length t (h::dummy)
else dummy::(partitionBySize length t (h::[]))
|[] -> dummy::[]
Then invoke it with partitionBySize 2 xs []
let partitionBySize size xs =
let sq = ref (seq xs)
seq {
while (Seq.length !sq >= size) do
yield Seq.take size !sq
sq := Seq.skip size !sq
if not (Seq.isEmpty !sq) then yield !sq
}
// result to list, if you want
|> Seq.map (Seq.toList)
|> Seq.toList
UPDATE
let partitionBySize size (sq:seq<_>) =
seq {
let e = sq.GetEnumerator()
let empty = ref true;
while !empty do
yield seq { for i = 1 to size do
empty := e.MoveNext()
if !empty then yield e.Current
}
}
array slice version:
let partitionBySize size xs =
let xa = Array.ofList xs
let len = xa.Length
[
for i in 0..size..(len-1) do
yield ( if i + size >= len then xa.[i..] else xa.[i..(i+size-1)] ) |> Array.toList
]
Well, I was late for the party. The code below is a tail-recursive version using high-order functions on List:
let partitionBySize size xs =
let i = size - (List.length xs - 1) % size
let xss, _, _ =
List.foldBack( fun x (acc, ls, j) ->
if j = size then ((x::ls)::acc, [], 1)
else (acc, x::ls, j+1)
) xs ([], [], i)
xss
I did the same benchmark as Daniel did. This function is efficient while it is 2x faster than his approach on my machine. I also compared it with an array/loop version, they are comparable in terms of performance.
Moreover, unlike John's answer, this version preserves order of elements in inner lists.