I have defined data type for Binary numbers as follows
data Bin = Nil | O Bin | I Bin
deriving (Show, Eq)
i want to define a function reverse :: Bin -> Bin so that when i give input like
reverse (I (O (I (I Nil)))) i should get the outut
I (I (O (I Nil))) that means reversed as input, any body please give me hint how i can do this ?
Why are you doing this this way? Why not something like this:
data Bit = I | O
newtype Bin = List Bit
Then you could just use the Prelude's reverse operation directly...
Edit
A simple substitution from the Prelude's function:
reverse x = rev x []
where
rev [] a = a
rev (x:xs) a = rev xs (x:a)
yields:
reverse x = rev x Nil
where
rev Nil a = a
rev (I xs) a = rev xs (I a)
rev (O xs) a = rev xs (O a)
The thing is, your type is very similar to the list type:
data List a = a : (List a) | []
so the logic for the List routines applies directly to your type.
data Bin = Nil | O Bin | I Bin deriving (Show, Eq)
reverse :: Bin -> Bin
reverse x = rev Nil x
where
rev a Nil = a
rev a ( O b ) = rev ( O a ) b
rev a ( I b ) = rev ( I a ) b
binToList Nil = []
binToList (O a) = False : binToList a
binToList (I a) = True : binToList a
listToBin [] = Nil
listToBin (False : xs) = O (listToBin xs)
listToBin (True : xs) = I (listToBin xs)
reverseBin = listToBin . reverse . binToList
GHC's List module defines the reverse function on lists like this:
reverse l = rev l []
where
rev [] a = a
rev (x:xs) a = rev xs (x:a)
The helper function rev uses its second element as an accumulator that stores the reversed part up to the current position. In each step the first element of the remaining input list is added to head of the accumulator that is passed to the recursive function call.
The same principle can be applied to your binary number type to reverse the order of the digits.
Seems odd that you're defining both a list type, and a type for bits. I think I'd reuse the base libraries list type [] and just set the elements to be your bit type, as Aidan shows above.
this is a possible solution:
reverseBin :: Bin -> Bin
reverseBin b = revBin b Nil
where revBin Nil acc = acc
revBin (I b) acc = revBin b (I acc)
revBin (O b) acc = revBin b (O acc)
Related
I am following the LH tutorial and am stuck at the excersise to refine the type of the filter function in a way that, if called with a list with unique elements, the output is also a unique list with unique elements.
This is the code I am working with:
import Data.Set hiding (insert, partition, filter, split, elems)
{-# measure elts #-}
elts :: (Ord a) => [a] -> Set a
elts [] = empty
elts (x:xs) = singleton x `union` elts xs
{-# measure unique #-}
unique :: (Ord a) => [a] -> Bool
unique [] = True
unique (x:xs) = unique xs && not (member x (elts xs))
{-# filter' :: (a -> Bool) -> xs:[a] -> { l:[a] | unique xs => unique l } #-}
filter' _ [] = []
filter' f (x:xs)
| f x = x : xs'
| otherwise = xs'
where
xs' = filter' f xs
however, when let LH prove it the following error is thrown by LH:
filter.hs:16:17-23: Error: Liquid Type Mismatch
16 | | f x = x : xs'
^^^^^^^
Inferred type
VV : {v : [a] | tail v == xs'
&& head v == x
&& listElts v == Set_cup (Set_sng x) (listElts xs')
&& len v == 1 + len xs'
&& Main.elts v == Set_cup (Set_sng x) (Main.elts xs')
&& (Main.unique v <=> Main.unique xs'
&& not (Set_mem x (Main.elts xs')))
&& len v >= 0}
not a subtype of Required type
VV : {VV : [a] | Main.unique ?a => Main.unique VV}
In Context
xs : {v : [a] | len v >= 0}
xs' : {v : [a] | (Main.unique xs => Main.unique v)
&& len v >= 0}
x : a
?a : {?a : [a] | len ?a >= 0}
I have already tried to specify it differently, using an if in the refinement and by using different refinement types, but none of it seems to work...
can you please point me in the right direction? I am still having a hard time understanding the error messages. As I understand it, the inferred type contains the information that it is uniqe if xs' is unique, so in my opinion, this should be a subtype of the required one.
sorry for the delay, I just saw this! (I tend to monitor the slack channel more closely.) The error message basically says that LH cannot prove that the output list x:xs' will indeed be unique. Specifically, LH has no way to know that x is not already an element of xs'.
Now, why is x not an element of xs'? Because
(1) x is not an element of xs AND
(2) xs' is a subset of the values of xs
LH knows the first property if the input list is unique.
But it does not know the second. So if you add that to the output
of filter that would help.
[This is an excellent question by the way; I will update the tutorial with a hint]
I have a problem making Backtracking on Haskell, I know how to do recursive functions but I get troubles when I try to get multiple solutions or the best one (backtracking).
There's a list with some strings, then I need to get the solutions to get from a string to another one changing one letter from the string, I will get the list, the first string and the last one. If there is solution return the count of steps that it did, if there is not solution it returns -1. here's an example:
wordF ["spice","stick","smice","stock","slice","slick","stock"] "spice" "stock"
Then I have my list and I need to start with "spice" and get to "stock"
and the best solution is ["spice","slice","slick","stick","stock"] with four steps to get from "spice" to "stock". then it return 4.
Another solution is ["spice","smice","slice","slick","stick","stock"] with five steps to get to "stock" then it return `5. But this is a wrong solution because there's another one that's better with lesser steps than this one.
I'm having troubles making a backtracking to get the best solution, because I don't know how to make that my code search another solutions and just not one..
Here's a code that i tried to make but i get some errors, btw i dont know if my way to "make" backtracking is good or if there are some mistakes that im not seeing..
wordF :: [String] -> String -> String -> (String, String, Int)
wordF [] a b = (a, b, -1)
wordF list a b | (notElem a list || notElem b list) = (a, b, -1)
| otherwise = (a, b, (wordF2 list a b [a] 0 (length list)))
wordF2 :: [String] -> String -> String -> [String] -> Int -> Int -> Int
wordF2 list a b list_aux cont maxi | (cont==maxi) = 1000
| (a==b) = length list_aux
| (a/=b) && (cont<maxi) && notElemFound && (checkin /= "ThisWRONG") && (wording1<=wording2) = wording1
| (a/=b) && (cont<maxi) && notElemFound && (checkin /= "ThisWRONG") && (wording1>wording2) = wording2
| (a/=b) && (checkin == "ThisWRONG") = wordF2 list a b list_aux (cont+1) maxi
where
checkin = (check_word2 a (list!!cont) (list!!cont) 0)
wording1 = (wordF2 list checkin b (list_aux++[checkin]) 0 maxi)
wording2 = (wordF2 list checkin b (list_aux++[checkin]) 1 maxi)
notElemFound = ((any (==(list!!cont)) list_aux) == False)
check_word2 :: String -> String -> String -> Int -> String
check_word2 word1 word2 word3 dif | (dif > 1) = "ThisWRONG"
| ((length word1 == 1) && (length word2 == 1) && (head word1 == head word2)) = word3
| ((length word1 == 1) && (length word2 == 1) && (head word1 /= head word2) && (dif<1)) = word3
| ((head word1) == (head word2)) = check_word2 (tail word1) (tail word2) word3 dif
| otherwise = check_word2 (tail word1) (tail word2) word3 (dif+1)
My first function wordF2 get the list, the start, the end, an auxiliary list to get the current solution with the first element that always will be there ([a]), a counter with 0, and the max size of the counter (length list)..
and the second function check_word2 it checks if a word can pass to another word, like "spice" to "slice" if it cant like "spice" to "spoca" it returns "ThisWRONG".
This solution gets an error of pattern match failure
Program error: pattern match failure: wordF2 ["slice","slick"] "slice" "slick" ["slice"] 0 1
I was trying with little cases and nothing, and I'm restricting that i get a wrong position of the list with the count and the max.
Or may be I dont know how to implement backtracking on haskell to get multiple solutions, the best solution, etc..
UPDATE: I did a solution but its not backtracking
wordF :: [String] -> String -> String -> (String, String, Int)
wordF [] a b = (a, b, -1)
wordF list a b | (notElem a list || notElem b list) = (a, b, -1)
| otherwise = (a, b, (wordF1 list a b))
wordF1 :: [String] -> String -> String -> Int
wordF1 list a b | ((map length (wordF2 (subconjuntos2 (subconjuntos list) a b))) == []) = -1
| (calculo > 0) = calculo
| otherwise = -1
where
calculo = (minimum (map length (wordF2 (subconjuntos2 (subconjuntos list) a b))))-1
wordF2 :: [[String]] -> [[String]]
wordF2 [[]] = []
wordF2 (x:xs) | ((length xs == 1) && ((check_word x) == True) && ((check_word (head xs)) == True)) = x:xs
| ((length xs == 1) && ((check_word x) == False) && ((check_word (head xs)) == True)) = xs
| ((length xs == 1) && ((check_word x) == True) && ((check_word (head xs)) == False)) = [x]
| ((length xs == 1) && ((check_word x) == False) && ((check_word (head xs)) == False)) = []
| ((check_word x) == True) = x:wordF2 xs
| ((check_word x) == False ) = wordF2 xs
check_word :: [String] -> Bool
check_word [] = False
check_word (x:xs) | ((length xs == 1) && ((check_word2 x (head xs) 0) == True)) = True
| ((length xs >1) && ((check_word2 x (head xs) 0) == True)) = True && (check_word xs)
| otherwise = False
check_word2 :: String -> String -> Int -> Bool
check_word2 word1 word2 dif | (dif > 1) = False
| ((length word1 == 1) && (length word2 == 1) && (head word1 == head word2)) = True
| ((length word1 == 1) && (length word2 == 1) && (head word1 /= head word2) && (dif<1)) = True
| ((head word1) == (head word2)) = check_word2 (tail word1) (tail word2) dif
| otherwise = check_word2 (tail word1) (tail word2) (dif+1)
subconjuntos2 :: [[String]] -> String -> String -> [[String]]
subconjuntos2 [] a b = []
subconjuntos2 (x:xs) a b | (length x <= 1) = subconjuntos2 xs a b
| ((head x == a) && (last x == b)) = (x:subconjuntos2 xs a b)
| ((head x /= a) || (last x /= b)) = (subconjuntos2 xs a b)
subconjuntos :: [a] -> [[a]]
subconjuntos [] = [[]]
subconjuntos (x:xs) = [x:ys | ys <- sub] ++ sub
where sub = subconjuntos xs
Mmm may be its inefficient but at least it does the solution..
i search all posible solutions, i compare head == "slice" and last == "stock", then i filter the ones that are solution and print the shorter one,
thanks and if you guys have any suggest say it :)
Not thoroughly tested, but this hopefully will help:
import Data.Function (on)
import Data.List (minimumBy, delete)
import Control.Monad (guard)
type Word = String
type Path = [String]
wordF :: [Word] -> Word -> Word -> Path
wordF words start end =
start : minimumBy (compare `on` length) (generatePaths words start end)
-- Use the list monad to do the nondeterminism and backtracking.
-- Returns a list of all paths that lead from `start` to `end`
-- in steps that `differByOne`.
generatePaths :: [Word] -> Word -> Word -> [Path]
generatePaths words start end = do
-- Choose one of the words, nondeterministically
word <- words
-- If the word doesn't `differByOne` from `start`, reject the choice
-- and backtrack.
guard $ differsByOne word start
if word == end
then return [word]
else do
next <- generatePaths (delete word words) word end
return $ word : next
differsByOne :: Word -> Word -> Bool
differsByOne "" "" = False
differsByOne (a:as) (b:bs)
| a == b = differsByOne as bs
| otherwise = as == bs
Example run:
>>> wordF ["spice","stick","smice","stock","slice","slick","stock"] "spice" "stock"
["spice","slice","slick","stick","stock"]
The list monad in Haskell is commonly described as a form of nondeterministic, backtracking computation. What the code above is doing is allowing the list monad to take on the responsibility of generating alternatives, testing whether they satisfy criteria, and backtracking on failure to the most recent choice point. The bind of the list monad, e.g. word <- words, means "nondeterministically pick one of the words. guard means "if the choices so far don't satisfy this condition, backtrack and make a different choice. The result of a list monad computation is the list of all the results that stem from choices that did not violate any guards.
If this looks like list comprehensions, well, list comprehensions are the same thing as the list monad—I chose to express it with the monad instead of comprehensions.
There have been several articles published recently on classic brute-force search problems.
Mark Dominus published a simple example of using lists for a simple exhaustive search.
Justin Le followed up with a small modification to the previous article that simplified tracking the current state of the search.
I followed up with a further modification that allowed measuring the gains from early rejection of part of the search tree.
Note that the code in my article is quite slow because it's measuring the amount of work done as well as doing it. My article has good examples for how to quickly reject parts of the search tree, but it should be considered only an illustration - not production code.
A brute force approach using recursion:
import Data.List (filter, (\\), reverse, delete, sortBy)
import Data.Ord (comparing)
neighbour :: String -> String -> Bool
neighbour word = (1 ==) . length . (\\ word)
process :: String -> String -> [String] -> [(Int, [String])]
process start end dict =
let
loop :: String -> String -> [String] -> [String] -> [(Int,[String])] -> [(Int,[String])]
loop start end dict path results =
case next of
[] -> results
xs ->
if elem end xs
then (length solution, solution) : results
else results ++ branches xs
where
next = filter (neighbour start) dict'
dict' = delete start dict
path' = start : path
branches xs = [a | x <- xs, a <- loop x end dict' path' results]
solution = reverse (end : path')
in
loop start end dict [] []
shortestSolution :: Maybe Int
shortestSolution = shortest solutions
where
solutions = process start end dict
shortest s =
case s of
[] -> Nothing
xs -> Just $ fst $ head $ sortBy (comparing fst) xs
start = "spice"
end = "stock"
dict = ["spice","stick","smice","slice","slick","stock"]
Notes:
This code computes all possibles solutions (process) and select the shortest one (shortestSolution), as Carl said, you might want to prune parts of the search tree for better performance.
Using a Maybe instead of returning -1 when a function can fail to return results is preferred.
Another way using a tree with breadth-first search:
import Data.Tree
import Data.List( filter, (\\), delete )
import Data.Maybe
node :: String -> [String] -> Tree String
node label dict = Node{ rootLabel = label, subForest = branches label (delete label dict) }
branches :: String -> [String] -> [Tree String]
branches start dict = map (flip node dict) (filter (neighbour start) dict)
neighbour :: String -> String -> Bool
neighbour word = (1 ==) . length . (\\ word)
-- breadth first traversal
shortestBF tree end = find [tree] end 0
where
find ts end depth
| null ts = Nothing
| elem end (map rootLabel ts) = Just depth
| otherwise = find (concat (map subForest ts)) end (depth+1)
result = shortestBF tree end
tree :: Tree String
tree = node start dict
start = "spice"
end = "stock"
dict = ["spice","stick","smice","slice","slick","stock"]
So I'm trying to define a function in Haskell that if given an integer and a list of integers will give a 'true' or 'false' whether the integer occurs only once or not.
So far I've got:
let once :: Eq a => a -> [a] -> Bool; once x l =
But I haven't finished writing the code yet. I'm very new to Haskell as you may be able to tell.
Start off by using pattern matching:
once x [] =
once x (y:ys) =
This won't give you a good program immediately, but it will lead you in the right direction.
Here's a solution that doesn't use pattern matching explicitly. Instead, it keeps track of a Bool which represents if a occurance has already been found.
As others have pointed out, this is probably a homework problem, so I've intentionally left the then and else branches blank. I encourage user3482534 to experiment with this code and fill them in themselves.
once :: Eq a => a -> [a] -> Bool
once a = foldr f False
where f x b = if x == a then ??? else ???
Edit: The naive implementation I was originally thinking of was:
once :: Eq a => a -> [a] -> Bool
once a = foldr f False
where f x b = if x == a then b /= True else b
but this is incorrect as,
λ. once 'x' "xxx"
True
which should, of course, be False as 'x' occurs more than exactly once.
However, to show that it is possible to write once using a fold, here's a revised version that uses a custom monoid to keep track of how many times the element has occured:
import Data.List
import Data.Foldable
import Data.Monoid
data Occur = Zero | Once | Many
deriving Eq
instance Monoid Occur where
mempty = Zero
Zero `mappend` x = x
x `mappend` Zero = x
_ `mappend` _ = Many
once :: Eq a => a -> [a] -> Bool
once a = (==) Once . foldMap f
where f x = if x == a then Once else Zero
main = do
let xss = inits "xxxxx"
print $ map (once 'x') xss
which prints
[False,True,False,False,False]
as expected.
The structure of once is similar, but not identical, to the original.
I'll answer this as if it were a homework question since it looks like one.
Read about pattern matching in function declarations, especially when they give an example of processing a list. You'll use tools from Data.List later, but probably your professor is teaching about pattern matching.
Think about a function that maps values to a 1 or 0 depending on whethere there is a match ...
match :: a -> [a] -> [Int]
match x xs = map -- fill in the thing here such that
-- match 3 [1,2,3,4,5] == [0,0,1,0,0]
Note that there is the sum function that takes a list of numbers and returns the sum of the numbers in the list. So to count the matches a function can take the match function and return the counts.
countN :: a -> [a] -> Int
countN x xs = ? $ match x xs
And finally a function that exploits the countN function to check for a count of only 1. (==1).
Hope you can figure out the rest ...
You can filter the list and then check the length of the resulting list. If length == 1, you have only one occurrence of the given Integer:
once :: Eq a => a -> [a] -> Bool
once x = (== 1) . length . filter (== x)
For counting generally, with import Data.List (foldl'), pointfree
count pred = foldl' (\ n x -> if pred x then n + 1 else n) 0
applicable like
count (< 10) [1 .. 10] == 9
count (== 'l') "Hello" == 2
gives
once pred xs = count pred xs == 1
Efficient O(n) short-circuit predicated form, testing whether the predicate is satisfied exactly once:
once :: (a -> Bool) -> [a] -> Bool
once pred list = one list 0
where
one [] 1 = True
one [] _ = False
one _ 2 = False
one (x : xs) n | pred x = one xs (n + 1)
| otherwise = one xs n
Or, using any:
none pred = not . any pred
once :: (a -> Bool) -> [a] -> Bool
once _ [] = False
once pred (x : xs) | pred x = none pred xs
| otherwise = one pred xs
gives
elemOnce y = once (== y)
which
elemOnce 47 [1,1,2] == False
elemOnce 2 [1,1,2] == True
elemOnce 81 [81,81,2] == False
I'm stuck with my homework task, somebody help, please..
Here is the task:
Find all possible partitions of string into words of some dictionary
And here is how I'm trying to do it:
I use dynamical programming concept to fill matrix and then I'm stuck with how to retrieve data from it
-- Task5_2
retrieve :: [[Int]] -> [String] -> Int -> Int -> Int -> [[String]]
retrieve matrix dict i j size
| i >= size || j >= size = []
| index /= 0 = [(dict !! index)]:(retrieve matrix dict (i + sizeOfWord) (i + sizeOfWord) size) ++ retrieve matrix dict i (next matrix i j) size
where index = (matrix !! i !! j) - 1; sizeOfWord = length (dict !! index)
next matrix i j
| j >= (length matrix) = j
| matrix !! i !! j > 0 = j
| otherwise = next matrix i (j + 1)
getPartitionMatrix :: String -> [String] -> [[Int]]
getPartitionMatrix text dict = [[ indiceOfWord (getWord text i j) dict 1 | j <- [1..(length text)]] | i <- [1..(length text)]]
--------------------------
getWord :: String -> Int -> Int -> String
getWord text from to = map fst $ filter (\a -> (snd a) >= from && (snd a) <= to) $ zip text [1..]
indiceOfWord :: String -> [String] -> Int -> Int
indiceOfWord _ [] _ = 0
indiceOfWord word (x:xs) n
| word == x = n
| otherwise = indiceOfWord word xs (n + 1)
-- TESTS
dictionary = ["la", "a", "laa", "l"]
string = "laa"
matr = getPartitionMatrix string dictionary
test = retrieve matr dictionary 0 0 (length string)
Here is a code that do what you ask for. It doesn't work exactly like your solution but should work as fast if (and only if) both our dictionary lookup were improved to use tries as would be reasonable. As it is I think it may be a bit faster than your solution :
module Partitions (partitions) where
import Data.Array
import Data.List
data Branches a = Empty | B [([a],Branches a)] deriving (Show)
isEmpty Empty = True
isEmpty _ = False
flatten :: Branches a -> [ [ [a] ] ]
flatten Empty = []
flatten (B []) = [[]]
flatten (B ps) = concatMap (\(word, bs) -> ...) ps
type Dictionary a = [[a]]
partitions :: (Ord a) => Dictionary a -> [a] -> [ [ [a] ] ]
partitions dict xs = flatten (parts ! 0)
where
parts = listArray (0,length xs) $ zipWith (\i ys -> starting i ys) [0..] (tails xs)
starting _ [] = B []
starting i ys
| null words = ...
| otherwise = ...
where
words = filter (`isPrefixOf` ys) $ dict
go word = (word, parts ! (i + length word))
It works like this : At each position of the string, it search all possible words starting from there in the dictionary and evaluates to a Branches, that is either a dead-end (Empty) or a list of pairs of a word and all possible continuations after it, discarding those words that can't be continued.
Dynamic programming enter the picture to record every possibilities starting from a given index in a lazy array. Note that the knot is tied : we compute parts by using starting, which uses parts to lookup which continuations are possible from a given index. This only works because we only lookup indices after the one starting is computing and starting don't use parts for the last index.
To retrieve the list of partitions from this Branches datatype is analogous to the listing of all path in a tree.
EDIT : I removed some crucial parts of the solution in order to let the questioner search for himself. Though that shouldn't be too hard to complete with some thinking. I'll probably put them back with a somewhat cleaned up version later.
I'm trying to complete the last part of my Haskell homework and I'm stuck, my code so far:
data Entry = Entry (String, String)
class Lexico a where
(<!), (=!), (>!) :: a -> a -> Bool
instance Lexico Entry where
Entry (a,_) <! Entry (b,_) = a < b
Entry (a,_) =! Entry (b,_) = a == b
Entry (a,_) >! Entry (b,_) = a > b
entries :: [(String, String)]
entries = [("saves", "en vaut"), ("time", "temps"), ("in", "<`a>"),
("{", "{"), ("A", "Un"), ("}", "}"), ("stitch", "point"),
("nine.", "cent."), ("Zazie", "Zazie")]
build :: (String, String) -> Entry
build (a, b) = Entry (a, b)
diction :: [Entry]
diction = quiksrt (map build entries)
size :: [a] -> Integer
size [] = 0
size (x:xs) = 1+ size xs
quiksrt :: Lexico a => [a] -> [a]
quiksrt [] = []
quiksrt (x:xs)
|(size [y|y <- xs, y =! x]) > 0 = error "Duplicates not allowed."
|otherwise = quiksrt [y|y <- xs, y <! x]++ [x] ++ quiksrt [y|y <- xs, y >! x]
english :: String
english = "A stitch in time save nine."
show :: Entry -> String
show (Entry (a, b)) = "(" ++ Prelude.show a ++ ", " ++ Prelude.show b ++ ")"
showAll :: [Entry] -> String
showAll [] = []
showAll (x:xs) = Main.show x ++ "\n" ++ showAll xs
main :: IO ()
main = do putStr (showAll ( diction ))
The question asks:
Write a Haskell programs that takes
the English sentence 'english', looks
up each word in the English-French
dictionary using binary search,
performs word-for-word substitution,
assembles the French translation, and
prints it out.
The function 'quicksort' rejects
duplicate entries (with 'error'/abort)
so that there is precisely one French
definition for any English word. Test
'quicksort' with both the original
'raw_data' and after having added
'("saves", "sauve")' to 'raw_data'.
Here is a von Neumann late-stopping
version of binary search. Make a
literal transliteration into Haskell.
Immediately upon entry, the Haskell
version must verify the recursive
"loop invariant", terminating with
'error'/abort if it fails to hold. It
also terminates in the same fashion if
the English word is not found.
function binsearch (x : integer) : integer
local j, k, h : integer
j,k := 1,n
do j+1 <> k --->
h := (j+k) div 2
{a[j] <= x < a[k]} // loop invariant
if x < a[h] ---> k := h
| x >= a[h] ---> j := h
fi
od
{a[j] <= x < a[j+1]} // termination assertion
found := x = a[j]
if found ---> return j
| not found ---> return 0
fi
In the Haskell version
binsearch :: String -> Integer -> Integer -> Entry
as the constant dictionary 'a' of type
'[Entry]' is globally visible. Hint:
Make your string (English word) into
an 'Entry' immediately upon entering
'binsearch'.
The programming value of the
high-level data type 'Entry' is that,
if you can design these two functions
over the integers, it is trivial to
lift them to to operate over Entry's.
Anybody know how I'm supposed to go about my binarysearch function?
The instructor asks for a "literal transliteration", so use the same variable names, in the same order. But note some differences:
the given version takes only 1
parameter, the signature he gives
requires 3. Hmmm,
the given version is not recursive, but he asks for a
recursive version.
Another answer says to convert to an Array, but for such a small exercise (this is homework after all), I felt we could pretend that lists are direct access. I just took your diction::[Entry] and indexed into that. I did have to convert between Int and Integer in a few places.
Minor nit: You've got a typo in your english value (bs is a shortcut to binSearch I made):
*Main> map bs (words english)
[Entry ("A","Un"),Entry ("stitch","point"),Entry ("in","<`a>"),Entry ("time","te
mps"),*** Exception: Not found
*Main> map bs (words englishFixed)
[Entry ("A","Un"),Entry ("stitch","point"),Entry ("in","<`a>"),Entry ("time","te
mps"),Entry ("saves","en vaut"),Entry ("nine.","cent.")]
*Main>
A binary search needs random access, which is not possible on a list. So, the first thing to do would probably be to convert the list to an Array (with listArray), and do the search on it.
here's my code for just the English part of the question (I tested it and it works perfectly) :
module Main where
class Lex a where
(<!), (=!), (>!) :: a -> a -> Bool
data Entry = Entry String String
instance Lex Entry where
(Entry a _) <! (Entry b _) = a < b
(Entry a _) =! (Entry b _) = a == b
(Entry a _) >! (Entry b _) = a > b
-- at this point, three binary (infix) operators on values of type 'Entry'
-- have been defined
type Raw = (String, String)
raw_data :: [Raw]
raw_data = [("than a", "qu'un"), ("saves", "en vaut"), ("time", "temps"),
("in", "<`a>"), ("worse", "pire"), ("{", "{"), ("A", "Un"),
("}", "}"), ("stitch", "point"), ("crime;", "crime,"),
("a", "une"), ("nine.", "cent."), ("It's", "C'est"),
("Zazie", "Zazie"), ("cat", "chat"), ("it's", "c'est"),
("raisin", "raisin sec"), ("mistake.", "faute."),
("blueberry", "myrtille"), ("luck", "chance"),
("bad", "mauvais")]
cook :: Raw -> Entry
cook (x, y) = Entry x y
a :: [Entry]
a = map cook raw_data
quicksort :: Lex a => [a] -> [a]
quicksort [] = []
quicksort (x:xs) = quicksort (filter (<! x) xs) ++ [x] ++ quicksort (filter (=! x) xs) ++ quicksort (filter (>! x) xs)
getfirst :: Entry -> String
getfirst (Entry x y) = x
getsecond :: Entry -> String
getsecond (Entry x y) = y
binarysearch :: String -> [Entry] -> Int -> Int -> String
binarysearch s e low high
| low > high = " NOT fOUND "
| getfirst ((e)!!(mid)) > s = binarysearch s (e) low (mid-1)
| getfirst ((e)!!(mid)) < s = binarysearch s (e) (mid+1) high
| otherwise = getsecond ((e)!!(mid))
where mid = (div (low+high) 2)
translator :: [String] -> [Entry] -> [String]
translator [] y = []
translator (x:xs) y = (binarysearch x y 0 ((length y)-1):translator xs y)
english :: String
english = "A stitch in time saves nine."
compute :: String -> [Entry] -> String
compute x y = unwords(translator (words (x)) y)
main = do
putStr (compute english (quicksort a))
An important Prelude operator is:
(!!) :: [a] -> Integer -> a
-- xs!!n returns the nth element of xs, starting at the left and
-- counting from 0.
Thus, [14,7,3]!!1 ~~> 7.