December 2008 - Haskell-Cafe

Commutative monads vs Applicative functors
by Ronald Guida 10 Jul '16

10 Jul '16

I have a few questions about commutative monads and applicative functors. >From what I have read about applicative functors, they are weaker than monads because with a monad, I can use the results of a computation to select between alternative future computations and their side effects, whereas with an applicative functor, I can only select between the results of computations, while the structure of those computations and their side effects are fixed in advance. But then there are commutative monads. I'm not exactly sure what a commutative monad is, but my understanding is that in a commutative monad the order of side effects does not matter. This leads me to wonder, are commutative monads still stronger than applicative functors, or are they equivalent? And by the way, what exactly is a commutative monad?

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Cannot understand liftM2
by Nicola Paolucci 14 Feb '12

14 Feb '12

Hi All, I'm loving learning Haskell quite a bit. It is stretching my brain but in a delightfull way. I've googled, I've hoogled but I haven't found a clear explanation for what exactly liftM2 does in the context below. Using the cool lambdabot "pointless" utility I found out that: > \x -> snd(x) - fst(x) is the same as: > liftM2 (-) snd fst I like the elegance of this but I cannot reconcile it with its type. I can't understand it. I check the signature of liftM2 and I get: Prelude> :t liftM2 Prelude> liftM2 :: (Monad m) => (a1 -> a2 -> r) -> m a1 -> m a2 -> m r Can someone help me understand what's happening here ? What does a Monad have to do with a simple subtraction ? What is actually the "m" of my example ? I am sure if I get this I'll be another step closer to illumination ... Thanks, Nick

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really difficult for a beginner like me...
by Ivan Amarquaye 25 Mar '10

25 Mar '10

Hi everyone, this is my first posting on here and this was what drove me here and the quest to know more as i anticipate a lot of help and direction in this quite new and different environment haskell.I have this paper that i'm working on and need to solve these scenarios/ cases based on some sample codes: Scenarios/cases: 1) Allow words to be hyphenated and treat the hyphenated word as a single word (including the hyphen). 2) As for no. 2 but if the hyphen is the last character on a line treat the hyphenated word as a single word without the hyphen. 3) Treat a capitalised word (one or more capital letters) the same as lower case, i.e. only the lower case word appears in the index. 4) Treat a word ending in an ‘s’ as a plural and thus the same as the singular, i.e. only the singular appears in the index. 5) As for no. 5 but (a) treat suffix ‘ss’ as not a plural; and (b) treat the plural suffixes ‘sses’, ‘zzes’, ‘oes’, ‘xes’, ‘shes’, ‘ches’ the same as the singular, i.e. without the ‘es’, e.g. “branches” (except for 4- and 5-letter plurals with suffices ‘oes’ and ‘ches’, e.g. “floes”, and 4-letter plural suffix ‘xes’); and (c) treat the plural suffix ‘ies’ (except for 4-letter plurals, e.g. “pies”) as the singular suffix ‘y’. 6) Make the output more readable in the form of an index table. 7) Use an output file 8) Include a user-friendly menu by which the user can choose input and output file names. This is the code i'm supposed tomodify and in some cases create new functions to support.I also need some explanation as the various approaches in solving them.............................................................................................. The function makeIndex given a document produces a list of entries. Each entry is a word and a list of line numbers (for words > 4 letters) Type definitions: import Prelude -- hiding (Word) -- predefined Word hidden, so we can define ours -- type String = [Char] defined in Prelude type Doc = String type Line = String type Word = String -- our version makeIndex :: Doc -> [ ([Int], Word) ] A data-directed design considers a sequence of functions (i.e using composition operator ‘.’) to transform the document of type, Doc, into an index of type, [ ([Int], Word) ]. splitUp the document, doc, into a list of lines, [Line]. numLines pairs each line with a line number, [(Int, Line)]. allNumWords splits lines into words and line no., [(Int, Word)]. sortLs sorts words into alphabetical order, [(Int, Word)]. makeLists makes a list for each line number, [([Int], Word)]. amalgamate nos. into a list of nos. for each word, [([Int], Word)]. shorten into a list for words > 4 letters, [([Int], Word)]. makeIndex = shorten . -- [([Int], Word)] -> [([Int], Word)] amalgamate . -- [([Int], Word)] -> [([Int], Word)] makeLists . -- [(Int, Word)] -> [([Int], Word)] sortLs . -- [(Int, Word)] -> [(Int, Word)] allNumWords . -- [(Int, Line)] -> [(Int, Word)] numLines . -- [Line] -> [(Int, Line)] splitUp -- Doc -> [Line] Last -- [a] -> [a ] splitUp function splitUp :: Doc -> [Line] splitUp [] = [] splitUp text = takeWhile (/='\n') text : -- first line (splitUp . -- splitup other lines dropWhile (==’\n’) . -- delete 1st newline(s) dropWhile (/='\n')) text -- other lines Example: splitUp “hello world\n\nnext world” => [“hello world”, “next world”] numLines function: numLines :: [Line] -> [(Int, Line)] numLines lines -- list of pairs of = zip [1 .. length lines] lines -- line no. & line Example: numLines [“hello world”, “next world”] => [(1, “hello world”), (2, “next world”)] splitWords function: -- for each line -- a) split into words -- b) attach line no. to each word splitWords :: Line -> [Word] -- a) splitWords [ ] = [ ] splitWords line = takeWhile isLetter line : -- first word in line (splitWords . -- split other words dropWhile (not.isLetter && Last ==’-’) . -- delete separators dropWhile isLetter) line -- other words where isLetter ch = (‘a’<=ch) && (ch<=’z’) || (‘A’<=ch) && (ch<=’Z’) Example: splitWords “hello world” => [“hello”, “world”] allNumWords function: numWords :: (Int, Line) -> [(Int, Word)] -- b) numWords (number, line) = map addLineNum ( splitWords line) -- all line pairs where addLineNum word = (number, word) -- a pair allNumWords :: [(Int, Line)] -> [(Int, Word)] allNumWords = concat . map numWords -- doc pairs Examples: addLineNum “hello” => (1, “hello”) numWords (1, “hello world”) => [(1, “hello”), (1, “world”)] allNumWords [(1, “hello world”), (2, “next world”)] => [(1, “hello”), (1, “world”), (2, “next”), (2, “world”)] SortLs function: sortLs :: [(Int, Word)] -> [(Int, Word)] sortLs [ ] = [ ] sortLs (a:x) = sortLs [b | b <- x, compare b a] -- sort 1st half ++ [a] ++ -- 1st in middle sortLs [b | b <- x, compare a b] -- sort 2nd half where compare (n1, w1) (n2, w2) = (w1 < w2) -- 1st word less || (w1 == w2 && n1 < n2) -- check no. Example: sortLs [(1, “hello”), (1, “world”), (2, “next”), (2, “world”)] => [(1, “hello”), (2, “next”), (1, “world”), (2, “world”)] makeLists function: makeLists :: [(Int, Word)] -> [([Int], Word)] makeLists = map mk -- all pairs where mk (num, word) = ([num], word) -- list of single no. Examples: mk (1, “hello”) => ([1], “hello”) makeLists [(1, “hello”), (2, “next”), (1, “world”), (2, “world”)] => [([1], “hello”), ([2], “next”), ([1], “world”), ([2], “world”)] Amalgamate function: amalgamate :: [([Int], Word)] -> [([Int], Word)] amalgamate [ ] = [ ] amalgamate [a] = [a] amalgamate ((n1, w1) : (n2, w2) : rest) -- pairs of pairs | w1 /= w2 = (n1, w1) : amalgamate ((n2, w2) : rest) | otherwise = amalgamate ((n1 ++ n2, w1) : rest) -- if words are same grow list of numbers Example: amalgamate [([1], “hello”), ([2], “next”), ([1], “world”), ([2], “world”)] => [([1], “hello”), ([2], “next”), ([1, 2], “world”)] Shorten function: shorten :: [([Int], Word)] -> [([Int], Word)] shorten = filter long -- keep pairs >4 where long (num, word) = length word > 4 -- check word >4 Example: shorten [([1], “hello”), ([2], “next”), ([1, 2], “world”)] => [([1], “hello”), ([1, 2], “world”)] _________________________________________________________________ Connect to the next generation of MSN Messenger http://imagine-msn.com/messenger/launch80/default.aspx?locale=en-us&source=…

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Arch Haskell News: Nov 23 2008
by Don Stewart 17 Jan '10

17 Jan '10

News about Haskell on Arch Linux * Arch now has 734 Haskell packages now * That’s an increase of 29 new packages in the last 8 days! * 3.6 new Haskell releases are occuring each day. Noteworthy, * haskell-hledger-0.2: “A ledger-compatible text-based accounting tool.” * gitit-0.3.1: “Wiki using HAppS, git, and pandoc.” * lhc-20081121: “Lhc Haskell Compiler” * haskell-hosc-0.6: “Haskell Open Sound Control” * haskell-flickr-0.3.2: “Haskell binding to the Flickr API” * haskell-delicious-0.3.2: “Accessing the del.icio.us APIs from Haskell (v2)” * haskell-mediawiki 0.2.3: “Interfacing with the MediaWiki API” * darcs-2.1.2.2: “a distributed, interactive, smart revision control system” Full update list, http://archhaskell.wordpress.com/2008/11/24/arch-haskell-news-nov-23-2008/ -- Don

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Re: Code and Perf. Data for Prime Finders (was: Genuine Eratosthenes sieve)
by Dave Bayer 29 Dec '09

29 Dec '09

Here is another prime sieve. It is about half the length of the fastest contributed code (ONeillPrimes) and nearly as fast until it blows up on garbage collection: > % cat ONeillPrimes.hs | grep -v "^--" | wc > 185 1105 6306 > % cat BayerPrimes.hs | grep -v "^--" | wc > 85 566 2418 > > [Integer] -O 1*10^6 2*10^6 3*10^6 4*10^6 5*10^6 > --------------------------------------------------------- > ONeillPrimes | 3.555 | 7.798 | 12.622 | 18.927 | 23.529 > BayerPrimes | 3.999 | 8.895 | 18.003 | 22.977 | 38.053 I wrote this as an experiment in coding data structures in Haskell's lazy evaluation model, rather than as explicit data. The majority of the work done by this code is done by "merge"; the multiples of each prime percolate up through a tournament consisting of a balanced tree of suspended "merge" function calls. In order to create an infinite lazy balanced tree of lists, the datatype > data List a = A a (List a) | B [a] is used as scaffolding. One thinks of the root of the infinite tree as starting at the leftmost child, and crawling up the left spine as necessary. What I'm calling a "venturi" > venturi :: Ord a => [[a]] -> [a] merges an infinite list of infinite lists into one list, under the assumption that each list, and the heads of the lists, are in increasing order. This could be a generally useful function. If one can think of a better way to write "venturi", swapping in your code would in particular yield a faster prime sieve. I found that a tertiary merge tree was faster than a binary merge tree, because this leads to fewer suspensions. One can speed this code up a bit by interleaving strict and lazy calls, but I prefer to leave the code short and readable. It is bizarre that > trim p = let f m x = mod x m /= 0 in filter (f p) lurks in the prime sieve code, but it is only used with primes of size up to the square root of the largest output prime. I tried more thoughtful alternatives, and they all slowed down the sieve. Sometimes dumb is beautiful. Thanks to apfelmus for various helpful remarks that lead me to think of this approach. Here is the code: > module BayerPrimes (venturi,primes) where > > -- Code for venturi :: Ord a => [[a]] -> [a] > > merge :: Ord a => [a] -> [a] -> [a] -> [a] > merge xs@(x:xt) ys@(y:yt) zs@(z:zt) > | x <= y = if x <= z > then x : (merge xt ys zs) > else z : (merge xs ys zt) > | otherwise = if y <= z > then y : (merge xs yt zs) > else z : (merge xs ys zt) > merge _ _ _ = undefined > > data List a = A a (List a) | B [a] > > mergeA :: Ord a => List a -> List a -> List a -> List a > mergeA (A x xt) ys zs = A x (mergeA xt ys zs) > mergeA (B xs) ys zs = mergeB xs ys zs > > mergeB :: Ord a => [a] -> List a -> List a -> List a > mergeB xs@(x:xt) ys@(A y yt) zs = case compare x y of > LT -> A x (mergeB xt ys zs) > EQ -> A x (mergeB xt yt zs) > GT -> A y (mergeB xs yt zs) > mergeB xs (B ys) zs = mergeC xs ys zs > mergeB _ _ _ = undefined > > mergeC :: Ord a => [a] -> [a] -> List a -> List a > mergeC xs@(x:xt) ys@(y:yt) zs@(A z zt) > | x < y = if x < z > then A x (mergeC xt ys zs) > else A z (mergeC xs ys zt) > | otherwise = if y < z > then A y (mergeC xs yt zs) > else A z (mergeC xs ys zt) > mergeC xs ys (B zs) = B $ merge xs ys zs > mergeC _ _ _ = undefined > > root :: Ord a => List a -> [List a] -> [a] > root (A x xt) yss = x : (root xt yss) > root (B xs) (ys:zs:yst) = root (mergeB xs ys zs) yst > root _ _ = undefined > > wrap :: [a] -> List a > wrap [] = B [] > wrap (x:xt) = A x $ B xt > > triple :: Ord a => [List a] -> [List a] > triple (x:y:z:xs) = mergeA x y z : (triple xs) > triple _ = undefined > > group :: Ord a => [List a] -> [List a] > group (x:y:xt) = x : y : (group $ triple xt) > group _ = undefined > > venturi :: Ord a => [[a]] -> [a] > venturi (x:xt) = root (wrap x) $ group $ map wrap xt > venturi _ = undefined > > -- Code for primes :: Integral a => [a] > > diff :: Ord a => [a] -> [a] -> [a] > diff xs@(x:xt) ys@(y:yt) = case compare x y of > LT -> x : (diff xt ys) > EQ -> (diff xt yt) > GT -> (diff xs yt) > diff _ _ = undefined > > trim :: Integral a => a -> [a] -> [a] > trim p = let f m x = mod x m /= 0 in filter (f p) > > seed :: Integral a => [a] > seed = [2,3,5,7,11,13,17] > > wheel :: Integral a => [a] > wheel = drop 1 [ m*j + k | j <- [0..], k <- ws ] > where m = foldr1 (*) seed > ws = foldr trim [1..m] seed > > multiples :: Integral a => [a] -> [[a]] > multiples ws = map fst $ tail $ iterate g ([], ws) > where g (_,ps@(p:pt)) = ([ m*p | m <- ps ], trim p pt) > g _ = undefined > > primes :: Integral a => [a] > primes = seed ++ (diff wheel $ venturi $ multiples wheel)

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Will GHC finally support epoll in 2009?
by Levi Greenspan 12 Dec '09

12 Dec '09

Ticket #635 "Replace use of select() in the I/O manager with epoll/kqueue/etc." (http://hackage.haskell.org/trac/ghc/ticket/635) dates back from 2005. Now its 2009 and GHC can handle hundreds of thousands of threads, yet having more than 1024 file descriptors open is still impossible. This limitation is a real bottle neck and prevents some cool developments on server side, like scalable comet servers. Instead others (e.g. Erlang guys) post stories about how they achived 1 million comet users (cf. http://www.metabrew.com/article/a-million-user-comet-application-with-mochi… ). Hence my question - is it likely that GHC will support epoll in 2009? Cheers and happy new year! - Levi

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HDBC Oracle bindings(?/!)
by Thiago Arrais 15 Sep '09

15 Sep '09

Are there Oracle bindings for HDBC anywhere? The HDBC project page talks about Oracle backends but there isn't an Oracle-specific driver listed (I assume one can reach an Oracle backend through the ODBC interface?). I started writing Oracle bindings for HDBC based on the Takusen code. It was done mostly for playing than for anything else and certainly isn't production ready, but it is available in a darcs repo at http://thiagoarrais.com/repos/hdbc-oracle in case anyone is interested. This code isn't ready for prime time yet and I'm looking for some that *is*. Is there such a thing already? Cheers, Thiago Arrais -- Enjoy Chaos - http://blog.thiagoarrais.com Thoughts, ideas and useless ranting about software development and technology

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[Haskell] ANNOUNCE: ChristmasTree 0.1 (excuses for second attempt)
by S.Doaitse Swierstra 08 Jul '09

08 Jul '09

We are pleased to announce the availability of the package "ChristmasTree", which contains the code associated with our paper at the last Haskell symposium: @inproceedings{1411296, author = {Marcos Viera and S. Doaitse Swierstra and Eelco Lempsink}, title = {Haskell, do you read me?: constructing and composing efficient top-down parsers at runtime}, booktitle = {Haskell '08: Proceedings of the first ACM SIGPLAN symposium on Haskell}, year = {2008}, isbn = {978-1-60558-064-7}, pages = {63--74}, location = {Victoria, BC, Canada}, doi = {http://doi.acm.org/10.1145/1411286.1411296}, publisher = {ACM}, address = {New York, NY, USA}, } The name of the package stands for: "Changing Haskell's Read Implementation Such That by Manipulating Abstract Syntax Trees it Reads Expressions Efficiently" which, given the time of year, seems appropriate. Feel free to download and unpack this "present" at what for the Dutch is called "Sinterklaasavond" (http://en.wikipedia.org/wiki/Sinterklaas) Arthur Baars Marcos Viera Eelco Lempsink Doaitse Swierstra PS: the package uses our library supporting transformation of typed abstract syntax, which we placed in a separate package TTTAS

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I love purity, but it's killing me.
by Tom Hawkins 10 Jun '09

10 Jun '09

I've been programming with Haskell for a few years and love it. One of my favorite applications of Haskell is using for domain specific languages. However, after designing a handful of DSLs, I continue to hit what appears to be a fundamental hurdle -- or at least I have yet to find an adequate solution. My DSLs invariably define a datatype to capture expressions; something like this: data Expression = Add Expression Expression | Sub Expression Expression | Variable String | Constant Int deriving Eq Using the datatype Expression, it is easy to mass a collections of functions to help assemble complex expressions, which leads to very concise programs in the DSL. The problem comes when I want to generate efficient code from an Expression (ie. to C or some other target language). The method I use invovles converting the tree of subexpressions into an acyclic graphic to eliminate common subexpressions. The nodes are then topologically ordered and assigned an instruction, or statement for each node. For example: let a = Add (Constant 10) (Variable "i1") b = Sub (Variable "i2") (Constant 2) c = Add a b would compile to a C program that may look like this: a = 10 + i1; b = i2 - 2; c = a + b; The process of converting an expression tree to a graph uses either Eq or Ord (either derived or a custom instance) to search and build a set of unique nodes to be ordered for execution. In this case "a", then "b", then "c". The problem is expressions often have shared, equivalent subnodes, which dramatically grows the size of the tree. For example: let d = Add c c e = Add d d -- "e" now as 16 leaf nodes. As these trees grow in size, the equality comparison in graph construction quickly becomes the bottleneck for DSL compilation. What's worse, the phase transition from tractable to intractable is very sharp. In one of my DSL programs, I made a seemingly small change, and compilation time went from milliseconds to not-in-a-million-years. Prior to Haskell, I wrote a few DSLs in OCaml. I didn't have this problem in OCaml because each "let" expression was mutable, and I could use the physical equality operator to perform fast comparisons. Unfortunately, I have grown to love Haskell's type system and its lack of side effects, and could never go back. Is there anything that can be done to dramatically speed up comparisons, or is there a better approach I can take to extract common subexpressions? I should point out I have an opportunity to get Haskell on a real industrial application. But if I can't solve this problem, I may have to resort to far less eloquent languages. :-( Thanks for any and all help. -Tom

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Lazy vs correct IO [Was: A round of golf]
by oleg＠okmij.org 07 Apr '09

07 Apr '09

Lennart Augustsson wrote > main = do > name:_ <- getArgs > file <- readFile name > print $ length $ lines file Given the stance against top-level mutable variables, I have not expected to see this Lazy IO code. After all, what could be more against the spirit of Haskell than a `pure' function with observable side effects. With Lazy IO, one indeed has to choose between correctness and performance. The appearance of such code is especially strange after the evidence of deadlocks with Lazy IO, presented on this list less than a month ago. Let alone unpredictable resource usage and reliance on finalizers to close files (forgetting that GHC does not guarantee that finalizers will be run at all). Is there an alternative? -- Counting the lines in a file import IterateeM count_nl = liftI $ IE_cont (step 0) where step acc (Chunk str) = liftI $ IE_cont (step $! acc + count str) step acc stream = liftI $ IE_done acc stream count [] = 0 count ('\n':str) = succ $! count str count (_:str) = count str main = do name:_ <- getArgs IE_done counter _ <- unIM $ enum_file name >. enum_eof ==<< count_nl print counter The function count_nl could have been in the library, but I'm a minimalist. It is written in a declarative rather than imperative style, and one easily sees what it does. The above code as well as the IterateeM library is Haskell98. It does not use any unsafe Haskell functions whatsoever. time wc -l /usr/share/dict/words 235882 /usr/share/dict/words real 0m0.024s user 0m0.022s sys 0m0.000s time ~/Docs/papers/DEFUN08/Wc /usr/share/dict/words 235882 real 0m0.141s user 0m0.126s sys 0m0.008s To compare with lazy IO, the code using readFile gives time ~/Docs/papers/DEFUN08/Wc /usr/share/dict/words 235882 real 0m0.297s user 0m0.262s sys 0m0.023s So, choosing correctness does not mean losing in performance; in fact, one may even gain. Can enumerators compose? Well, we already seen the example above (enum_file name >. enum_eof) where the operation (>.) e1 >. e2 = (==<<) e2 . e1 is a flipped composition if monadic bind were considered a flipped application. Here is a more interesting example: count words in all the files whose names are given on the command line. There may be many files given, thousands of them. -- Count the stream. Again, could have been in the library stream_count :: Monad m => IterateeGM el m Int stream_count = liftI $ IE_cont (step 0) where step acc (Chunk []) = liftI $ IE_cont (step acc) step acc (Chunk [_]) = liftI $ IE_cont (step $! succ acc) step acc (Chunk ls) = liftI $ IE_cont (step $! acc + length ls) step acc stream = liftI $ IE_done acc stream main = do names <- getArgs let enumerators = foldr (\name -> (enum_file name >.)) enum_eof names IE_done (IE_done counter _) _ <- unIM $ enumerators ==<< enum_words stream_count print counter We notice that the composition of enumerators corresponds to the `concatenation' of their sources. Declaratively, the meaning of the above code is: -- all the given files are concatenated -- the resulting stream of characters is converted to a stream of words -- the stream of words is counted. Operationally, the code does not open more than one file at a time. More importantly, the code *never* reads more than 4096 characters at a time. A block of the file is read, split into words, counted, and only then another chunk is read. After one file is done, it is closed, and another file is processed. One can see that only one file is being opened at a time by enabling traces. The processing is fully incremental. /usr/local/share/doc/ghc6> find . -name \*.html -print | time xargs ~/Docs/papers/DEFUN08/Wc 3043421 16.99 real 15.83 user 0.71 sys BTW, the program has counted words in 1169 files. It is interesting to compare the above main function with the corresponding lazy IO: main'' = do names <- getArgs files <- mapM readFile names print $ length $ words (concat files) The number of lines is comparable. The execution is not. If we try to run the lazy IO code, we get: /usr/local/share/doc/ghc6> find . -name \*.html -print | time xargs ~/Docs/papers/DEFUN08/Wc Wc: ./libraries/Win32/Graphics-Win32-GDI-Path.html: openFile: resource exhausted (Too many open files)

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