a Haskell programming blog

I just finished an initial release of an interpreter for the befunge programming language, based on the ‘93 spec. The project was quite fun! My goal was to produce a well-designed program with performance that didn’t suck too bad. Here are some highlights:

Design

I found that writing the core functionality of the interpreter took almost no time, once I settled on the approach I would take. I used a monad transformer for the first time, StateT:

type REPL a = StateT ProgramState IO a

This let me pass around the state of the computation in the State monad while doing IO actions. This made some potentially-awkward befunge commands really easy to implement.
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I put together a skeleton module which should be usable for any of the problems in this year’s Google CodeJam Programming Contest (as long as they don’t adopt a new format/pattern for the problems this year). The module includes some useful parsing functions which should in the worst case be useful as a cheatsheet for those not too experienced with Parsec (*cough me). I hope it will encourage people to sign up and use Haskell in the contest!

To adapt the module to a problem, one defines the following:

1. a type Case into which we will parse a test case (i.e. problem)
2. a type SolvedCase, representing a solution to a test case
3. our algorithm :: Case -> SolvedCase
4. a Parser caseParser which can parse a single test case (n.b. not the whole file) into the Case type
5. a function formatCase :: SolvedCase -> String which brings the solution to the state where it can be prepended with the standard “Case #1: ” string

Here is the module, filled in to solve the Minimum Scalar Product practice problem. You can download the code here:

module Main
    where

import Text.ParserCombinators.Parsec
import IO hiding (try)
import System.Environment

-- PROBLEM-SPECIFIC IMPORTS --
import Data.List (sort)
import Control.Arrow
------------------------------


-- the input file will be parsed into a list of some type representing
-- individual cases to be solved by our algorithm:
type Input = [Case] 

-- our algorithms will produce a list of some type SolutionCase:
type Solution = [SolvedCase] 

-- we convert each solved case into a String, which is zipped with
--  the standard "Case #x: " strings for the final output
type Output = [String]





-- --------------- BEGIN EDITING --------------- --



-- DEFINE A TYPE TO REPRESENT A SINGLE UNSOLVED TEST CASE: --
type Case =
    -- EXAMPLE: a pair of lists of Ints (our "vectors"):
    ([Int],[Int])


-- DEFINE A TYPE TO REPRESENT A SINGLE SOLVED TEST CASE: --
type SolvedCase = 
    -- EXAMPLE: our Minimum Scalar Product as an Int:
    Int


     -- -- -- ALGORITHMS -- -- --


-- SOLVE A TEST CASE HERE:
algorithm :: Case -> SolvedCase
algorithm = 
    -- EXAMPLE: simply sort both lists, reverse one, and combine:
    sum . uncurry (zipWith (*)) . (reverse . sort *** sort)



    -- -- -- PARSING INPUT -- -- --


-- DEFINE PARSER FOR A TEST CASE: --
caseParser :: Parser Case
caseParser = do
     -- EXAMPLE: parses a case consisting of 3 lines: the first describes the
     --  number n of elements in the following two lines, the next two lines
     -- have n space-separated elements:
    w <- word
    let n = read w
    as <- count n word
    bs <- count n word
    return  (map read as, map read bs)



     -- -- -- FORMAT OUTPUT -- -- --


-- DEFINE A FUNCTION FROM AN INDIVIDUAL SolvedCase -> String.
formatCase :: SolvedCase -> String
formatCase sol = 
    -- EXAMPLE: nothing to speak of here:
    show sol
    
  

-- --------------- STOP EDITING --------------- --



    --------------------
    -- IO BOILERPLATE --
    --------------------


main = do
    
     -- pass the input file name to our program:
    (f:_) <- getArgs
    file  <- readFile f

     -- start parsing, solve problem, and prepare output:
    let inp  = parseWith mainParser file
        solution        = map algorithm inp
        solutionStrings = map formatCase solution
        outp = zipWith (++) prefixes solutionStrings
    
     -- write the prepared output to screen:
    putStr $ unlines outp


-- dies with error, or returns some datatype with our parsed data:
parseWith p = either (error . show) id . parse p "" 

-- to begin parsing, we read in a line containing the number of test cases
-- to follow. We parse them with caseParser, returning a list:
mainParser :: Parser Input
mainParser = do
    n  <- word
    ms <- count (read n) caseParser 
    return ms
    <?> "mainParser"

-- strings to prepend to output:
prefixes = [ "Case #" ++ show n ++ ": " | n <- [1..]]





    ---------------------
    -- VARIOUS PARSERS --
    ---------------------


-- -- LINE PARSERS -- --


-- a single line String, up to the newline:
wholeLine :: Parser String
wholeLine = manyTill anyChar (try newline) <?> "wholeLine"

-- parse a String with whitespace-separated values into [String]
whiteSepLine = manyTill spaceSepWord newline <?> "whiteSepLine"


-- -- WORD PARSERS -- --


-- a single word followed by whitespace (space, newline, etc.):
word = do
    w <- many1 nonWhite
    spaces
    return w
    <?> "word"

-- a single word followed by one or more ' ' characters (won't consume '\n')
spaceSepWord = do
    w <- many1 nonWhite
    many (char ' ')
    return w
    <?> "spaceSepWord"

-- e.g. "hello:world" ---> ("hello","world")
-- won't consume newlines
twoWordsSepBy c = do
    x <- manyTill nonWhite (try$ char c)
    y <- many1 nonWhite
    many (char ' ')
    return (x,y)
    <?> "twoWordsSepBy"


-- -- CHARACTER PARSERS -- --


-- nonWhitespace character:
nonWhite = noneOf " \v\f\t\r\n" <?> "nonWhite"

EDIT: Don’t use this package, but use instead Data.List.Split by Brent Yorgey. I didn’t see that a package like his existed! This module will hopefully be removed from hackage if they can do that.

I just finished the initial release of a simple module called list-grouping that contains functions to partition a list into sub-lists in various ways, based on some predicate or integer offset. Functions like these are a little awkward to write and I was surprised when I didn’t see anything on hackage!

Check out the package description and install it with:

$ cabal install list-grouping

Here is an example from a previous post to build a binary tree from an in-order list, which uses the above library:

module Main
    where

import Data.List.Grouping

data Tree a = Node a (Tree a) (Tree a)
            | End
             deriving Show

fromSorted :: [a] -> Tree a
fromSorted = foldl mkTree End . splitWith [2^n | n<-[0..]]
    where mkTree l (n:ns) = Node n l (fromSorted ns)

I’m sure the functions can be made more efficient, to take advantage of fusion or what-not, and I hope the library will eventually contain the most efficient implementations possible.

I also am looking for suggestions for other useful list grouping functions to include. Send your suggestions along! You can get the darcs source with:

$ darcs get http://coder.bsimmons.name/code/ListGrouping/