I am trying to write a pseudo-random generator in Elm (to generate points on a screen) but not so easy since it interferes with the ``purity" of the Elm compiler.
OK, so why not write our own function? We can get sort-of random behavior if we write stuff like:
-- initial state
randomNumbers = [ 1 ]
x = 1
b = 2
n = 2017
-- generate random numbers
x = (x*b) % n
randomNumbers = x :: randomNumbers
This does not follow Elm's rule of variable assignments. Something is wrong.
All I am doing is finding the power of 2 mod 2017. The first few are easy but then the sequence gets unpredictible. So I need to keep track of the last number computed.
[ 1, 2, 4, 8, ... , 1024, 31, 62, 124, ...]
Even if I try to use special properties of arithmetic I still have to compute this sparse list of powers
[ 1, 2^1, 2^2, 2^4, 2^8, 2^16, ... ]
can I can solve by successive squaring, but I still need some way to memorize the last step.
I thought... as long as I write my own code, I don't have to import randomness from the "real world" to generate my fair numbers. This way respecting the pureness of Elm. However, I end up writing something that is stateful.
Elm does have a random-number generator now - an implementation of some algorithm - in the Random library, returning a generator type.
This repl session has been quite instructive:
> import Random exposing (..)
>
> initialSeed 0
Seed {
state = State 1 1 ,
next = <function>,
split = <function>,
range = <function>
}
: Random.Seed
> seed0 = initialSeed 101
Seed {
state = State 102 1, ,
next = <function>,
split = <function>,
range = <function>
}
: Random.Seed
> step ( int 0 10 ) seed0
(10,Seed { state = State 4081428 40692, ,
next = <function>,
split = <function>,
range = <function>
}
: ( Int, Random.Seed )
Even with my simplified baby random number generator, what is so stateful here?
My apologies in advance if I say memoizaton when I mean dynamic programming or the other way around
What is the difference between memoization and dynamic programming?
If I could write a stateful function f that remembered things, I could generate all my values with just one line:
List.map f [1..100]
two candidates:
A
https://github.com/elm-community/list-extra/blob/3.1.0/src/List/Extra.elm
iterate : (a -> Maybe a) -> a -> List a
iterate f x =
case f x of
Just x' -> x :: iterate f x'
Nothing -> [x]
B
https://github.com/elm-community/elm-lazy-list/blob/1.3.0/src/Lazy/List.elm
{-| Create an infinite list of applications of a function on some value.
Equivalent to:
x ::: f x ::: f (f x) ::: f (f (f x)) ::: ... -- etc...
-}
iterate : (a -> a) -> a -> LazyList a
iterate f a =
lazy <|
\() ->
Cons a (iterate f (f a))
Related
I am trying to build a function that converts a Decimal(Int) into a Binary number.
Unfortunately other than in java it is not possible to divide an int by two in haskell.
I am very new to functional programming so the problem could be something trivial.
So far I could not find another solution to this problem but
here is my first try :
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = if (mod n 2 == 0) then
do
0:fromDecimal(n/2)
else
do
1:fromDecimal(n/2)
I got an java implementation here which I did before :
public void fromDecimal(int decimal){
for (int i=0;i<values.length;i++){
if(decimal % 2 = 0)
values[i]=true ;
decimal = decimal/ 2;
else {values[i]= false;
} }
}
Hopefully this is going to help to find a solution!
There are some problems with your solution. First of all, I advise not to use do at all, until you understand what do does. Here we do not need do at all.
Unfortunately other than in java it is not possible to divide an int by two in haskell.
It actually is, but the / operator (which is in fact the (/) function), has type (/) :: Fractional a => a -> a -> a. An Int is not Fractional. You can perform integer division with div :: Integral a => a -> a -> a.
So then the code looks like:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = if (mod n 2 == 0) then 0:fromDecimal (div n 2) else 1:fromDecimal (div n 2)
But we can definitely make this more elegant. mod n 2 can only result in two outcomes: 0 and 1, and these are exactly the ones that we use at the left side of the (:) operator.
So we do not need to use an if-then-else at all:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = mod n 2 : fromDecimal (div n 2)
Likely this is still not exactly what you want: here we write the binary value such that the last element, is the most significant one. This function will add a tailing zero, which does not make a semantical difference (due to that order), but it is not elegant either.
We can define an function go that omits this zero, if the given value is not zero, like:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = go n
where go 0 = []
go k = mod k 2 : go (div k 2)
If we however want to write the most significant bit first (so in the same order as we write decimal numbers), then we have to reverse the outcome. We can do this by making use of an accumulator:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = go n []
where go 0 r = r
go k rs = go (div k 2) (mod k 2:rs)
You cannot / integers in Haskell – division is not defined in terms of integral numbers! For integral division use div function, but in your case more suitable would be divMod that comes with mod gratis.
Also, you are going to get reversed output, so you can reverse manually it after that, or use more memory-efficient version with accumulator:
decToBin :: Int -> [Int]
decToBin = go [] where
go acc 0 = acc
go acc n = let (d, m) = n `divMod` 2 in go (m : acc) d
go will give you an empty list for 0. You may add it manually if the list is empty:
decToBin = (\l -> if null l then [0] else l) . go [] where ...
Think through how your algorithm will work. It starts from 2⁰, so it will generate bits backward from how we ordinarily think of them, i.e., least-significant bit first. Your algorithm can represent non-negative binary integers only.
fromDecimal :: Int -> [Int]
fromDecimal d | d < 0 = error "Must be non-negative"
| d == 0 = [0]
| otherwise = reverse (go d)
where go 0 = []
go d = d `rem` 2 : go (d `div` 2)
In Haskell, when we generate a list in reverse, go ahead and do so but then reverse the result at the end. The reason for this is consing up a list (gluing new items at the head with :) has a constant cost and the reverse at the end has a linear cost — but appending with ++ has a quadratic cost.
Common Haskell style is to have a private inner loop named go that the outer function applies when it’s happy with its arguments. The base case is to terminate with the empty list when d reaches zero. Otherwise, we take the current remainder modulo 2 and then proceed with d halved and truncated.
Without the special case for zero, fromDecimal 0 would be the empty list rather than [0].
The binary numbers are usually strings and not really used in calculations.
Strings are also less complicated.
The pattern of binary numbers is like any other. It repeats but at a faster clip.
Only a small set is necessary to generate up to 256 (0-255) binary numbers.
The pattern can systematically be expanded for more.
The starting pattern is 4, 0-3
bd = ["00","01","10","11"]
The function to combine them into larger numbers is
d2b n = head.drop n $ [ d++e++f++g | d <- bd, e <- bd, f <- bd, g <- bd]
d2b 125
"01111101"
If it's not obvious how to expand, then
bd = ["000","001","010","011","100","101","110","111"]
Will give you up to 4096 binary digits (0-4095). All else stays the same.
If it's not obvious, the db2 function uses 4 pairs of binary numbers so 4 of the set. (2^8) - 1 or (2^12) - 1 is how many you get.
By the way, list comprehension are sugar coated do structures.
Generate the above patterns with
[ a++b | a <- ["0","1"], b <- ["0","1"] ]
["00","01","10","11"]
and
[ a++b++c | a <- ["0","1"], b <- ["0","1"], c <- ["0","1"] ]
["000","001","010","011","100","101","110","111"]
More generally, one pattern and one function may serve the purpose
b2 = ["0","1"]
b4 = [ a++b++c++d | a <- b2, b <- b2, c <- b2, d <- b2]
b4
["0000","0001","0010","0011","0100","0101","0110","0111","1000","1001","1010","1011","1100","1101","1110","1111"]
bb n = head.drop n $ [ a++b++c++d | a <- b4, b <- b4, c <- b4, d <- b4]
bb 32768
"1000000000000000"
bb 65535
"1111111111111111"
To calculate binary from decimal directly in Haskell using subtraction
cvtd n (x:xs) | x>n = 0:(cvtd n xs)
| n>x = 1:(cvtd (n-x) xs)
| True = 1:[0|f<-xs]
Use any number of bits you want, for example 10 bits.
cvtd 639 [2^e|e<-[9,8..0]]
[1,0,0,1,1,1,1,1,1,1]
import Data.List
dec2bin x =
reverse $ binstr $ unfoldr ndiv x
where
binstr = map (\x -> "01" !! x)
exch (a,b) = (b,a)
ndiv n =
case n of
0 -> Nothing
_ -> Just $ exch $ divMod n 2
I have written this function that computes Collatz sequences, and I see wildly varying times of execution depending on the spin I give it. Apparently it is related to something called "memoization", but I have a hard time understanding what it is and how it works, and, unfortunately, the relevant article on HaskellWiki, as well as the papers it links to, have all proven to not be easily surmountable. They discuss intricate details of the relative performance of highly layman-indifferentiable tree constructions, while what I miss must be some very basic, very trivial point that these sources neglect to mention.
This is the code. It is a complete program, ready to be built and executed.
module Main where
import Data.Function
import Data.List (maximumBy)
size :: (Integral a) => a
size = 10 ^ 6
-- Nail the basics.
collatz :: Integral a => a -> a
collatz n | even n = n `div` 2
| otherwise = n * 3 + 1
recollatz :: Integral a => a -> a
recollatz = fix $ \f x -> if (x /= 1)
then f (collatz x)
else x
-- Now, I want to do the counting with a tuple monad.
mocollatz :: Integral b => b -> ([b], b)
mocollatz n = ([n], collatz n)
remocollatz :: Integral a => a -> ([a], a)
remocollatz = fix $ \f x -> if x /= 1
then f =<< mocollatz x
else return x
-- Trivialities.
collatzLength :: Integral a => a -> Int
collatzLength x = (length . fst $ (remocollatz x)) + 1
collatzPairs :: Integral a => a -> [(a, Int)]
collatzPairs n = zip [1..n] (collatzLength <$> [1..n])
longestCollatz :: Integral a => a -> (a, Int)
longestCollatz n = maximumBy order $ collatzPairs n
where
order :: Ord b => (a, b) -> (a, b) -> Ordering
order x y = snd x `compare` snd y
main :: IO ()
main = print $ longestCollatz size
With ghc -O2 it takes about 17 seconds, without ghc -O2 -- about 22 seconds to deliver the length and the seed of the longest Collatz sequence starting at any point below size.
Now, if I make these changes:
diff --git a/Main.hs b/Main.hs
index c78ad95..9607fe0 100644
--- a/Main.hs
+++ b/Main.hs
## -1,6 +1,7 ##
module Main where
import Data.Function
+import qualified Data.Map.Lazy as M
import Data.List (maximumBy)
size :: (Integral a) => a
## -22,10 +23,15 ## recollatz = fix $ \f x -> if (x /= 1)
mocollatz :: Integral b => b -> ([b], b)
mocollatz n = ([n], collatz n)
-remocollatz :: Integral a => a -> ([a], a)
-remocollatz = fix $ \f x -> if x /= 1
- then f =<< mocollatz x
- else return x
+remocollatz :: (Num a, Integral b) => b -> ([b], a)
+remocollatz 1 = return 1
+remocollatz x = case M.lookup x (table mutate) of
+ Nothing -> mutate x
+ Just y -> y
+ where mutate x = remocollatz =<< mocollatz x
+
+table :: (Ord a, Integral a) => (a -> b) -> M.Map a b
+table f = M.fromList [ (x, f x) | x <- [1..size] ]
-- Trivialities.
-- Then it will take just about 4 seconds with ghc -O2, but I would not live long enough to see it complete without ghc -O2.
Looking at the details of cost centres with ghc -prof -fprof-auto -O2 reveals that the first version enters collatz about a hundred million times, while the patched one -- just about one and a half million times. This must be the reason of the speedup, but I have a hard time understanding the inner workings of this magic. My best idea is that we replace a portion of expensive recursive calls with O(log n) map lookups, but I don't know if it's true and why it depends so much on some godforsaken compiler flags, while, as I see it, such performance swings should all follow solely from the language.
Can I haz an explanation of what happens here, and why the performance differs so vastly between ghc -O2 and plain ghc builds?
P.S. There are two requirements to the achieving of automagical memoization highlighted elsewhere on Stack Overflow:
Make a function to be memoized a top-level name.
Make a function to be memoized a monomorphic one.
In line with these requirements, I rebuilt remocollatz as follows:
remocollatz :: Int -> ([Int], Int)
remocollatz 1 = return 1
remocollatz x = mutate x
mutate :: Int -> ([Int], Int)
mutate x = remocollatz =<< mocollatz x
Now it's as top level and as monomorphic as it gets. Running time is about 11 seconds, versus the similarly monomorphized table version:
remocollatz :: Int -> ([Int], Int)
remocollatz 1 = return 1
remocollatz x = case M.lookup x (table mutate) of
Nothing -> mutate x
Just y -> y
mutate :: Int -> ([Int], Int)
mutate = \x -> remocollatz =<< mocollatz x
table :: (Int -> ([Int], Int)) -> M.Map Int ([Int], Int)
table f = M.fromList [ (x, f x) | x <- [1..size] ]
-- Running in less than 4 seconds.
I wonder why the memoization ghc is supposedly performing in the first case here is almost 3 times slower than my dumb table.
Can I haz an explanation of what happens here, and why the performance differs so vastly between ghc -O2 and plain ghc builds?
Disclaimer: this is a guess, not verified by viewing GHC core output. A careful answer would do so to verify the conjectures outlined below. You can try peering through it yourself: add -ddump-simpl to your compilation line and you will get copious output detailing exactly what GHC has done to your code.
You write:
remocollatz x = {- ... -} table mutate {- ... -}
where mutate x = remocollatz =<< mocollatz x
The expression table mutate in fact does not depend on x; but it appears on the right-hand side of an equation that takes x as an argument. Consequently, without optimizations, this table is recomputed each time remocollatz is called (presumably even from inside the computation of table mutate).
With optimizations, GHC notices that table mutate does not depend on x, and floats it to its own definition, effectively producing:
fresh_variable_name = table mutate
where mutate x = remocollatz =<< mocollatz x
remocollatz x = case M.lookup x fresh_variable_name of
{- ... -}
The table is therefore computed just once for the entire program run.
don't know why it [the performance] depends so much on some godforsaken compiler flags, while, as I see it, such performance swings should all follow solely from the language.
Sorry, but Haskell doesn't work that way. The language definition tells clearly what the meaning of a given Haskell term is, but does not say anything about the runtime or memory performance needed to compute that meaning.
Another approach to memoization that works in some situations, like this one, is to use a boxed vector, whose elements are computed lazily. The function used to initialize each element can use other elements of the vector in its calculation. As long as the evaluation of an element of the vector doesn't loop and refer to itself, just the elements it recursively depends on will be evaluated. Once evaluated, an element is effectively memoized, and this has the further benefit that elements of the vector that are never referenced are never evaluated.
The Collatz sequence is a nearly ideal application for this technique, but there is one complication. The next Collatz value(s) in sequence from a value under the limit may be outside the limit, which would cause a range error when indexing the vector. I solved this by just iterating through the sequence until back under the limit and counting the steps to do so.
The following program takes 0.77 seconds to run unoptimized and 0.30 when optimized:
import qualified Data.Vector as V
limit = 10 ^ 6 :: Int
-- The Collatz function, which given a value returns the next in the sequence.
nextCollatz val
| odd val = 3 * val + 1
| otherwise = val `div` 2
-- Given a value, return the next Collatz value in the sequence that is less
-- than the limit and the number of steps to get there. For example, the
-- sequence starting at 13 is: [13, 40, 20, 10, 5, 16, 8, 4, 2, 1], so if
-- limit is 100, then (nextCollatzWithinLimit 13) is (40, 1), but if limit is
-- 15, then (nextCollatzWithinLimit 13) is (10, 3).
nextCollatzWithinLimit val = (firstInRange, stepsToFirstInRange)
where
firstInRange = head rest
stepsToFirstInRange = 1 + (length biggerThanLimit)
(biggerThanLimit, rest) = span (>= limit) (tail collatzSeqStartingWithVal)
collatzSeqStartingWithVal = iterate nextCollatz val
-- A boxed vector holding Collatz length for each index. The collatzFn used
-- to generate the value for each element refers back to other elements of
-- this vector, but since the vector elements are only evaluated as needed and
-- there aren't any loops in the Collatz sequences, the values are calculated
-- only as needed.
collatzVec :: V.Vector Int
collatzVec = V.generate limit collatzFn
where
collatzFn :: Int -> Int
collatzFn index
| index <= 1 = 1
| otherwise = (collatzVec V.! nextWithinLimit) + stepsToGetThere
where
(nextWithinLimit, stepsToGetThere) = nextCollatzWithinLimit index
main :: IO ()
main = do
-- Use a fold through the vector to find the longest Collatz sequence under
-- the limit, and keep track of both the maximum length and the initial
-- value of the sequence, which is the index.
let (maxLength, maxIndex) = V.ifoldl' accMaxLen (0, 0) collatzVec
accMaxLen acc#(accMaxLen, accMaxIndex) index currLen
| currLen <= accMaxLen = acc
| otherwise = (currLen, index)
putStrLn $ "Max Collatz length below " ++ show limit ++ " is "
++ show maxLength ++ " at index " ++ show maxIndex
I use System.Random and System.Random.Shuffle to shuffle the order of characters in a string, I shuffle it using:
shuffle' string (length string) g
g being a getStdGen.
Now the problem is that the shuffle can result in an order that's identical to the original order, resulting in a string that isn't really shuffled, so when this happens I want to just shuffle it recursively until it hits a a shuffled string that's not the original string (which should usually happen on the first or second try), but this means I need to create a new random number generator on each recursion so it wont just shuffle it exactly the same way every time.
But how do I do that? Defining a
newg = newStdGen
in "where", and using it results in:
Jumble.hs:20:14:
Could not deduce (RandomGen (IO StdGen))
arising from a use of shuffle'
from the context (Eq a)
bound by the inferred type of
shuffleString :: Eq a => IO StdGen -> [a] -> [a]
at Jumble.hs:(15,1)-(22,18)
Possible fix:
add an instance declaration for (RandomGen (IO StdGen))
In the expression: shuffle' string (length string) g
In an equation for `shuffled':
shuffled = shuffle' string (length string) g
In an equation for `shuffleString':
shuffleString g string
= if shuffled == original then
shuffleString newg shuffled
else
shuffled
where
shuffled = shuffle' string (length string) g
original = string
newg = newStdGen
Jumble.hs:38:30:
Couldn't match expected type `IO StdGen' with actual type `StdGen'
In the first argument of `jumble', namely `g'
In the first argument of `map', namely `(jumble g)'
In the expression: (map (jumble g) word_list)
I'm very new to Haskell and functional programming in general and have only learned the basics, one thing that might be relevant which I don't know yet is the difference between "x = value", "x <- value", and "let x = value".
Complete code:
import System.Random
import System.Random.Shuffle
middle :: [Char] -> [Char]
middle word
| length word >= 4 = (init (tail word))
| otherwise = word
shuffleString g string =
if shuffled == original
then shuffleString g shuffled
else shuffled
where
shuffled = shuffle' string (length string) g
original = string
jumble g word
| length word >= 4 = h ++ m ++ l
| otherwise = word
where
h = [(head word)]
m = (shuffleString g (middle word))
l = [(last word)]
main = do
g <- getStdGen
putStrLn "Hello, what would you like to jumble?"
text <- getLine
-- let text = "Example text"
let word_list = words text
let jumbled = (map (jumble g) word_list)
let output = unwords jumbled
putStrLn output
This is pretty simple, you know that g has type StdGen, which is an instance of the RandomGen typeclass. The RandomGen typeclass has the functions next :: g -> (Int, g), genRange :: g -> (Int, Int), and split :: g -> (g, g). Two of these functions return a new random generator, namely next and split. For your purposes, you can use either quite easily to get a new generator, but I would just recommend using next for simplicity. You could rewrite your shuffleString function to something like
shuffleString :: RandomGen g => g -> String -> String
shuffleString g string =
if shuffled == original
then shuffleString (snd $ next g) shuffled
else shuffled
where
shuffled = shuffle' string (length string) g
original = string
End of answer to this question
One thing that might be relevant which I don't know yet is the difference between "x = value", "x <- value", and "let x = value".
These three different forms of assignment are used in different contexts. At the top level of your code, you can define functions and values using the simple x = value syntax. These statements are not being "executed" inside any context other than the current module, and most people would find it pedantic to have to write
module Main where
let main :: IO ()
main = do
putStrLn "Hello, World"
putStrLn "Exiting now"
since there isn't any ambiguity at this level. It also helps to delimit this context since it is only at the top level that you can declare data types, type aliases, and type classes, these can not be declared inside functions.
The second form, let x = value, actually comes in two variants, the let x = value in <expr> inside pure functions, and simply let x = value inside monadic functions (do notation). For example:
myFunc :: Int -> Int
myFunc x =
let y = x + 2
z = y * y
in z * z
Lets you store intermediate results, so you get a faster execution than
myFuncBad :: Int -> Int
myFuncBad x = (x + 2) * (x + 2) * (x + 2) * (x + 2)
But the former is also equivalent to
myFunc :: Int -> Int
myFunc x = z * z
where
y = x + 2
z = y * y
There are subtle difference between let ... in ... and where ..., but you don't need to worry about it at this point, other than the following is only possible using let ... in ..., not where ...:
myFunc x = (\y -> let z = y * y in z * z) (x + 2)
The let ... syntax (without the in ...) is used only in monadic do notation to perform much the same purpose, but usually using values bound inside it:
something :: IO Int
something = do
putStr "Enter an int: "
x <- getLine
let y = myFunc (read x)
return (y * y)
This simply allows y to be available to all proceeding statements in the function, and the in ... part is not needed because it's not ambiguous at this point.
The final form of x <- value is used especially in monadic do notation, and is specifically for extracting a value out of its monadic context. That may sound complicated, so here's a simple example. Take the function getLine. It has the type IO String, meaning it performs an IO action that returns a String. The types IO String and String are not the same, you can't call length getLine, because length doesn't work for IO String, but it does for String. However, we frequently want that String value inside the IO context, without having to worry about it being wrapped in the IO monad. This is what the <- is for. In this function
main = do
line <- getLine
print (length line)
getLine still has the type IO String, but line now has the type String, and can be fed into functions that expect a String. Whenever you see x <- something, the something is a monadic context, and x is the value being extracted from that context.
So why does Haskell have so many different ways of defining values? It all comes down to its type system, which tries really hard to ensure that you can't accidentally launch the missiles, or corrupt a file system, or do something you didn't really intend to do. It also helps to visually separate what is an action, and what is a computation in source code, so that at a glance you can tell if an action is being performed or not. It does take a while to get used to, and there are probably valid arguments that it could be simplified, but changing anything would also break backwards compatibility.
And that concludes today's episode of Way Too Much Information(tm)
(Note: To other readers, if I've said something incorrect or potentially misleading, please feel free to edit or leave a comment pointing out the mistake. I don't pretend to be perfect in my descriptions of Haskell syntax.)
this is an expansion to my last question here: basic haskell : Copying elements
however when an invalid input is added then I want it to print out an error message saying "negative value" or something similar. Is this possible in haskell?
working code:
copy :: Int->a->[a]
copy 0 _ = []
copy y a = [a]++(copy (y-1) a)
final line:
copy b c = error "negative value"
Because partial functions make me sad, I'd suggest doing something more along the lines of
copy :: Int -> a -> Maybe [a]
copy 0 _ = Just []
copy n a | n < 0 = Nothing
| otherwise = fmap (a:) (copy (n-1) a)
We've swapped out that if for a "guard"
foo bar | baz = quux
| ...
is just
foo bar = if baz then quux else ...
Note that I also changed your code a little,
[a] ++ copy (y-1) a ====> fmap (a:) (copy (y-1) a)
You can think of (:) as append.
1 : [2, 3] ==> [1, 2, 3]
It's the preferred alternative to [1] ++ [2, 3]. Say it out loud as "cons", like "construct". We can write this with an operator section
(a:) ==> \x -> a : x
Next we use this wonky fmap function. Think of fmap like this
fmap f Nothing = Nothing
fmap f (Just x) = Just (f x)
So it unwraps a Just and applies a function before rewrapping the result. So our final code returns Nothing if our number is negative, otherwise, just the list.
Why aren't I recommending error? Well because error will blow up your whole program with pretty minimal information and it's a bad idea to try to catch it. Haskell doesn't even mandate that it's possible to do so, GHC just implements error in such a way that it's possible. In other words, you have little chance to recover.
This isn't a big deal for 10 lines of code, but I've spent upwards of 6 hours searching for the offending call to a function using error. It's much faster to debug and more idiomatic haskell.
You can do this with guards
copy :: Int -> a -> [a]
copy n x
| n < 0 = error "negative value"
| n == 0 = []
| otherwise = x : copy (n - 1) x
However, if this fails then it will likely crash your program. A better way is to use the Maybe type:
copySafe :: Int -> a -> Maybe [a]
copySafe n x
| n < 0 = Nothing
| otherwise = Just (copy n x)
Then you can use it as
main = do
putStrLn "Enter a number:"
nStr <- getLine
let n = read nStr :: Int
maybeXs = copySafe n n
case maybeXs of
Nothing -> putStrLn "You entered a negative number!"
Just xs -> print xs
This style forces you to consider both cases of copySafe, either it can fail on a negative value or it can return a valid list. It doesn't crash your program and the error handling is enforced by the type system.
look at http://www.haskell.org/haskellwiki/Error_vs._Exception
for example
copy b c = if c > b then error "negativ value"
How do you increment a variable in a functional programming language?
For example, I want to do:
main :: IO ()
main = do
let i = 0
i = i + 1
print i
Expected output:
1
Simple way is to introduce shadowing of a variable name:
main :: IO () -- another way, simpler, specific to monads:
main = do main = do
let i = 0 let i = 0
let j = i i <- return (i+1)
let i = j+1 print i
print i -- because monadic bind is non-recursive
Prints 1.
Just writing let i = i+1 doesn't work because let in Haskell makes recursive definitions — it is actually Scheme's letrec. The i in the right-hand side of let i = i+1 refers to the i in its left hand side — not to the upper level i as might be intended. So we break that equation up by introducing another variable, j.
Another, simpler way is to use monadic bind, <- in the do-notation. This is possible because monadic bind is not recursive.
In both cases we introduce new variable under the same name, thus "shadowing" the old entity, i.e. making it no longer accessible.
How to "think functional"
One thing to understand here is that functional programming with pure — immutable — values (like we have in Haskell) forces us to make time explicit in our code.
In imperative setting time is implicit. We "change" our vars — but any change is sequential. We can never change what that var was a moment ago — only what it will be from now on.
In pure functional programming this is just made explicit. One of the simplest forms this can take is with using lists of values as records of sequential change in imperative programming. Even simpler is to use different variables altogether to represent different values of an entity at different points in time (cf. single assignment and static single assignment form, or SSA).
So instead of "changing" something that can't really be changed anyway, we make an augmented copy of it, and pass that around, using it in place of the old thing.
As a general rule, you don't (and you don't need to). However, in the interests of completeness.
import Data.IORef
main = do
i <- newIORef 0 -- new IORef i
modifyIORef i (+1) -- increase it by 1
readIORef i >>= print -- print it
However, any answer that says you need to use something like MVar, IORef, STRef etc. is wrong. There is a purely functional way to do this, which in this small rapidly written example doesn't really look very nice.
import Control.Monad.State
type Lens a b = ((a -> b -> a), (a -> b))
setL = fst
getL = snd
modifyL :: Lens a b -> a -> (b -> b) -> a
modifyL lens x f = setL lens x (f (getL lens x))
lensComp :: Lens b c -> Lens a b -> Lens a c
lensComp (set1, get1) (set2, get2) = -- Compose two lenses
(\s x -> set2 s (set1 (get2 s) x) -- Not needed here
, get1 . get2) -- But added for completeness
(+=) :: (Num b) => Lens a b -> Lens a b -> State a ()
x += y = do
s <- get
put (modifyL x s (+ (getL y s)))
swap :: Lens a b -> Lens a b -> State a ()
swap x y = do
s <- get
let x' = getL x s
let y' = getL y s
put (setL y (setL x s y') x')
nFibs :: Int -> Int
nFibs n = evalState (nFibs_ n) (0,1)
nFibs_ :: Int -> State (Int,Int) Int
nFibs_ 0 = fmap snd get -- The second Int is our result
nFibs_ n = do
x += y -- Add y to x
swap x y -- Swap them
nFibs_ (n-1) -- Repeat
where x = ((\(x,y) x' -> (x', y)), fst)
y = ((\(x,y) y' -> (x, y')), snd)
There are several solutions to translate imperative i=i+1 programming to functional programming. Recursive function solution is the recommended way in functional programming, creating a state is almost never what you want to do.
After a while you will learn that you can use [1..] if you need a index for example, but it takes a lot of time and practice to think functionally instead of imperatively.
Here's a other way to do something similar as i=i+1 not identical because there aren't any destructive updates. Note that the State monad example is just for illustration, you probably want [1..] instead:
module Count where
import Control.Monad.State
count :: Int -> Int
count c = c+1
count' :: State Int Int
count' = do
c <- get
put (c+1)
return (c+1)
main :: IO ()
main = do
-- purely functional, value-modifying (state-passing) way:
print $ count . count . count . count . count . count $ 0
-- purely functional, State Monad way
print $ (`evalState` 0) $ do {
count' ; count' ; count' ; count' ; count' ; count' }
Note: This is not an ideal answer but hey, sometimes it might be a little good to give anything at all.
A simple function to increase the variable would suffice.
For example:
incVal :: Integer -> Integer
incVal x = x + 1
main::IO()
main = do
let i = 1
print (incVal i)
Or even an anonymous function to do it.