Does Haskell provides a way to evaluate IO monad immediately? - haskell

I am currently making a ray tracing program with Haskell. As I am a very beginner of Haskell, I don't understand the evaluation strategy of IO monad clearly.
The problem is the memory usage of a long list of "IO a", which is "IO Vec" in my code.
Each element of the list is computed by a recursive function that compute IO Vec which represents the color for a pixel. Therefore, the length of the list is equals to width x height.
In addition, I take multiple samples for a pixels. As a whole, the function radiance to compute pixel value is called width x height x samples times.
First I was implemented this program simply by using list comprehension. The code is like,
main = do
...
let ray = (compute ray for every pair of [0..w-1], [0..h-1]
pixels <- sequence [ (sumOfRadiance scene ray samples) | ray <- rays]
In my understanding, as pixels is not used before it is written to a file, Haskell stores some data for function call inside pixels which is an array of IO Vec. Finally, memory consumption increases by calling recursive function radiance to compute pixel values.
If I change the program to evaluate the pixel value one by one using unsafePerformIO can prevent this strange use of memory space.
main = do
...
let ray = (compute ray for every pair of [0..w-1], [0..h-1]
let pixels = [ (unsafePerformIO (sumOfRadiance scene ray samples)) | ray <- rays]
I know unsafePerformIO is a bad solution, so I'd like to know if Haskell provides another way to evaluate inside of IO monad immediately. The following is the whole of my code (Sorry, it's a bit long...)
Thank you for your help.
-- Small path tracing with Haskell
import System.Environment
import System.Random.Mersenne
import System.IO.Unsafe
import Control.Monad
import Codec.Picture
import Data.Time
import qualified Data.Word as W
import qualified Data.Vector.Storable as V
-- Parameters
eps :: Double
eps = 1.0e-4
inf :: Double
inf = 1.0e20
nc :: Double
nc = 1.0
nt :: Double
nt = 1.5
-- Vec
data Vec = Vec (Double, Double, Double) deriving (Show)
instance (Num Vec) where
(Vec (x, y, z)) + (Vec (a, b, c)) = Vec (x + a, y + b, z + c)
(Vec (x, y, z)) - (Vec (a, b, c)) = Vec (x - a, y - b, z - c)
(Vec (x, y, z)) * (Vec (a, b, c)) = Vec (x * a, y * b, z * c)
abs = undefined
signum = undefined
fromInteger x = Vec (dx, dx, dx) where dx = fromIntegral x
x :: Vec -> Double
x (Vec (x, _, _)) = x
y :: Vec -> Double
y (Vec (_, y, _)) = y
z :: Vec -> Double
z (Vec (_, _, z)) = z
mul :: Vec -> Double -> Vec
mul (Vec (x, y, z)) s = Vec (x * s, y * s, z * s)
dot :: Vec -> Vec -> Double
dot (Vec (x, y, z)) (Vec (a, b, c)) = x * a + y * b + z * c
norm :: Vec -> Vec
norm (Vec (x, y, z)) = Vec (x * invnrm, y * invnrm, z * invnrm)
where invnrm = 1 / sqrt (x * x + y * y + z * z)
cross :: Vec -> Vec -> Vec
cross (Vec (x, y, z)) (Vec (a, b, c)) = Vec (y * c - b * z, z * a - c * x, x * b - a * y)
-- Ray
data Ray = Ray (Vec, Vec) deriving (Show)
org :: Ray -> Vec
org (Ray (org, _)) = org
dir :: Ray -> Vec
dir (Ray (_, dir)) = dir
-- Material
data Refl = Diff
| Spec
| Refr
deriving Show
-- Sphere
data Sphere = Sphere (Double, Vec, Vec, Vec, Refl) deriving (Show)
rad :: Sphere -> Double
rad (Sphere (rad, _, _, _, _ )) = rad
pos :: Sphere -> Vec
pos (Sphere (_ , p, _, _, _ )) = p
emit :: Sphere -> Vec
emit (Sphere (_ , _, e, _, _ )) = e
col :: Sphere -> Vec
col (Sphere (_ , _, _, c, _ )) = c
refl :: Sphere -> Refl
refl (Sphere (_ , _, _, _, refl)) = refl
intersect :: Sphere -> Ray -> Double
intersect sp ray =
let op = (pos sp) - (org ray)
b = op `dot` (dir ray)
det = b * b - (op `dot` op) + ((rad sp) ** 2)
in
if det < 0.0
then inf
else
let sqdet = sqrt det
t1 = b - sqdet
t2 = b + sqdet
in ansCheck t1 t2
where ansCheck t1 t2
| t1 > eps = t1
| t2 > eps = t2
| otherwise = inf
-- Scene
type Scene = [Sphere]
sph :: Scene
sph = [ Sphere (1e5, Vec ( 1e5+1, 40.8, 81.6), Vec (0.0, 0.0, 0.0), Vec (0.75, 0.25, 0.25), Diff) -- Left
, Sphere (1e5, Vec (-1e5+99, 40.8, 81.6), Vec (0.0, 0.0, 0.0), Vec (0.25, 0.25, 0.75), Diff) -- Right
, Sphere (1e5, Vec (50.0, 40.8, 1e5), Vec (0.0, 0.0, 0.0), Vec (0.75, 0.75, 0.75), Diff) -- Back
, Sphere (1e5, Vec (50.0, 40.8, -1e5+170), Vec (0.0, 0.0, 0.0), Vec (0.0, 0.0, 0.0), Diff) -- Front
, Sphere (1e5, Vec (50, 1e5, 81.6), Vec (0.0, 0.0, 0.0), Vec (0.75, 0.75, 0.75), Diff) -- Bottom
, Sphere (1e5, Vec (50,-1e5+81.6,81.6), Vec (0.0, 0.0, 0.0), Vec (0.75, 0.75, 0.75), Diff) -- Top
, Sphere (16.5, Vec (27, 16.5, 47), Vec (0.0, 0.0, 0.0), Vec (1,1,1) `mul` 0.999, Spec) -- Mirror
, Sphere (16.5, Vec (73, 16.5, 78), Vec (0.0, 0.0, 0.0), Vec (1,1,1) `mul` 0.999, Refr) -- Glass
, Sphere (600, Vec (50, 681.6 - 0.27, 81.6), Vec (12, 12, 12), Vec (0, 0, 0), Diff) ] -- Light
-- Utility functions
clamp :: Double -> Double
clamp = (max 0.0) . (min 1.0)
isectWithScene :: Scene -> Ray -> (Double, Int)
isectWithScene scene ray = foldr1 (min) $ zip [ intersect sph ray | sph <- scene ] [0..]
nextDouble :: IO Double
nextDouble = randomIO
lambert :: Vec -> Double -> Double -> (Vec, Double)
lambert n r1 r2 =
let th = 2.0 * pi * r1
r2s = sqrt r2
w = n
u = norm $ (if (abs (x w)) > eps then Vec (0, 1, 0) else Vec (1, 0, 0)) `cross` w
v = w `cross` u
uu = u `mul` ((cos th) * r2s)
vv = v `mul` ((sin th) * r2s)
ww = w `mul` (sqrt (1.0 - r2))
rdir = norm (uu + vv + ww)
in (rdir, 1)
reflect :: Vec -> Vec -> (Vec, Double)
reflect v n =
let rdir = v - (n `mul` (2.0 * n `dot` v))
in (rdir, 1)
refract :: Vec -> Vec -> Vec -> Double -> (Vec, Double)
refract v n orn rr =
let (rdir, _) = reflect v orn
into = (n `dot` orn) > 0
nnt = if into then (nc / nt) else (nt / nc)
ddn = v `dot` orn
cos2t = 1.0 - nnt * nnt * (1.0 - ddn * ddn)
in
if cos2t < 0.0
then (rdir, 1.0)
else
let tdir = norm $ ((v `mul` nnt) -) $ n `mul` ((if into then 1 else -1) * (ddn * nnt + (sqrt cos2t)))
a = nt - nc
b = nt + nc
r0 = (a * a) / (b * b)
c = 1.0 - (if into then -ddn else (tdir `dot` n))
re = r0 + (1 - r0) * (c ** 5)
tr = 1.0 - re
pp = 0.25 + 0.5 * re
in
if rr < pp
then (rdir, (pp / re))
else (tdir, ((1.0 - pp) / tr))
radiance :: Scene -> Ray -> Int -> IO Vec
radiance scene ray depth = do
let (t, i) = (isectWithScene scene ray)
if inf <= t
then return (Vec (0, 0, 0))
else do
r0 <- nextDouble
r1 <- nextDouble
r2 <- nextDouble
let obj = (scene !! i)
let c = col obj
let prob = (max (x c) (max (y c) (z c)))
if depth >= 5 && r0 >= prob
then return (emit obj)
else do
let rlt = if depth < 5 then 1 else prob
let f = (col obj)
let d = (dir ray)
let x = (org ray) + (d `mul` t)
let n = norm $ x - (pos obj)
let orn = if (d `dot` n) < 0.0 then n else (-n)
let (ndir, pdf) = case (refl obj) of
Diff -> (lambert orn r1 r2)
Spec -> (reflect d orn)
Refr -> (refract d n orn r1)
nextRad <- (radiance scene (Ray (x, ndir)) (succ depth))
return $ ((emit obj) + ((f * nextRad) `mul` (1.0 / (rlt * pdf))))
toByte :: Double -> W.Word8
toByte x = truncate (((clamp x) ** (1.0 / 2.2)) * 255.0) :: W.Word8
accumulateRadiance :: Scene -> Ray -> Int -> Int -> IO Vec
accumulateRadiance scene ray d m = do
let rays = take m $ repeat ray
pixels <- sequence [radiance scene r 0 | r <- rays]
return $ (foldr1 (+) pixels) `mul` (1 / fromIntegral m)
main :: IO ()
main = do
args <- getArgs
let argc = length args
let w = if argc >= 1 then (read (args !! 0)) else 400 :: Int
let h = if argc >= 2 then (read (args !! 1)) else 300 :: Int
let spp = if argc >= 3 then (read (args !! 2)) else 4 :: Int
startTime <- getCurrentTime
putStrLn "-- Smallpt.hs --"
putStrLn $ " width = " ++ (show w)
putStrLn $ " height = " ++ (show h)
putStrLn $ " spp = " ++ (show spp)
let dw = fromIntegral w :: Double
let dh = fromIntegral h :: Double
let cam = Ray (Vec (50, 52, 295.6), (norm $ Vec (0, -0.042612, -1)));
let cx = Vec (dw * 0.5135 / dh, 0.0, 0.0)
let cy = (norm $ cx `cross` (dir cam)) `mul` 0.5135
let dirs = [ norm $ (dir cam) + (cy `mul` (y / dh - 0.5)) + (cx `mul` (x / dw - 0.5)) | y <- [dh-1,dh-2..0], x <- [0..dw-1] ]
let rays = [ Ray ((org cam) + (d `mul` 140.0), (norm d)) | d <- dirs ]
let pixels = [ (unsafePerformIO (accumulateRadiance sph r 0 spp)) | r <- rays ]
let pixelData = map toByte $! pixels `seq` (foldr (\col lst -> [(x col), (y col), (z col)] ++ lst) [] pixels)
let pixelBytes = V.fromList pixelData :: V.Vector W.Word8
let img = Image { imageHeight = h, imageWidth = w, imageData = pixelBytes } :: Image PixelRGB8
writePng "image.png" img
endTime <- getCurrentTime
print $ diffUTCTime endTime startTime

First, I think there is an error. When you talk about going from
pixels <- sequence [ (sumOfRadiance scene ray samples) | ray <- rays]
to
pixels <- sequence [ (unsafePerformIO (sumOfRadiance scene ray samples)) | ray <- rays]
that doesn't make sense. The types shouldn't match up -- sequence only makes sense if you are combining a bunch of things of type m a. It would be correct to do
let pixels = [ unsafePerformIO (sumOfRadiance scene ray samples) | ray <- rays ]
I will somewhat cavalierly assume that that is what you did and you simply made a mistake when entering your question.
If this is the case, then what you are actually looking for is a way to execute IO actions more lazily, not more immediately. The sequence call forces all the actions to be run right then, whereas the unsafePerformIO version simply creates a list of un-run actions (and indeed the list itself is generated lazily so it doesn't exist all at once), and the actions are run individually as their results are needed.
It appears that the reason you need IO is to generate random numbers. Randomness can be kind of a pain -- usually MonadRandom does the job, but it still creates a sequential dependence between actions and may still not be lazy enough (I'd give it a try -- if you use it you get reproducibility -- the same seed gives the same results, even after refactorings that respect the monad laws).
If MonadRandom doesn't work and you need to generate random numbers in a more on-demand way, the way would be to make your own randomness monad which does the same thing as your unsafePerformIO solution, but in a way that is properly encapsulated. I'm going to show you the way I consider to be the Haskell Way To Cheat. First, a lovely pure implementation sketch:
-- A seed tells you how to generate random numbers
data Seed = ...
splitSeed :: Seed -> (Seed, Seed)
random :: Seed -> Double
-- A Cloud is a probability distribution of a's, or an a which
-- depends on a random seed. This monad is just as lazy as a
-- pure computation.
newtype Cloud a = Cloud { runCloud :: Seed -> a }
deriving (Functor)
instance Monad Cloud where
return = Cloud . const
m >>= f = Cloud $ \seed ->
let (seed1, seed2) = splitSeed seed in
runCloud (f (runCloud m seed1)) seed2
(I think I got that right. The point is that at every bind you split the seed in two and pass one to the left and the other to the right.)
Now this is a perfectly pure implementation of randomness... with a couple catches. (1) there is no non-trivial splitSeed which will strictly respect the monad laws, and (2) even if we allow the laws to be broken, random number generators based on splitting can be pretty slow. But if we give up determinism, if all we care about is that we get a good sampling from the distribution rather than the exact same result, then we don't need to strictly respect the monad laws. And at that point we cheat and pretend there is a suitable Seed type:
data Seed = Seed
splitSeed Seed = (Seed, Seed)
-- Always NOINLINE functions with unsafePerformIO to keep the
-- optimizer from messing with you.
{-# NOINLINE random #-}
random Seed = unsafePerformIO randomIO
We should hide this inside a module to keep the abstraction barrier clear. Cloud and runCloud should not be exposed since they allow us to violate purity; expose only
runCloudIO :: Cloud a -> IO a
runCloudIO = return . runCloud
which doesn't technically need IO, but communicates that this will not be deterministic. Then you can build up whatever you need as a value in the Cloud monad, and run it once in your main program.
You might ask why we have a Seed type at all if it doesn't have any information. Well, I think splitSeed is just a nod to purity and isn't actually doing anything -- you could remove it -- but we need Cloud to be a function type so that the implicit caching of laziness doesn't break our semantics. Otherwise
let foo = random in liftM2 (,) foo foo
would always return a pair with two identical components, since the random value was really associated with foo. I am not sure about these things since at this point we are at war with the optimizer, it takes some experimentation.
Happy cheating. :-)

Related

Memoization with Monad.Memo for mutual recursion in Haskell

I'm doing some dynamic programming in Haskell with mutual recursion implementation.
I decided to speed things up using memoization.
Monad.Memo offers MemoT transformer for that exact case. But it uses Map as internal representation for stored values. And while this gave me order of magnitude speed boost it is still not enough.
While lib supports Array-based and Vector-based implementation as internal storage it only works for simple recursion and I did not found any transformers like MemoT to use it for mutual recursion.
What is the best way to do mutual recursion memoization with efficient vector based internal representation (if any)?
My next question is about memoization effect. So I expected my function to take more time during first run and much less during consecutive runs. But what I found running it in ghci the time it takes each time is the same. So no difference between first and second run. I measured time as follows:
timeit $ print $ dynamic (5,5)
With dynamic being my function.
The full implementation is as follows:
import Control.Monad.Memo
import Control.Monad.Identity
type Pos = (Int, Int)
type MemoQ = MemoT (Int, Int, Int) [Int]
type MemoV = MemoT (Int, Int, Int) Int
type MemoQV = MemoQ (MemoV Identity)
-- we are moving to (0,0) as we can always shift the world by substituting variables
-- due to symmetry of cost function it is enougth to solve for only positive x and y
dynamic :: Pos -> [Int]
dynamic (x, y) = lastUnique $ map (evalQ x y) [1 ..]
where lastUnique (x0:x1:xs) | x0 == x1 = x0
| otherwise = lastUnique (x1:xs)
evalQ :: Int -> Int -> Int -> [Int]
evalQ x y n = startEvalMemo . startEvalMemoT $ fqmon x y n
fqmon :: Int -> Int -> Int -> MemoQV [Int]
fqmon _ _ 0 = return [0,0,0,0]
fqmon x y n = do
let pts = neighbours (x, y)
let v = for3 memol1 fvmon n
let c = cost (x, y)
let q = fmap (c +) . uncurry v
traverse q pts
fvmon :: Int -> Int -> Int -> MemoQV Int
fvmon _ 0 0 = return 0
fvmon 0 x y = return $ cost (x, y)
fvmon n x y | limit = return 1000000
| otherwise = liftM minimum $ for3 memol0 fqmon x' y' (n - 1)
where x' = abs x
y' = abs y
limit = x' > 25 || y' > 25
cost :: Pos -> Int
cost (x, y) = abs x + abs y
neighbours :: Pos -> [Pos]
neighbours (x, y) = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
Added:
According to #liqui comment I tried memcombinators.
So first is the non memoized initial implementation:
type Pos = (Int, Int)
dynamic :: Int -> Int -> [Int]
dynamic x y = lastUnique $ map (fq x y) [1 ..]
where lastUnique (x0:x1:xs) | x0 == x1 = x0
| otherwise = lastUnique (x1:xs)
fq :: Int -> Int -> Int -> [Int]
fq _ _ 0 = [0, 0, 0, 0] -- Q at 0 step is 0 in all directions
fq x y n = (cost (x, y) +) . (uncurry $ fv n) <$> neighbours (x, y)
fv :: Int -> Int -> Int -> Int
fv _ 0 0 = 0 -- V at (0, 0) is 0 at any atep
fv 0 x y = cost (x, y) -- V at 0 step is a cost
fv n x y = minimum $ fq x y (n - 1)
cost :: Pos -> Int
cost (x, y) = abs x + abs y
neighbours :: Pos -> [Pos]
neighbours (x, y) = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
Then my attempt to memization (only changed part):
dynamic :: Int -> Int -> [Int]
dynamic x y = lastUnique $ map (fqmem x y) [1 ..]
where lastUnique (x0:x1:xs) | x0 == x1 = x0
| otherwise = lastUnique (x1:xs)
-- memoizing version of fq
fqmem :: Int -> Int -> Int -> [Int]
fqmem x y n = fqmem' x y n
where fqmem' = memo3 integral integral integral fq
-- memoizing version of fv
fvmem :: Int -> Int -> Int -> Int
fvmem n x y = fvmem' n x y
where fvmem' = memo3 integral integral integral fv
fq :: Int -> Int -> Int -> [Int]
fq _ _ 0 = [0, 0, 0, 0] -- Q at 0 step is 0 in all directions
fq x y n = (cost (x, y) +) . (uncurry $ fvmem n) <$> neighbours (x, y)
fv :: Int -> Int -> Int -> Int
fv _ 0 0 = 0 -- V at (0, 0) is 0 at any atep
fv 0 x y = cost (x, y) -- V at 0 step is a cost
fv n x y = minimum $ fqmem x y (n - 1)
The result a bit of paradox. It is 3 time slower than non memoized recursive implementation. Memoizing only one function (namely fq) and not touching fv gives results 2 times slower. The more I memoize with memcombinators the slower the computation. And again no difference between first and second invocation.
Also the last question. What is the rationale for choosing between Monad.Memo or memcombinators or MemotTrie? There is a point on using last 2 in comments. What are the situations when Monad.Memo is a better choice?
Finally MemoTrie did the job.
At first invocation it works as fast (possibly much faster) than Monad.Memo and at consecutive invocations it take virtually no time!
And tha change in code is trivial compared to monadic approach:
import Data.MemoTrie
type Pos = (Int, Int)
-- we are moving to (0,0) as we can always shift the world by substituting variables
-- due to symmetry it is enougth to solve for only positive x and y
dynamic :: Int -> Int -> [Int]
dynamic x y = lastUnique $ map (fqmem x y) [1 ..]
where lastUnique (x0:x1:xs) | x0 == x1 = x0
| otherwise = lastUnique (x1:xs)
fqmem = memo3 fq
fvmem = memo3 fv
fq :: Int -> Int -> Int -> [Int]
fq _ _ 0 = [0, 0, 0, 0] -- Q at 0 step is 0 in all directions
fq x y n = (cost (x, y) +) . (uncurry $ fvmem n) <$> neighbours (x, y)
fv :: Int -> Int -> Int -> Int
fv _ 0 0 = 0 -- V at (0, 0) is 0 at any atep
fv 0 x y = cost (x, y) -- V at 0 step is a cost
fv n x y = minimum $ fqmem x y (n - 1)
cost :: Pos -> Int
cost (x, y) = abs x + abs y
neighbours :: Pos -> [Pos]
neighbours (x, y) = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
Still I would like to know what is the benefits of using Monad.Memo and what are use cases for that? Or it becomes obsolete with MemoTrie?
Why Memocombinators did not worked for me?
What is the rule of thumb on choosing between Monad.Memo, Memocombinators or MemoTrie?

Calculating position via net force, different dts yield different answers

I'm trying to write a simulator for charged and massed objects based on just calculating the net force on each object then finding the change in position across the period of time specified by the user.
However, I'm finding that when I change the dt, the change in position is drastic, when it shouldn't change significantly, decreasing the dt should just let the position converge on the correct answer.
For instance, with objects at the Cartesian coordinates (1, 0, 0) and (-1, 0, 0), with masses of 9e-31 (mass of electron) and a charge of 1 Coulomb (not the charge of an electron, I know), run for 0.1 seconds and a dt of 0.01 seconds, there is a total change of position of 2048 meters for each object. However, run for 0.1 seconds and a dt of 0.001 seconds, there is a change in position of about 1.3e30 meters. This seems rather outrageous to me, but I can't find any issues in the parts that use dt.
The code I'm using (c/p'd to avoid any possible changes)
import Data.List
main = print $ mainprog
where
mainprog = runUniverse makeUniverse 1 0.1
type Length = Double
type Mass = Double
type Charge = Double
type Time = Double
type Vector = (Double, Double, Double)
type Position = Vector
type Velocity = Vector
type Acceleration = Vector
type Force = Vector
data Widget = Widget {pos :: Position, mass :: Double, charge :: Double, velocity :: Velocity} deriving (Eq, Show, Read)
--utils
toScalar :: Vector -> Double
toScalar (x, y, z) = sqrt (x ^^ 2 + y ^^ 2 + z ^^ 2)
toUnit :: Vector -> Vector
toUnit (x, y, z) = (x / scalar, y / scalar, z / scalar)
where
scalar = toScalar (x, y, z)
add :: Vector -> Vector -> Vector
add (x1, y1, z1) (x2, y2, z2) = (x1 + x2, y1 + y2, z1 + z2)
mult :: Vector -> Double -> Vector
mult (x, y, z) k = (k * x, k * y, k * z)
diff :: Vector -> Vector -> Vector
diff (x1, y1, z1) (x2, y2, z2) = (x1 - x2, y1 - y2, z1 - z2)
--calcs
gForce :: Widget -> Widget -> Force
gForce (Widget pos1 mass1 _ _) (Widget pos2 mass2 _ _) = mult unitForce scalarForce
where
unitForce = toUnit posdiff
scalarForce = (g * mass1 * mass2) / (radius ^^ 2)
g = 6.674e-11
radius = toScalar posdiff
posdiff = diff pos1 pos2
eForce :: Widget -> Widget -> Force
eForce (Widget pos1 _ charge1 _) (Widget pos2 _ charge2 _) = mult unitForce scalarForce
where
unitForce = (toUnit posdiff)
--necessary to determine attraction vs repulsion, whereas gravitational is always attractive
scalarForce = ((abs (k_C * charge1 * charge2)) / (radius ^^ 2)) * (signum charge1) * (signum charge2)
k_C = 8.988e9
radius = toScalar posdiff
posdiff = diff pos1 pos2
netForce :: [Force] -> Force
netForce = foldl add (0, 0, 0)
toAccel :: Force -> Widget -> Acceleration
toAccel f (Widget _ mass _ _) = mult f (1/mass)
newVeloc :: Velocity -> Acceleration -> Time -> Velocity
newVeloc v a dt = add v (mult a dt)
newPos :: Vector -> Velocity -> Time -> Vector
newPos s v dt = add s (mult v dt)
newWidget :: Widget -> Position -> Velocity -> Widget
newWidget (Widget pos1 mass charge vel1) pos2 vel2 = Widget pos2 mass charge vel2
tUniverse :: [Widget] -> Time -> [Widget]
tUniverse widgets dt = zipWith3 newWidget widgets poses vels
where
netMassForces = map (\w -> gForcePrime w (widgets \\ [w])) widgets
gForcePrime w ws = netForce $ map (gForce w) ws
netElectricForces = map (\w -> eForcePrime w (widgets \\ [w])) widgets
eForcePrime w ws = netForce $ map (eForce w) ws
volds = map velocity widgets
polds = map pos widgets
accels = zipWith toAccel (map netForce (zipWith (\a b -> a : [b]) netMassForces netElectricForces)) widgets
vels = zipWith (\v a -> newVeloc v a dt) volds accels
poses = zipWith (\s v -> newPos s v dt) polds vels
makeUniverse :: [Widget]
makeUniverse = [(Widget (-1, 0, 0) 1 1 (0, 0, 0)), (Widget (1, 0, 0) 1 1 (0, 0, 0))]
runUniverse :: [Widget] -> Time -> Time -> [Widget]
runUniverse ws t dt
| t <= 0 = ws
| otherwise = runUniverse (tUniverse (inelasticCollide ws) dt) (t-dt) dt
inelasticCollide :: [Widget] -> [Widget]
inelasticCollide [] = []
inelasticCollide (w:[]) = [w]
inelasticCollide (w:ws) = (combine w (sameposes w ws)) : (inelasticCollide $ ws \\ (sameposes w ws))
where
sameposes w ws = filter (\w' -> pos w == pos w') ws
combine :: Widget -> [Widget] -> Widget
combine = foldl (\(Widget pos mass1 charge1 veloc1) (Widget _ mass2 charge2 veloc2) -> Widget pos (charge1 + charge2) (mass1 + mass2) (newveloc mass1 mass2 veloc1 veloc2))
--inelastic collision, m1v1 + m2v2 = m3v3 therefore v3 = (m1v1 + m2v2)/(m1 + m2)
newveloc m1 m2 v1 v2 = ((v1 `mult` m1) `add` (v2 `mult` m2)) `mult` (1 / (m1 + m2))
The issue I know is in the tUniverse function, probably in some calculation of either acceleration, velocity, or position (accels, vels, or poses). I've tried changing toAccel, newVeloc, and newPos by multiplying each by the inverse of dt, but it didn't significantly change the outputs.
Feel free to ignore inelasticCollide, I could probably replace it with the id function, but I just left it in because it will be relevant at some point.
EDIT: I've updated the code to fix the incorrect calculation of acceleration, the switching of mass and charge in inelasticallyCollide, and the double counting with dpos/dvel, but I'm still finding that I'm getting an error of by a magnitude of 10. For instance, with a charge of 1 C for each, I got ~10^8 for dt = 0.01 and ~10^7 for dt = 0.1 and with a charge of 0.01 C for each, ~250 for dt = 0.01 and ~65 for dt = 0.1.
It seems the "obvious" issue is that newWidget assumes dpos and dvel are deltas, but when it's called in tUniverse poses and vels have actually already done the addition.
To debug I had rewritten things to use newtypes thinking that perhaps there was a mismatch somewhere. There did turn out to be an issue of masses and charges being transposed in inelasticCollide but that didn't matter for my test case. The way I found this issue was by adding the traces and seeing that the object's position component doubled each tick when the velocity component was 1.
I have no idea whether any calculations are accurate otherwise.
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
import Data.List
import Debug.Trace (trace)
main = print $ runUniverse makeUniverse 0.1 0.01
newtype Length = Length {unLength::Double}
newtype Mass = Mass {unMass::Double} deriving (Num,Eq,Show)
newtype Charge = Charge {unCharge::Double} deriving (Num,Eq,Show)
newtype Time = Time {unTime::Double} deriving (Num,Eq,Ord,Fractional)
type Vector = (Double,Double,Double)
newtype Position = Position {unPosition::Vector} deriving (Eq,Show)
newtype Velocity = Velocity {unVelocity::Vector} deriving (Eq,Show)
newtype Acceleration = Acceleration {unAcceleration::Vector}
newtype Force = Force {unForce::Vector} deriving (Eq,Show)
data Widget = Widget {pos :: Position, mass :: Mass, charge :: Charge, velocity :: Velocity} deriving (Eq, Show)
--utils
toScalar :: Vector -> Double
toScalar (x, y, z) = sqrt (x ^^ 2 + y ^^ 2 + z ^^ 2)
toUnit :: Vector -> Vector
toUnit (x, y, z) = (x / scalar, y / scalar, z / scalar)
where
scalar = toScalar (x, y, z)
add :: Vector -> Vector -> Vector
add (x1, y1, z1) (x2, y2, z2) = (x1 + x2, y1 + y2, z1 + z2)
mult :: Vector -> Double -> Vector
mult (x, y, z) k = (k * x, k * y, k * z)
diff :: Vector -> Vector -> Vector
diff (x1, y1, z1) (x2, y2, z2) = (x1 - x2, y1 - y2, z1 - z2)
--calcs
gForce :: Widget -> Widget -> Force
gForce (Widget (Position pos1) (Mass mass1) _ _) (Widget (Position pos2) (Mass mass2) _ _) = Force (mult unitForce scalarForce)
where
unitForce = toUnit posdiff
scalarForce = (g * mass1 * mass2) / (radius ^^ 2)
g = 6.674e-11
radius = toScalar posdiff
posdiff = diff pos1 pos2
eForce :: Widget -> Widget -> Force
eForce (Widget (Position pos1) _ (Charge charge1) _) (Widget (Position pos2) _ (Charge charge2) _) = Force (mult unitForce scalarForce)
where
unitForce = (toUnit posdiff)
--necessary to determine attraction vs repulsion, whereas gravitational is always attractive
scalarForce = ((abs (k_C * charge1 * charge2)) / (radius ^^ 2)) * (signum charge1) * (signum charge2)
k_C = 8.988e9
radius = toScalar posdiff
posdiff = diff pos1 pos2
netForce :: [Force] -> Force
netForce = Force . foldl add (0,0,0) . map unForce
toAccel :: Force -> Widget -> Acceleration
toAccel f (Widget _ mass _ _) = Acceleration (mult (unForce f) (unMass mass))
newVeloc :: Velocity -> Acceleration -> Time -> Velocity
newVeloc v a dt = Velocity (add (unVelocity v) (mult (unAcceleration a) (unTime dt)))
newPos :: Position -> Velocity -> Time -> Position
newPos s v dt = Position (add (unPosition s) (mult (unVelocity v) (unTime dt)))
newWidget :: Widget -> Position -> Velocity -> Widget
newWidget w#(Widget pos1 _ _ vel1) dpos dvel = w { pos=Position ((unPosition dpos)),velocity=Velocity ((unVelocity dvel)) }
tUniverse :: [Widget] -> Time -> [Widget]
tUniverse widgets dt = zipWith3 newWidget widgets (trace (show poses) poses) (trace (show vels) vels)
where
netMassForces = map (\w -> gForcePrime w (widgets \\ [w])) widgets
gForcePrime w ws = netForce $ map (gForce w) ws
netElectricForces = map (\w -> eForcePrime w (widgets \\ [w])) widgets
eForcePrime w ws = netForce $ map (eForce w) ws
volds = map velocity widgets
polds = map pos widgets
accels = zipWith toAccel (map netForce (zipWith (\a b -> a : [b]) netMassForces netElectricForces)) widgets
vels = zipWith (\v a -> newVeloc v a dt) volds accels
poses = zipWith (\s v -> newPos s v dt) polds vels
makeUniverse :: [Widget]
makeUniverse = [Widget (Position (1,0,0)) (Mass 0) (Charge 0) (Velocity (1,0,0))] -- , (Widget (1, 0, 0) 9e-31 1 (0, 0, 0))]
runUniverse :: [Widget] -> Time -> Time -> [Widget]
runUniverse ws t dt
| t < 0 = ws
| otherwise = runUniverse (tUniverse (inelasticCollide ws) dt) (t-dt) dt
inelasticCollide :: [Widget] -> [Widget]
inelasticCollide [] = []
inelasticCollide (w:[]) = [w]
inelasticCollide (w:ws) = (combine w (sameposes w ws)) : (inelasticCollide $ ws \\ (sameposes w ws))
where
sameposes w ws = filter (\w' -> pos w == pos w') ws
combine :: Widget -> [Widget] -> Widget
combine = foldl (\(Widget pos mass1 charge1 veloc1) (Widget _ mass2 charge2 veloc2) -> Widget pos (mass1 + mass2) (charge1 + charge2) (Velocity (newveloc (unMass mass1) (unMass mass2) (unVelocity veloc1) (unVelocity veloc2))))
--inelastic collision, m1v1 + m2v2 = m3v3 therefore v3 = (m1v1 + m2v2)/(m1 + m2)
newveloc m1 m2 v1 v2 = ((v1 `mult` m1) `add` (v2 `mult` m2)) `mult` (1 / (m1 + m2))

haskell takes a (x,y) and return a infinite list with the a definition of Orbit(x,y) [duplicate]

I am trying to define a function that accepts a point (x,y) as input, and returns an infinite list corresponding to recursively calling
P = (u^2 − v^2 + x, 2uv + y)
The initial values of u and v are both 0.
The first call would be
P = (0^2 - 0^2 + 1, 2(0)(0) + 2) = (1,2)
Then that resulting tuple (1,2) would be the next values for u and v, so then it would be
P = (1^2 - 2^2 + 1, 2(1)(2) + 2) = (-2,6)
and so on.
I'm trying to figure out how to code this in Haskell. This is what I have so far:
o :: Num a =>(a,a) -> [(a,a)]
o (x,y) = [(a,b)| (a,b)<- [p(x,y)(x,y)]]
where p(x,y)(u,v) = ((u^2)-(v^2)+x,(2*u*v)+y)
I'm really not sure how to make this work. Any help would be appreciated!
Let's first ignore the exact question you have, and focus on getting the loop working. What you want, essentially, is to have something that takes some initial value iv (namely, (0, 0) for (u, v)), and returns the list
f iv : f (f iv) : f (f (f iv)) : f (f (f (f iv))) : ...
for some function f (constructed from your p and (x, y)). Moreover, you want the result to reuse the previously computed elements of the list. If I would write a function myself that does this, it might looke like this (but maybe with some different names):
looper :: (a -> a) -> a -> [a]
looper f iv = one_result : more_results
where
one_result = f iv
more_results = looper f one_result
But, of course, I would first look if a function with that type exists. It does: it's called Data.List.iterate. The only thing it does wrong is the first element of the list will be iv, but that can be easily fixed by using tail (which is fine here: as long as your iteration function terminates, iterate will always generate an infinite list).
Let's now get back to your case. We established that it'll generally look like this:
o :: Num a => (a, a) -> [(a, a)]
o (x, y) = tail (iterate f iv)
where
f (u, v) = undefined
iv = undefined
As you indicated, the initial value of (u, v) is (0, 0), so that's what our definition of iv will be. f now has to call p with the (x, y) from o's argument and the (u, v) for that iteration:
o :: Num a => (a, a) -> [(a, a)]
o (x, y) = tail (iterate f iv)
where
f (u, v) = p (x, y) (u, v)
iv = (0, 0)
p = undefined
It's as simple as that: the (x, y) from o's definition is actually in scope in the where-clause. You could even decide to merge f and p, and end up with
o :: Num a => (a, a) -> [(a, a)]
o (x, y) = tail (iterate p iv)
where
iv = (0, 0)
p (u, v) = (u^2 - v^2 + x, 2 * u * v + y)
Also, may I suggest that you use Data.Complex for your application? This makes the constraints on a a bit stricter (you need RealFloat a, because of Num.signum), but in my opinion, it makes your code much easier to read:
import Data.Complex
import Data.List (iterate)
{- ... -}
o :: Num (Complex a) => Complex a -> [Complex a]
o c = tail (iterate p iv)
where
iv = 0 -- or "0 :+ 0", if you want to be explicit
p z = z^2 + c
You want:
To construct a list [(u, v)] with the head of this list equal (0, 0)
And then map this list with the function \(u, v) -> (u^2 - v^2 + x, 2 * u * v + y), appending results of this function to the list.
We can write this function as described:
func :: (Num t) => (t, t) -> [(t, t)]
func (x, y) = (0, 0) : map functionP (func (x, y))
where functionP (u, v) = (u^2 - v^2 + x, 2 * u * v + y)
GHCi > take 5 $ func (1, 2)
> [(0,0),(1,2),(-2,6),(-31,-22),(478,1366)]

Optimizing this haskell linear algebra code

I have this Haskell code for testing collisions between shapes (it depends on the linear and lens packages)
import Linear hiding (trace)
import Control.Lens.Getter ((^.))
type Vec3 = V3 Float
data Tri = Tri Vec3 Vec3 Vec3
data Box = Box Vec3 Vec3
-- | Any primitive collidable in 3D
class Collide a where
axes :: a -> [Vec3] -- | return all potentially separating axes, normalized
-- | project the shape onto the normalized axis, giving a 1D line segment
project :: a -> Vec3 -> (Float, Float)
intersects :: (Collide a, Collide b) => a -> b -> Bool
intersects a b = isOverlap `all` axs
where axs = axes a ++ axes b
--do the line segments overlap?
overlaps (l, r) (l', r') = l' <= r && r' >= l
isOverlap ax = project a ax `overlaps` project b ax
{-# SPECIALIZE intersects :: Box -> Tri -> Bool #-}
instance Collide Tri where
--face normal, and edge normals
{-# INLINE axes #-}
axes (Tri q r s) = map normalize (face : edges)
where face = (q ^-^ r) `cross` (s ^-^ r)
edges = map (face `cross`) [q ^-^ r, r ^-^ s, s ^-^ q]
{-# INLINE project #-}
project (Tri q r s) ax = (minimum projs, maximum projs)
where projs = map (dot ax) [q, r, s]
instance Collide Box where
{-# INLINE axes #-}
axes _ = basis --it's axis aligned!
{-# INLINE project #-}
project (Box a b) ax#(V3 x y z) = (min l r, max l r)
--there are 4 possible pairs of point depending on the direction of ax
--partially apply just x and y to the constructor in c' and d'
where (c', d') | x*y > 0 = (V3 (a^._x) (a^._y), V3 (b^._x) (b^._y)) --same x and y
| otherwise = (V3 (a^._x) (b^._y), V3 (b^._x) (a^._y)) --different x and y
(c, d) | x*z > 0 = (c' (a^._z), d' (b^._z)) --same x and z
| otherwise = (c' (b^._z), d' (a^._z)) --different x and z
(l, r) = (c `dot` ax, d `dot` ax)
Calls to intersects :: Box -> Tri -> Bool are slowing down my application, but I can't get it to run any faster. I've looked at outputs from the profiler, heap profile, and -ddump-simpl, but they don't provide any clues. What am I missing?

Is there a way to remove duplicate where statements in Haskell?

I have the following code in Haskell:
move :: Camera -> (Double, Double, Double) -> Camera
move camera (xt, yt, zt) = camera { cPosition = (x + xt, y + yt, z + zt) }
where (x, y, z) = cPosition camera
moveForward :: Camera -> Camera
moveForward camera = move camera (-1 * sin ya, 0, -1 * cos ya)
where (_, ya, _) = cRotation camera
moveBackward :: Camera -> Camera
moveBackward camera = move camera (sin ya, 0, cos ya)
where (_, ya, _) = cRotation camera
You'll notice that the moveForward and moveBackward functions have identical where statements. Is there a way to remove this duplication? I have numerous functions with the same where clauses (read: more than two).
I would prefer not to pass it in as another argument - since it will never change. It will always be cRotation.
What about making these functions take the tuple as an argument, and then wrapping them with another function that automatically does the boring work of extracting the tuple?
rotated :: ((Double, Double, Double) -> Camera -> a) -> Camera -> a
rotated f camera = f (cPosition camera) camera
moveForward :: Camera -> Camera
moveForward = rotated moveForward'
where moveForward' (_, ya, _) camera = move camera (-1 * sin ya, 0, -1 * cos ya)
moveBackward :: Camera -> Camera
moveBackward = rotated moveBackward'
where moveBackward' (_, ya, _) camera = move camera (sin ya, 0, cos ya)
Edit: Reviewing my answer six months later, I note there is some more duplication that could be lifted out: the move camera call. So really your functions like moveForward can just take a 3-tuple and return a 3-tuple, like so:
moveRotated :: ((Double, Double, Double) -> (Double, Double, Double)) -> Camera -> Camera
moveRotated f camera = move camera . f $ cPosition camera
moveForward :: Camera -> Camera
moveForward = moveRotated forward
where forward (_, ya, _) = (- sin ya, 0, - cos ya)
moveBackward :: Camera -> Camera
moveBackward = moveRotated backward
where backward (_, ya, _) = (sin ya, 0, cos ya)
This gives less power to moveForward and moveBackward, of course, since you can't use them to do anything but move. But it nicely distills them down to their essences, and ensures you can't accidentally do something other than move.
There's the simple answer of just define your own function
snd3 :: (a, b, c) -> b
snd3 (a, b, c) = b
And then you could use a lambda
moveForward camera = \ya -> (-1 * sin ya, 0, -1 * cos ya) $ snd3 $ cRotation camera
moveBackward camera = \ya -> (sin ya, 0, cos ya) $ snd3 $ cRotation camera
Or if you want to add the lens library as a dependency, you can replace snd3 cRotation camera with cRotation camera ^. _2 or equivalently view _2 $ cRotation camera. As for removing that lambda, there isn't much you can do other than defining a new function
apply3 :: (a -> a') -> (b -> b') -> (c -> c') -> (a, b, c) -> (a', b', c')
apply3 f1 f2 f3 (a, b, c) = (f1 a, f2 b, f3 c)
moveForward = apply3 (negate . sin) (const 0) (negate . cos) . snd3 . cRotation
moveBackward = apply3 sin (const 0) cos . snd3 . cRotation
And use some eta-reduction.
Unfortunately, there are lots of elegant tricks for working with 2-tuples but not as many for 3-tuples.

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