Data commit

Task/Monte-Carlo-methods/Haskell/monte-carlo-methods-1.hs

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+import Control.Monad
+import System.Random
+
+getPi throws = do
+  results <- replicateM throws one_trial
+  return (4 * fromIntegral (sum results) / fromIntegral throws)
+  where
+    one_trial = do
+      rand_x <- randomRIO (-1, 1)
+      rand_y <- randomRIO (-1, 1)
+      let dist :: Double
+          dist = sqrt (rand_x * rand_x + rand_y * rand_y)
+      return (if dist < 1 then 1 else 0)

Task/Monte-Carlo-methods/Haskell/monte-carlo-methods-2.hs

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+import Control.Monad (foldM, (>=>))
+import System.Random (randomRIO)
+import Data.Functor ((<&>))
+
+------- APPROXIMATION TO PI BY A MONTE CARLO METHOD ------
+
+monteCarloPi :: Int -> IO Double
+monteCarloPi n =
+  (/ fromIntegral n) . (4 *) . fromIntegral
+    <$> foldM go 0 [1 .. n]
+  where
+    rnd = randomRIO (0, 1) :: IO Double
+    go a _ = rnd >>= ((<&>) rnd . f a)
+    f a x y
+      | 1 > x ** 2 + y ** 2 = succ a
+      | otherwise = a
+
+--------------------------- TEST -------------------------
+main :: IO ()
+main =
+  mapM_
+    (monteCarloPi >=> print)
+    [1000, 10000, 100000, 1000000]