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Introduction to Functional Programming for abso...

Anupam
December 21, 2017

Introduction to Functional Programming for absolute beginners

A 2 hour presentation given at S&P Gurgaon on 21st Dec 2017

Anupam

December 21, 2017
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  1. FUNCTIONAL PROGRAMMING WHO AM I ▸ Programming professionally for over

    12 years ▸ Organise FPNCR Meetup group ▸ The usual suspects - Javascript, Java, Ruby, Python, C ▸ Discovered Functional Programming around 2004 ▸ Lisp -> (OCaml, Erlang) -> Clean -> … -> Haskell ▸ Elm, Purescript, countless others 2 * Please join
  2. FUNCTIONAL PROGRAMMING FP IN NCR ▸ The Delhi/NCR Haskell and

    other Functional Programming Languages Meetup Group ▸ https://www.meetup.com/DelhiNCR-Haskell-And-Functional-Programming- Languages-Group ▸ FPNCR Github Organisation - common projects, propose talks, etc. ▸ https://github.com/fpncr ▸ FPNCR Mailing List ▸ https://groups.google.com/forum/#!forum/fpncr ▸ FPNCR Channel on Slack ▸ https://functionalprogramming.slack.com - #FPNCR 3
  3. FUNCTIONAL PROGRAMMING SIMPLICITY ▸ Single Unit of abstraction - Functions

    ▸ Functions are Values ▸ All Values can be passed to Functions 5
  4. FUNCTIONAL PROGRAMMING SIMPLE FUNCTIONS 1 fib :: Integer -> Integer

    2 fib 0 = 1 3 fib 1 = 1 4 fib n = fib (n-1) + fib (n-2) 6
  5. FUNCTIONAL PROGRAMMING CASE EXPRESSIONS 1 fib :: Integer -> Integer

    2 fib n = case n of 3 0 -> 1 4 1 -> 1 5 n -> fib (n-1) + fib (n-2) 7
  6. FUNCTIONAL PROGRAMMING ANONYMOUS FUNCTIONS 1 fib :: Integer -> Integer

    2 fib = \n -> case n of 3 0 -> 1 4 1 -> 1 5 n -> fib (n-1) + fib (n-2) 8
  7. FUNCTIONAL PROGRAMMING GUARDS 1 fib :: Integer -> Integer 2

    fib n 3 | n < 2 = 1 4 | otherwise = fib (n-1) + fib (n-2) 9
  8. FUNCTIONAL PROGRAMMING THINGS THAT MAKE HASKELL UNIQUE ▸ Strongly Typed

    unlike Clojure ▸ Pure unlike OCaml, Scala ▸ Lazy unlike Purescript, Elm 10
  9. FUNCTIONAL PROGRAMMING STRONG STATIC TYPING ▸ Checked at compile time

    ▸ Not the same as Types in, say, Java ▸ Slight Hyperbole - “If it compiles it works” ▸ Reality - “Refactor without fear” ▸ Let the computer help you help yourself 11
  10. FUNCTIONAL PROGRAMMING STRONG STATIC TYPING ▸ Primitive Types ▸ Algebraic

    Data Types ▸ Recursive Types ▸ Function Types ▸ Pattern Matching ▸ Totality 12
  11. FUNCTIONAL PROGRAMMING PRIMITIVE TYPES ▸ Void ▸ () = ()

    ▸ Bool = True | False ▸ Char = ‘a’ | ‘b’ | … ▸ Integer = 0 | 1 | … ▸ String = [Char] 13
  12. FUNCTIONAL PROGRAMMING ALGEBRAIC DATA TYPES 1 data Shape 2 =

    Square 3 | Circle 4 | Triangle 5 6 data User 7 = Root 8 | Login String 14
  13. FUNCTIONAL PROGRAMMING DATA TYPES WITH PARAMS 1 data Maybe a

    2 = Nothing 3 | Just a 4 5 data Either a b 6 = Left a 7 | Right b 15
  14. FUNCTIONAL PROGRAMMING RECURSIVE DATA TYPES 1 data List a 2

    = Empty 3 | Cons a (List a) 4 5 data Tree a 6 = Leaf a 7 | Branch (Tree a) (Tree a) 8 16
  15. FUNCTIONAL PROGRAMMING TYPED FIRST CLASS FUNCTIONS 1 data Ordering 2

    = LT | GT | EQ 3 5 type Cmp a = a -> a -> Ordering 6 7 sort :: Cmp a -> [a] -> [a] 17
  16. FUNCTIONAL PROGRAMMING COMPOSITION VS INHERITANCE ▸ Don’t inherit, compose ▸

    Vehicle > Car > RacingCar ▸ data RacingCar = RacingCar VehicleData CarData RacingCarData 18
  17. FUNCTIONAL PROGRAMMING DEFINING FUNCTIONS WITH PATTERN MATCHING 1 data List

    a 2 = Empty 3 | Cons a (List a) 4 5 cdr :: List a -> List a 6 cdr Empty = ?? 7 cdr (Cons _ rest) = rest 8 9 car :: List a -> a 10 car Empty = ?? 11 car (Cons a _) = a 12 19
  18. FUNCTIONAL PROGRAMMING PATTERN MATCHING (CONTD…) 1 data List a 2

    = Empty 3 | Cons a (List a) 4 5 cdr :: List a -> List a 6 cdr Empty = Empty 7 cdr (Cons _ rest) = rest 8 9 car :: List a -> Maybe a 10 car Empty = Nothing 11 car (Cons a _) = Just a 12 13 data Maybe a = Nothing | Just a 14 20
  19. FUNCTIONAL PROGRAMMING LIST CONCATENATION 1 data List a 2 =

    Empty 3 | Cons a (List a) 4 5 concat :: List a -> List a -> List a 6 concat Empty b = b 7 concat a Empty = a 8 concat (Cons x xs) b@(Cons _ _) 9 = Cons x (concat xs b) 10 21
  20. FUNCTIONAL PROGRAMMING PYTHON 1 class Cell: 2 def __init__( self,

    data, next = None ): 3 self.data = data 4 self.next = next 5 6 def concat(A, B): 7 current = A 8 while current.next != None: 9 current = current.next 10 current.next = B 11 return A 22
  21. HASKELL 101 PYTHON def concat(A, B): current = A while

    current.next != None: current = current.next current.next = B return A 23 concat Empty b = b concat a Empty = a concat (Cons x xs) b = Cons x (concat xs b) HASKELL FIGHT!
  22. HASKELL 101 PYTHON def concat(A, B): current = A while

    current.next != None: current = current.next current.next = B return A 24 concat Empty b = b concat a Empty = a concat (Cons x xs) b = Cons x (concat xs b) HASKELL TEST assertEq(concat('','bar'), 'bar') assertEq(concat('foo',''), 'foo') assertEq(concat(‘foo’,'bar'), 'foobar')
  23. HASKELL 101 PYTHON def concat(A, B): current = A while

    current.next != None: current = current.next current.next = B return A 25 concat Empty b = b concat a Empty = a concat (Cons x xs) b = Cons x (concat xs b) HASKELL
  24. THERE ARE TWO WAYS OF CONSTRUCTING A SOFTWARE DESIGN: ONE

    WAY IS TO MAKE IT SO COMPLICATED THAT THERE ARE NO OBVIOUS DEFICIENCIES, AND THE OTHER WAY IS TO MAKE IT SO SIMPLE THAT THERE ARE OBVIOUSLY NO DEFICIENCIES. - C.A.R. Hoare, 1980 ACM Turing Award Lecture HASKELL 101 26
  25. FUNCTIONAL PROGRAMMING BUT WHAT ABOUT... ▸ Monads? ▸ “It’s just

    Monoids in the category of endofunctors. What is the problem?” ▸ Category Theory ▸ Blah Blah 27
  26. FUNCTIONAL PROGRAMMING You can ignore all the abstract math stuff

    and yet be highly productive with
 Functional Programming 28
  27. HASKELL 101 SUM NUMBERS FROM 1 TO 10 1 total

    = 0, count = 1 2 while count <= 10: 3 total = total + count 4 count = count + 1 31 1 total = sum [1..10]
  28. HASKELL 101 SHARED VOCABULARY COMPARED 1 total = 0, count

    = 1 2 while count <= 10: 3 total = total + count 4 count = count + 1 33 1 total = sum [1..10]
  29. FUNCTIONAL PROGRAMMING SUM ELEMENTS OF A LIST 1 sum ::

    [Int] -> Int 2 sum [] = 0 3 sum (x:xs) = x + sum xs 4 34
  30. FUNCTIONAL PROGRAMMING MULTIPLY ELEMENTS OF A LIST 1 prod ::

    [Int] -> Int 2 prod [] = 0 3 prod (x:xs) = x * sum xs 4 35
  31. FUNCTIONAL PROGRAMMING ABSTRACTION 1 fold :: (a -> Int ->

    a) -> a -> [Int] -> a 2 fold _ a [] = a 3 fold f a (x:xs) = fold f (f a x) xs 4 36
  32. FUNCTIONAL PROGRAMMING FOLD OVER ELEMENTS OF A LIST 1 sum

    = fold (+) 0 2 prod = fold (*) 1 3 37
  33. FUNCTIONAL PROGRAMMING IS ANY/ALL BOOLEAN TRUE 1 any = fold

    (||) False 2 all = fold (&&) True 3 38
  34. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS > 3 1 anyLarge =

    fold (\p e -> p || e > 3) False 2 allLarge = fold (\p e -> p && e > 3) True 3 39
  35. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS DIGITS 1 anyDigit = fold

    (\p e -> p || isDigit e) False 2 allDigit = fold (\p e -> p && isDigit e) True 3 40
  36. FUNCTIONAL PROGRAMMING MORE ABSTRACTION! 1 foldMap :: (a -> Int

    -> a) -> (Int -> Int) -> a -> [Int] -> a 2 foldMap _ _ a [] = a 3 foldMap f m a (x:xs) = foldMap f m (f a (m x)) xs 4 41
  37. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS > 3 1 anyLarge =

    foldMap (||) (> 3) False 2 allLarge = foldMap (&&) (> 3) True 3 42
  38. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS DIGITS 1 anyDigit = foldMap

    (||) isDigit False 2 allDigit = foldMap (&&) isDigit True 3 43
  39. FUNCTIONAL PROGRAMMING WHY ▸ Easy to understand what's going on

    ▸ Cleanly separated “logic” ((&&), isdigit, etc.) ▸ Common machinery (foldMap) can be easily reused ▸ Easy to test functions in isolation 45
  40. FUNCTIONAL PROGRAMMING BUT… ▸ foldMap has 3 arguments that are

    not easy to remember ▸ It's harder to generalise this pattern. Should we keep on adding arguments to fold/foldMap? 46
  41. FUNCTIONAL PROGRAMMING WE CAN ABSTRACT FURTHER 1 compose2 :: (a

    -> b -> d) -> (x -> b) -> a -> x -> d 2 compose2 f m a x = f a (m x) 3 foldMap f m a (x:xs) = foldMap f m (f a (m x)) xs 4 = foldMap f m (compose2 f m a x) xs foldMap f m a (x:xs) = fold (compose2 f m) a x xs 47
  42. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS > 3 1 anyLarge =

    fold (compose2 (||) (> 3)) False 2 allLarge = fold (compose2 (&&) (> 3)) True 3 48
  43. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS DIGITS 1 anyDigit = fold

    (compose2 (||) isDigit) False 2 allDigit = fold (compose2 (&&) isDigit) True 3 49
  44. FUNCTIONAL PROGRAMMING MUCH BETTER ▸ We removed an extra function

    (foldMap) which was actually not needed ▸ Our existing machinery (fold) turned out to be powerful enough to support our logic ▸ We reduced code complexity by introducing another “orthogonal” and “generic” shared vocabulary (compose2) 50
  45. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS DIGITS 1 anyDigit = fold

    (compose2 (||) isDigit) False 2 allDigit = fold (compose2 (&&) isDigit) True 3 51
  46. PERFECTION IS ATTAINED NOT WHEN THERE IS NOTHING MORE TO

    ADD, BUT WHEN THERE IS NOTHING MORE TO REMOVE. - Antoine de Saint Exupéry HASKELL 101 52
  47. FUNCTIONAL PROGRAMMING GO DEEPER 1 (.) :: (b -> c)

    -> (a -> b) -> a -> c 2 (.) f g a = f (g a) 3 4 flip :: (a -> b -> c) -> b -> a -> c 5 flip f b a = f a b 6 53
  48. FUNCTIONAL PROGRAMMING ARE ANY/ALL ELEMENTS DIGITS 1 anyDigit = fold

    (flip ((||) . isDigit)) False 2 allDigit = fold (flip ((&&) . isDigit)) True 3 54
  49. FUNCTIONAL PROGRAMMING FUNCTIONS ARE DATA PIPELINES (flip ((||) . isDigit))

    a b = ((||) . isDigit) b a = ((||) (isDigit b)) a = a || isDigit b 55
  50. FUNCTIONAL PROGRAMMING THIS IS SPARTA HASKELL 1 module Main where

    2 3 main :: IO () 4 main = do 5 putStrLn "Hello Haskell!" 6 56
  51. FUNCTIONAL PROGRAMMING THIS IS SPARTA HASKELL 1 module Main where

    2 3 main :: IO () 4 main = do 5 putStrLn "What is your name?" 6 name <- getLine 7 putStrLn ("Hello " ++ name ++ “!") 8 57
  52. FUNCTIONAL PROGRAMMING THIS IS SPARTA HASKELL 1 module Main where

    2 3 main :: IO () 4 main = do 5 name <- getName 6 putStrLn ("Hello " ++ name ++ "!") 7 8 getName :: IO String 9 getName = do 10 putStrLn "What is your name?" 11 getLine 58
  53. FUNCTIONAL PROGRAMMING “IO” DENOTES MUTATION 1 main :: IO ()

    2 main = do 3 name <- getName 4 showGreeting name 5 6 showGreeting :: String -> IO () 7 showGreeting name = putStrLn (greeting name) 8 9 getName :: IO String 10 getName = ... 11 12 greeting :: String -> String 13 greeting name = "Hello " ++ name ++ "!" 59
  54. FUNCTIONAL PROGRAMMING WAI-ROUTES - TYPE SAFE WEB ROUTING data MyRoute

    = HomeR | HelloR Text renderRoute :: MyRoute -> [Text] parseRoute :: [Text] -> MyRoute 62 Check out https://github.com/ajnsit/wai-routes
  55. FUNCTIONAL PROGRAMMING GENERATED WEB SERVER ROUTING case getPathInfo req of

    "hello" : arg : [] -> case getReqMethod req of GET -> getHello arg req POST -> postHello arg req 63
  56. FUNCTIONAL PROGRAMMING REAL WORLD HASKELL getAlbumsR :: Handler AlbumRoute getAlbumsR

    = runHandlerM $ do Just token <- reqHeader “Authorisation” auth <- liftIO $ post (showRoute ValidateR) token if isValid auth then status 403 >> json err else json albums 64
  57. FUNCTIONAL PROGRAMMING CONCUR - EXTREMELY SIMPLE UI FRAMEWORK main =

    liftIO (R.randomRIO (1,100)) >>= go where go n = do guessStr <- el "div" [] [ text "Try to guess: " , inputEnter [] ] case readMaybe guessStr of Nothing -> go n Just guess -> do if | guess < n -> el_ "div" [] (text $"Go High!") <|> go n | guess > n -> el_ "div" [] (text $"Go Low!") <|> go n | otherwise -> el_ "div" [] (text $ "You guessed it!”) <|> (button [] (text "Play again")) 65 Check out https://github.com/ajnsit/concur
  58. FUNCTIONAL PROGRAMMING FOLDABLE CLASS 1 class Foldable f where 2

    foldr :: (b -> a -> b) -> b -> f a -> b 68
  59. FUNCTIONAL PROGRAMMING LIST INSTANCES 2 instance Functor [] where 3

    fmap f [] = [] 4 fmap f (x:xs) = (f x: map f xs) 5 6 instance Foldable [] where 7 foldl f b [] = b 8 foldl f b (x:xs) = foldl f (f b x) xs 69
  60. FUNCTIONAL PROGRAMMING ANY DATA STRUCTURE CAN BE FOLDED ▸ When

    Data is Non-Recursive, its the same as fmap ▸ When a data structure is recursive, each recursive part needs to be handled separately 70
  61. FUNCTIONAL PROGRAMMING TREE 2 instance Functor Tree where 3 fmap

    f (Leaf a) = Leaf (f a) 4 fmap f (Branch x y) = Branch (fmap f x) (fmap f y) 5 6 instance Foldable Tree where 7 foldl f b (Leaf a) = f b a 8 foldl f b (Branch x y) = ?? (foldl f b x) (foldl f b y) 71
  62. FUNCTIONAL PROGRAMMING FOLD TREE 1 foldTree :: (b -> a

    -> b) -> (b -> b -> b) -> b 2 -> Tree a -> b 3 foldTree f g b (Leaf a) = f b a 4 foldTree f g b (Branch x y) 5 = g (foldTree f g b x) (foldTree f g b y) 72
  63. FUNCTIONAL PROGRAMMING ANOTHER COMMON PATTERN 1 foldTree :: (b ->

    a -> b) -> (b -> b -> b) -> b 2 -> Tree a -> b 3 foldTree f g b (Leaf a) = f b a 4 foldTree f g b (Branch x y) 5 = g (foldTree f g b x) (foldTree f g b y) 73
  64. FUNCTIONAL PROGRAMMING FOLDABLE TREE REVISITED 1 foldTree :: Monoid b

    => (b -> a -> b) -> b 2 -> Tree a -> b 3 foldTree f b (Leaf a) = f b a 4 foldTree f b (Branch x y) 5 = foldTree f b x <> foldTree f b y 75
  65. FUNCTIONAL PROGRAMMING ALGEBRAIC DATA TYPES 1 data Maybe a 2

    = Nothing 3 | Just a 4 5 data Either a b 6 = Left a 7 | Right b 8 9 data Person 10 = Empty 11 | Cons a (List a) 12 13 data Tree a 14 = Leaf a 15 | Branch (Tree a) (Tree a) 16 77