Inaugural presentation on functional programming for the JaxFunc meetup.

1. Functional Programming Concepts for Imperative Programmers Imperatively Functional chrislewis / chris@thegodcode.net http://twitter.com/burningodzilla http://github.com/chrislewis

2. Origins: The λ-Calculus

4. Formal system for function definition, application, and recursion.

6. A lambda may “close” over free variables to create a closure.

8. A lambda may “close” over free variables to create a closure.

9. Turing complete.true := λxy.x false := λxy.y if-then-else := λpab.p a b

11. An Implementation of the λ-calculus.

14. There are many facets of functional programming.We will focus primarily onhigher-order functions and thetreatment of functions as data.

15. Functions are Data varincrement = function(i) { return i + 1; } varsquare = function(i) { return i * i; } function doSomething(i, f) { return f(i); }

16. Functions are Data doSomething(5, increment); // -> 6 doSomething(5, square); // -> 25

17. Functions are Data valincrement = (i: Int) => i + 1 valsquare = (i: Int) => i * i def doSomething[A, B](x: A, f: A => B) = f(x) doSomething(5, increment) // -> 6 doSomething(5, square) // -> 25

20. A closure isn’t necessarily anonymous!

22. Ruby most notably has blocks, but also lambdas (keyword) and procs!

23. Scala has methods, function traits, and shorthand yielding trait instances.

27. “y is a function of x”

29. “y is a function of x”

30. The value of y depends on x; a simple mapping from one value, x, to another, y.

31. f: x -> yand g: y -> z

33. (g . f) = g(f(x)) or (g ∘ f) = g(f(x))

35. (g . f) = g(f(x)) or (g ∘ f) = g(f(x))

36. Combine the functions g and f into a new function that applies g to the result of applying f to x.Back to the code!

37. In JavaScript: function compose(f, g) { // a closure, closing over the free // variables f and g return function(x) { return g(f(x)); } } // recursive implementation: // http://gist.github.com/366614

38. In Scala: def compose[A, B, C](g: B => C, f: A => B) = (x: A) => g(f(x)) // the closure

40. Ruby, via Rails, brought higher-order functions that operate onlists mainstream.

42. Because we know how to pass functions around, we know how to implement these …… but we’ll probably duplicate code.

43. Because we know how to pass functions around, we know how to implement these …… but we’ll probably duplicate code.Let’s talk about folding.

44. In JavaScript: function foldr(list, init, f) { if(list.length == 0) return init; else { return foldr(list.slice(1), f(init, list[0]), f); } }

45. In Scala: def foldr[A, B](list: List[A], init: B, f: (B, A) => B): B = list match { case Nil => init case head :: tail => foldr(tail, f(init, head), f) }

47. product = foldr (*) 1

48. anytrue = foldr (∨) false

49. alltrue = foldr (∧) true

50. map = foldr (cons . f) []

52. “loan” pattern.

53. Event handling in the Lift framework.

55. Pattern matching

56. Type inference, higher-kinded types, and general type systems

57. Partial function application

58. Monads

59. Lazy vs strict evaluation

61. Thanks! chrislewis / chris@thegodcode.net http://twitter.com/burningodzilla http://github.com/chrislewis