1 COMP313A Functional Programming (1)

2 Main Differences with Imperative Languages Say more about what is computed as opposed to how Pure functional languages have no –variables –assignments –other side effects Means no loops

3 Differences… Imperative languages have an implicit state (state of variables) that is modified (side effect). Sequencing is important to gain precise, deterministic control over the state assignment statement needed to change the binding for a particular variable (alter the implicit state)

4 Imperative factorial n:=x; a:=1; while n > 0 do begin a:= a * n; n := n-1; end;

5 Differences Declarative languages have no implicit state Program with expressions The state is carried around explicitly –e.g. the formal parameter n in factorial Looping is accomplished with recursion rather than sequencing

6 Haskell fac ::Int -> Int fac n | n == 0 = 1 | n > 0 = fac(n-1) * n | otherwise = 0 Scheme (define fac (lambda (n) (if (= n 0) 1 (* n (fac (- n 1))))))

7 Advantages similar to traditional mathematics referential transparency makes programs more readable higher order functions give great flexibility concise programs

8 Referential Transparency the value of a function depends only on the values of its parameters for example Haskell …x + x… where x = f a

9 Evolution of Functional languages Lambda Calculus Alonzo Church Captures the essence of functional programming Formalism for expressing computation by functions Lambda abstraction In Scheme In Haskell ( x. + 1 x) (lambda(x) (+ 1 x))) add1 :: Int -> Int add1 x = 1 + x

10 Lambda Calculus… application of expressions reduction rule that substitutes 2 for x in the lambda (beta reduction) ( x. + 1 x) 2 ( x. + 1 x) 2  (+ 1 2)  3 ( x. * x x) (+ 2 3) i.e. (+ 2 3) / x)

11 Lambda Calculus… ( x.E) –variable x is bound by the lambda –the scope of the binding is the expression E ( x. + y x) –( x. + y x)2  (+ y 2) ( x. + (( y. (( x. * x y) 2)) x) y) 

12 Lambda Calculus… Each lambda abstraction binds only one variable Need to bind each variable with its own lambda. ( x. ( y. + x y))

13 Lisp McCarthy late 1950s Used Lambda Calculus for anonymous functions List processing language First attempt built on top of FORTRAN

14 LISP… McCarthy’s main contributions were 1.the conditional expression and its use in writing recursive functions Scheme (define fac (lambda (n) (if (= n 0) 1 (if (> n 0) (* n (fac (- n 1))) 0)))) Scheme (define fac2 (lambda (n) (cond ((= n 0) 1) ((> n 0) (* n (fac2 (- n 1)))) (else 0)))) Haskell fac ::Int -> Int fac n | n == 0 = 1 | n > 0 = fac(n-1) * n | otherwise = 0 Haskell fac :: Int -> Int fac n = if n == 0 then 1 else if n > 0 then fac(n-1) * n else 0

15 2.the use of lists and higher order operations over lists such as mapcar Lisp… Scheme (mymap + ’(3 4 6) ’(5 7 8)) Haskell mymap :: (Int -> Int-> Int) -> [Int] -> [Int] ->[Int] mymap f [] [] = [] mymap f (x:xs) (y:ys) = f x y : mymap f xs ys add :: Int -> Int -> Int add x y = x + y Scheme (define mymap (lambda (f a b) (cond ((and (null? a) (null? b)) '()) (else (cons (f (first a) (first b)) (mymap f (rest a) (rest b))))))) Haskell mymap add [3, 4, 6] [5, 7, 8]

16 Lisp 3.cons cell and garbage collection of unused cons cells Scheme (cons ’1 ’( )) ( ) Haskell cons :: Int -> [Int] -> [Int] cons x xs = x:xs Scheme (define mymap (lambda (f a b) (cond ((and (null? a) (null? b)) '()) (else (cons (f (first a) (first b)) (mymap f (first a) (first b)))))))

17 Lisp… 4.use of S-expressions to represent both program and data An expression is an atom or a list But a list can hold anything…

18 Scheme (cons ’1 ’( )) ( ) Scheme (define mymap (lambda (f a b) (cond ((and (null? a) (null? b)) '()) (else (cons (f (first a) (first b)) (mymap f (first a) (first b)))))))

19 ISWIM Peter Landin – mid 1960s If You See What I Mean Landon wrote “…can be looked upon as an attempt to deliver Lisp from its eponymous commitment to lists, its reputation for hand-to-mouth storage allocation, the hardware dependent flavour of its pedagogy, its heavy bracketing, and its compromises with tradition”

20 Iswim… Contributions –Infix syntax –let and where clauses, including a notion of simultaneous and mutually recursive definitions –the off side rule based on indentation layout used to specify beginning and end of definitions –Emphasis on generality small but expressive core language

21 let in Scheme let* - the bindings are performed sequentially (let * ((x 2) (y 3))  ( * x y)) (let * ((x 2) (y 3)) (let * ((x 7) (z (+ x y)))  ( * z x)))

22 let in Scheme (let ((x 2) (y 3))  ? ( * x y)) let - the bindings are performed in parallel, i.e. the initial values are computed before any of the variables are bound (let ((x 2) (y 3)) (let ((x 7) (z (+ x y)))  ? ( * z x)))

23 letrec in Scheme letrec – all the bindings are in effect while their initial values are being computed, allows mutually recursive definitions (letrec ((even? (lambda (n) (if (zero? n) #t (odd? (- n 1))))) (odd? (lambda (n) (if (zero? n) #f (even? (- n 1)))))) (even? 88))

24 ML Gordon et al 1979 Served as the command language for a proof generating system called LCF –LCF reasoned about recursive functions Comprised a deductive calculus and an interactive programming language – Meta Language (ML) Has notion of references much like locations in memory I/O – side effects But encourages functional style of programming

25 ML Type System –it is strongly and statically typed –uses type inference to determine the type of every expression –allows polymorphic functions Has user defined ADTs

26 SASL, KRC, and Miranda Used guards Higher Order Functions Lazy Evaluation Currying SASL fac n = 1,n = 0 = n * fac (n-1), n>0 Haskell fac n | n ==0 = 1 | n >0 = n * ( fac(n-1) add x y = + x y add 1 switch :: Int -> a -> a -> a switch n x y | n > 0 = x | otherwise = y multiplyC :: Int -> Int -> Int Versus multiplyUC :: (Int, Int) -> Int

27 SASL, KRC and Miranda KRC introduced list comprehension Miranda borrowed strong data typing and user defined ADTs from ML comprehension :: [Int] -> [Int] comprehension ex = [2 * n | n <- ex]