Introduction to SML Getting Started

Transcription

1 Introduction to SML Getting Started Michael R. Hansen Informatics and Mathematical Modelling Technical University of Denmark c Michael R. Hansen, Fall 2004 p.1/15

2 Background Standard Meta Language (SML) was originally designed for theorem proving Logic for Computable Functions (Edinburgh LCF) Gordon, Milner, Wadsworth (1977) c Michael R. Hansen, Fall 2004 p.2/15

3 Background Standard Meta Language (SML) was originally designed for theorem proving Logic for Computable Functions (Edinburgh LCF) Gordon, Milner, Wadsworth (1977) High quality compilers, e.g. Standard ML of New Jersey and Moscow ML, based on a formal semantics Milner, Tofte, Harper, MacQueen 1990 & 1997 c Michael R. Hansen, Fall 2004 p.2/15

4 Background Standard Meta Language (SML) was originally designed for theorem proving Logic for Computable Functions (Edinburgh LCF) Gordon, Milner, Wadsworth (1977) High quality compilers, e.g. Standard ML of New Jersey and Moscow ML, based on a formal semantics Milner, Tofte, Harper, MacQueen 1990 & 1997 SML have now may applications far away from its origins Compilers, Artificial Intelligence, Web-applications,... c Michael R. Hansen, Fall 2004 p.2/15

5 Background Standard Meta Language (SML) was originally designed for theorem proving Logic for Computable Functions (Edinburgh LCF) Gordon, Milner, Wadsworth (1977) High quality compilers, e.g. Standard ML of New Jersey and Moscow ML, based on a formal semantics Milner, Tofte, Harper, MacQueen 1990 & 1997 SML have now may applications far away from its origins Compilers, Artificial Intelligence, Web-applications,... Used to teach functional program design and programming style c Michael R. Hansen, Fall 2004 p.2/15

6 Background Standard Meta Language (SML) was originally designed for theorem proving Logic for Computable Functions (Edinburgh LCF) Gordon, Milner, Wadsworth (1977) High quality compilers, e.g. Standard ML of New Jersey and Moscow ML, based on a formal semantics Milner, Tofte, Harper, MacQueen 1990 & 1997 SML have now may applications far away from its origins Compilers, Artificial Intelligence, Web-applications,... Used to teach functional program design and programming style Also useful when programming using non-functional languages c Michael R. Hansen, Fall 2004 p.2/15

7 Special Features SML supports Functions as first class citizens c Michael R. Hansen, Fall 2004 p.3/15

8 Special Features SML supports Functions as first class citizens Structured values like lists, trees,... c Michael R. Hansen, Fall 2004 p.3/15

9 Special Features SML supports Functions as first class citizens Structured values like lists, trees,... Strong and flexible type discipline, including type inference and polymorphism c Michael R. Hansen, Fall 2004 p.3/15

10 Special Features SML supports Functions as first class citizens Structured values like lists, trees,... Strong and flexible type discipline, including type inference and polymorphism Powerful module system supporting abstract data types c Michael R. Hansen, Fall 2004 p.3/15

11 Special Features SML supports Functions as first class citizens Structured values like lists, trees,... Strong and flexible type discipline, including type inference and polymorphism Powerful module system supporting abstract data types Imperative programming assignments, loops, arrays, Input/Output, etc. c Michael R. Hansen, Fall 2004 p.3/15

12 Special Features SML supports Functions as first class citizens Structured values like lists, trees,... Strong and flexible type discipline, including type inference and polymorphism Powerful module system supporting abstract data types Imperative programming assignments, loops, arrays, Input/Output, etc. Programming as a modelling discipline High-level programming, declarative programming, executable specifications VDM, RAISE Fast prototyping correctness, time-to-market, program designs c Michael R. Hansen, Fall 2004 p.3/15

13 Overview The interactive environment Values, expressions, types, patterns Declarations of values and recursive functions Binding, environment and evaluation Type inference c Michael R. Hansen, Fall 2004 p.4/15

14 Overview The interactive environment Values, expressions, types, patterns Declarations of values and recursive functions Binding, environment and evaluation Type inference GOAL: By the end of the day you have constructed succinct, elegant and understandable SML programs, e.g. for sum(m, n) = n i=m i Fibonacci numbers (F 0 = 0, F 1 = 1, F n = F n 1 + F n 2 ) ( ) n Binomial coefficients k c Michael R. Hansen, Fall 2004 p.4/15

15 The Interactive Environment 2*3 +4; val it = 10 : int c Michael R. Hansen, Fall 2004 p.5/15

16 The Interactive Environment 2*3 +4; val it = 10 : int Input to the SML system Answer from the SML system c Michael R. Hansen, Fall 2004 p.5/15

17 The Interactive Environment 2*3 +4; val it = 10 : int Input to the SML system Answer from the SML system The keyword val indicates a value is computed The integer 10 is the computed value int is the type of the computed value The identifier it names the (last) computed value c Michael R. Hansen, Fall 2004 p.5/15

18 The Interactive Environment 2*3 +4; val it = 10 : int Input to the SML system Answer from the SML system The keyword val indicates a value is computed The integer 10 is the computed value int is the type of the computed value The identifier it names the (last) computed value The notion binding explains which entities are named by identifiers. it 10 reads: it is bound to 10 c Michael R. Hansen, Fall 2004 p.5/15

19 Value Declarations A value declaration has the form: val identifier = expression val price = 25 * 5; val price = 125 : int A declaration as input Answer from the SML system The effect of a declaration is a binding price 125 c Michael R. Hansen, Fall 2004 p.6/15

20 Value Declarations A value declaration has the form: val identifier = expression val price = 25 * 5; val price = 125 : int A declaration as input Answer from the SML system The effect of a declaration is a binding price 125 Bound identifiers can be used in expressions and declarations, e.g. val newprice = 2*price; val newprice = 250 : int newprice > 500; val it = false : bool c Michael R. Hansen, Fall 2004 p.6/15

21 Value Declarations A value declaration has the form: val identifier = expression val price = 25 * 5; val price = 125 : int A declaration as input Answer from the SML system The effect of a declaration is a binding price 125 Bound identifiers can be used in expressions and declarations, e.g. val newprice = 2*price; val newprice = 250 : int newprice > 500; val it = false : bool A collection of bindings price 125 newprice 250 it false is called an environment c Michael R. Hansen, Fall 2004 p.6/15

22 Function Declarations 1: fun f x = e Declaration of the circle area function: fun circlearea r = Math.pi * r * r; Math is a program library pi is an identifier (with type real) for π declared in Math c Michael R. Hansen, Fall 2004 p.7/15

23 Function Declarations 1: fun f x = e Declaration of the circle area function: fun circlearea r = Math.pi * r * r; Math is a program library pi is an identifier (with type real) for π declared in Math The type is automatically inferred in the answer: val circlearea = fn : real -> real c Michael R. Hansen, Fall 2004 p.7/15

24 Function Declarations 1: fun f x = e Declaration of the circle area function: fun circlearea r = Math.pi * r * r; Math is a program library pi is an identifier (with type real) for π declared in Math The type is automatically inferred in the answer: val circlearea = fn : real -> real Applications of the function: circlearea 1.0; (* this is a comment *) val it = : real circlearea(3.2); (* brackets are optional *) val it = : real c Michael R. Hansen, Fall 2004 p.7/15

25 Recursion: n! = n, n 0 Mathematical definition: 0! = 1 (i) n! = n (n 1)!, for n > 0 (ii) recursion formula Computation: 3! = 3 (3 1)! (ii) = 3 2 (2 1)! (ii) = (1 1)! (ii) = (i) = 6 c Michael R. Hansen, Fall 2004 p.8/15

26 Recursive declaration: n! Function declaration: fun fact 0 = 1 (* i *) fact n = n * fact(n-1) (* ii *) val fact = fn : int -> int Evaluation: fact(3) 3 fact(3 1) (ii) 3 2 fact(2 1) (ii) fact(1 1) (ii) (i) 6 e 1 e 2 reads: e 1 evaluates to e 2 c Michael R. Hansen, Fall 2004 p.9/15

27 Recursive declaration: n! Function declaration: fun fact 0 = 1 (* i *) fact n = n * fact(n-1) (* ii *) val fact = fn : int -> int Evaluation: fact(3) 3 fact(3 1) (ii) [n 3] 3 2 fact(2 1) (ii) [n 2] fact(1 1) (ii) [n 1] (i) [n 0] 6 e 1 e 2 reads: e 1 evaluates to e 2 c Michael R. Hansen, Fall 2004 p.9/15

28 Recursion: x n = x... x, n occurrences of x Mathematical definition: x 0 = 1 (1) x n = x x n 1, for n > 0 (2) recursion formula Function declaration: fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) Patterns: (, 0) matches any pair of the form (x, 0). The wildcard pattern _ matches any value. (x, n) matches any pair (u, i) yielding the bindings x u, n i c Michael R. Hansen, Fall 2004 p.10/15

29 Evaluation: power(4.0, 2) Function declaration: fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) Evaluation: power(4.0, 2) 4.0 power(4.0, 2 1) Clause 2, [x 4.0, n 2] 4.0 power(4.0, 1) 4.0 (4.0 power(4.0, 1 1)) Clause 2, [x 4.0, n 1] 4.0 (4.0 power(4.0, 0)) 4.0 (4.0 1) Clause c Michael R. Hansen, Fall 2004 p.11/15

30 If-then-else expressions Form: Evaluation rules: if b then e 1 else e 2 if true then e 1 else e 2 e 1 if false then e 1 else e 2 e 2 Alternative declarations: fun fact n = if n=0 then 1 else n * fact(n-1); fun power(x,n) = if n=0 then 1.0 else x * power(x,n-1); c Michael R. Hansen, Fall 2004 p.12/15

31 If-then-else expressions Form: Evaluation rules: if b then e 1 else e 2 if true then e 1 else e 2 e 1 if false then e 1 else e 2 e 2 Alternative declarations: fun fact n = if n=0 then 1 else n * fact(n-1); fun power(x,n) = if n=0 then 1.0 else x * power(x,n-1); Use of clauses and patterns often give more understandable programs c Michael R. Hansen, Fall 2004 p.12/15

32 Types every expression has a type e : τ type name example of values Integers int 27, 0, 15, Basic types: Reals real 27.3, 0.0, Booleans bool true, false Pairs: If e 1 : τ 1 and e 2 : τ 2 then (e 1, e 2 ) : τ 1 τ 2 pair (tuple) type constructor Examples: (4.0, 2): real*int c Michael R. Hansen, Fall 2004 p.13/15

33 Types every expression has a type e : τ type name example of values Integers int 27, 0, 15, Basic types: Reals real 27.3, 0.0, Booleans bool true, false If e 1 : τ 1 and e 2 : τ 2 Pairs: then (e 1, e 2 ) : τ 1 τ 2 pair (tuple) type constructor if f : τ 1 -> τ 2 and a : τ 1 function type constructor Functions: then f(a) : τ 2 Examples: (4.0, 2): real*int power: real*int -> real power(4.0, 2): real c Michael R. Hansen, Fall 2004 p.13/15

34 Types every expression has a type e : τ type name example of values Integers int 27, 0, 15, Basic types: Reals real 27.3, 0.0, Booleans bool true, false If e 1 : τ 1 and e 2 : τ 2 Pairs: then (e 1, e 2 ) : τ 1 τ 2 pair (tuple) type constructor if f : τ 1 -> τ 2 and a : τ 1 function type constructor Functions: then f(a) : τ 2 Examples: (4.0, 2): real*int power: real*int -> real power(4.0, 2): real * has higher precedence that -> c Michael R. Hansen, Fall 2004 p.13/15

35 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) c Michael R. Hansen, Fall 2004 p.14/15

36 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. c Michael R. Hansen, Fall 2004 p.14/15

37 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. τ 3 = real because 1.0:real (Clause 1, function value.) c Michael R. Hansen, Fall 2004 p.14/15

38 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. τ 3 = real because 1.0:real τ 2 = int because 0:int. (Clause 1, function value.) c Michael R. Hansen, Fall 2004 p.14/15

39 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. τ 3 = real because 1.0:real τ 2 = int because 0:int. x*power(x,n-1):real, because τ 3 = real. (Clause 1, function value.) c Michael R. Hansen, Fall 2004 p.14/15

40 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. τ 3 = real because 1.0:real τ 2 = int because 0:int. x*power(x,n-1):real, because τ 3 = real. multiplication can have (Clause 1, function value.) int*int -> int or real*real -> real as types, but no mixture of int and real c Michael R. Hansen, Fall 2004 p.14/15

41 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. τ 3 = real because 1.0:real τ 2 = int because 0:int. x*power(x,n-1):real, because τ 3 = real. multiplication can have (Clause 1, function value.) int*int -> int or real*real -> real as types, but no mixture of int and real Therefore x:real and τ 1 =real. c Michael R. Hansen, Fall 2004 p.14/15

42 Type inference: power fun power(_,0) = 1.0 (* 1 *) power(x,n) = x * power(x,n-1) (* 2 *) The type of the function must have the form: τ 1 * τ 2 -> τ 3, because argument is a pair. τ 3 = real because 1.0:real τ 2 = int because 0:int. x*power(x,n-1):real, because τ 3 = real. multiplication can have (Clause 1, function value.) int*int -> int or real*real -> real as types, but no mixture of int and real Therefore x:real and τ 1 =real. The SML system determines the type real*int -> real c Michael R. Hansen, Fall 2004 p.14/15

43 Summary The interactive environment Values, expressions, types, patterns Declarations of values and recursive functions Binding, environment and evaluation Type inference Breath first round through many concepts aiming at program construction from the first day. We will go deaper into each of the concepts later in the course. c Michael R. Hansen, Fall 2004 p.15/15