Chapter 1 | The core language

This part of the manual is a tutorial introduction to the Objective Caml language. A good familiarity with programming in a conventional languages (say, Pascal or C) is assumed, but no prior exposure to functional languages is required. The present chapter introduces the core language. Chapter 3 deals with the object-oriented features, and chapter 2 with the module system.

1.1 | Basics

For this overview of Caml, we use the interactive system, which is started by running `ocaml` from the Unix shell, or by launching the `OCamlwin.exe` application under Windows. This tutorial is presented as the transcript of a session with the interactive system: lines starting with `#` represent user input; the system responses are printed below, without a leading `#`.
Under the interactive system, the user types Caml phrases, terminated by `;;`, in response to the `#` prompt, and the system compiles them on the fly, executes them, and prints the outcome of evaluation. Phrases are either simple expressions, or `let` definitions of identifiers (either values or functions).

    #1+2*3;;
    - : int = 7

    #let pi = 4.0 *. atan 1.0;;
    val pi : float = 3.14159265358979312

    #let square x = x *. x;;
    val square : float -> float = <fun>

    #square(sin pi) +. square(cos pi);;
    - : float = 1.

The Caml system computes both the value and the type for each phrase. Even function parameters need no explicit type declaration: the system infers their types from their usage in the function. Notice also that integers and floating-point numbers are distinct types, with distinct operators: `+` and `*` operate on integers, but `+.` and `*.` operate on floats.

    #1.0 * 2;;
    This expression has type float but is here used with type int

Recursive functions are defined with the `let rec` binding:

    #let rec fib n =
       if n < 2 then 1 else fib(n-1) + fib(n-2);;
    val fib : int -> int = <fun>

    #fib 10;;
    - : int = 89

1.2 | Data types

In addition to integers and floating-point numbers, Caml offers the usual basic data types: booleans, characters, and character strings.

    #(1 < 2) = false;;
    - : bool = false

    #'a';;
    - : char = 'a'

    #"Hello world";;
    - : string = "Hello world"

Predefined data structures include tuples, arrays, and lists. General mechanisms for defining your own data structures are also provided. They will be covered in more details later; for now, we concentrate on lists. Lists are either given in extension as a bracketed list of semicolon-separated elements, or built from the empty list `[]` (pronounce ``nil'') by adding elements in front using the `::` (``cons'') operator.

    #let l = ["is"; "a"; "tale"; "told"; "etc."];;
    val l : string list = ["is"; "a"; "tale"; "told"; "etc."]

    #"Life" :: l;;
    - : string list = ["Life"; "is"; "a"; "tale"; "told"; "etc."]

As with all other Caml data structures, lists do not need to be explicitly allocated and deallocated from memory: all memory management is entirely automatic in Caml. Similarly, there is no explicit handling of pointers: the Caml compiler silently introduces pointers where necessary.
As with most Caml data structures, inspecting and destructuring lists is performed by pattern-matching. List patterns have the exact same shape as list expressions, with identifier representing unspecified parts of the list. As an example, here is insertion sort on a list:

    #let rec sort lst =
       match lst with
         [] -> []
       | head :: tail -> insert head (sort tail)
     and insert elt lst =
       match lst with
         [] -> [elt]
       | head :: tail -> if elt <= head then elt :: lst else head :: insert elt tail
     ;;
    val sort : 'a list -> 'a list = <fun>
    val insert : 'a -> 'a list -> 'a list = <fun>

    #sort l;;
    - : string list = ["a"; "etc."; "is"; "tale"; "told"]

The type inferred for `sort`, `'a list -> 'a list`, means that `sort` can actually apply to lists of any type, and returns a list of the same type. The type `'a` is a type variable, and stands for any given type. The reason why `sort` can apply to lists of any type is that the comparisons (`=`, `<=`, etc.) are polymorphic in Caml: they operate between any two values of the same type. This makes `sort` itself polymorphic over all list types.

    #sort [6;2;5;3];;
    - : int list = [2; 3; 5; 6]

    #sort [3.14; 2.718];;
    - : float list = [2.718; 3.14]

The `sort` function above does not modify its input list: it builds and returns a new list containing the same elements as the input list, in ascending order. There is actually no way in Caml to modify in-place a list once it is built: we say that lists are immutable data structures. Most Caml data structures are immutable, but a few (most notably arrays) are mutable, meaning that they can be modified in-place at any time.

1.3 | Functions as values

Caml is a functional language: functions in the full mathematical sense are supported and can be passed around freely just as any other piece of data. For instance, here is a `deriv` function that takes any float function as argument and returns an approximation of its derivative function:

    #let deriv f dx = function x -> (f(x +. dx) -. f(x)) /. dx;;
    val deriv : (float -> float) -> float -> float -> float = <fun>

    #let sin' = deriv sin 1e-6;;
    val sin' : float -> float = <fun>

    #sin' pi;;
    - : float = -1.00000000013961143

Even function composition is definable:

    #let compose f g = function x -> f(g(x));;
    val compose : ('a -> 'b) -> ('c -> 'a) -> 'c -> 'b = <fun>

    #let cos2 = compose square cos;;
    val cos2 : float -> float = <fun>

Functions that take other functions as arguments are called ``functionals'', or ``higher-order functions''. Functionals are especially useful to provide iterators or similar generic operations over a data structure. For instance, the standard Caml library provides a `List.map` functional that applies a given function to each element of a list, and returns the list of the results:

    #List.map (function n -> n * 2 + 1) [0;1;2;3;4];;
    - : int list = [1; 3; 5; 7; 9]

This functional, along with a number of other list and array functionals, is predefined because it is often useful, but there is nothing magic with it: it can easily be defined as follows.

    #let rec map f l =
       match l with
         [] -> []
       | hd :: tl -> f hd :: map f tl;;
    val map : ('a -> 'b) -> 'a list -> 'b list = <fun>

1.4 | Records and variants

User-defined data structures include records and variants. Both are defined with the `type` declaration. Here, we declare a record type to represent rational numbers.

    #type ratio = {num: int; denum: int};;
    type ratio = { num : int; denum : int; }

    #let add_ratio r1 r2 =
       {num = r1.num * r2.denum + r2.num * r1.denum;
        denum = r1.denum * r2.denum};;
    val add_ratio : ratio -> ratio -> ratio = <fun>

    #add_ratio {num=1; denum=3} {num=2; denum=5};;
    - : ratio = {num = 11; denum = 15}

The declaration of a variant type lists all possible shapes for values of that type. Each case is identified by a name, called a constructor, which serves both for constructing values of the variant type and inspecting them by pattern-matching. Constructor names are capitalized to distinguish them from variable names (which must start with a lowercase letter). For instance, here is a variant type for doing mixed arithmetic (integers and floats):

    #type number = Int of int | Float of float | Error;;
    type number = Int of int | Float of float | Error

This declaration expresses that a value of type `number` is either an integer, a floating-point number, or the constant `Error` representing the result of an invalid operation (e.g. a division by zero).
Enumerated types are a special case of variant types, where all alternatives are constants:

    #type sign = Positive | Negative;;
    type sign = Positive | Negative

    #let sign_int n = if n >= 0 then Positive else Negative;;
    val sign_int : int -> sign = <fun>

To define arithmetic operations for the `number` type, we use pattern-matching on the two numbers involved:

    #let add_num n1 n2 =
       match (n1, n2) with
         (Int i1, Int i2) ->
           (* Check for overflow of integer addition *)
           if sign_int i1 = sign_int i2 && sign_int(i1 + i2) <> sign_int i1
           then Float(float i1 +. float i2)
           else Int(i1 + i2)
       | (Int i1, Float f2) -> Float(float i1 +. f2)
       | (Float f1, Int i2) -> Float(f1 +. float i2)
       | (Float f1, Float f2) -> Float(f1 +. f2)
       | (Error, _) -> Error
       | (_, Error) -> Error;;
    val add_num : number -> number -> number = <fun>

    #add_num (Int 123) (Float 3.14159);;
    - : number = Float 126.14159

The most common usage of variant types is to describe recursive data structures. Consider for example the type of binary trees:

    #type 'a btree = Empty | Node of 'a * 'a btree * 'a btree;;
    type 'a btree = Empty | Node of 'a * 'a btree * 'a btree

This definition reads as follow: a binary tree containing values of type `'a` (an arbitrary type) is either empty, or is a node containing one value of type `'a` and two subtrees containing also values of type `'a`, that is, two `'a btree`.
Operations on binary trees are naturally expressed as recursive functions following the same structure as the type definition itself. For instance, here are functions performing lookup and insertion in ordered binary trees (elements increase from left to right):

    #let rec member x btree =
       match btree with
         Empty -> false
       | Node(y, left, right) ->
           if x = y then true else
           if x < y then member x left else member x right;;
    val member : 'a -> 'a btree -> bool = <fun>

    #let rec insert x btree =
       match btree with
         Empty -> Node(x, Empty, Empty)
       | Node(y, left, right) ->
           if x <= y then Node(y, insert x left, right)
                     else Node(y, left, insert x right);;
    val insert : 'a -> 'a btree -> 'a btree = <fun>

1.5 | Imperative features

Though all examples so far were written in purely applicative style, Caml is also equipped with full imperative features. This includes the usual `while` and `for` loops, as well as mutable data structures such as arrays. Arrays are either given in extension between `[|` and `|]` brackets, or allocated and initialized with the `Array.create` function, then filled up later by assignments. For instance, the function below sums two vectors (represented as float arrays) componentwise.

    #let add_vect v1 v2 =
       let len = min (Array.length v1) (Array.length v2) in
       let res = Array.create len 0.0 in
       for i = 0 to len - 1 do
         res.(i) <- v1.(i) +. v2.(i)
       done;
       res;;
    val add_vect : float array -> float array -> float array = <fun>

    #add_vect [| 1.0; 2.0 |] [| 3.0; 4.0 |];;
    - : float array = [|4.; 6.|]

Record fields can also be modified by assignment, provided they are declared `mutable` in the definition of the record type:

    #type mutable_point = { mutable x: float; mutable y: float };;
    type mutable_point = { mutable x : float; mutable y : float; }

    #let translate p dx dy =
       p.x <- p.x +. dx; p.y <- p.y +. dy;;
    val translate : mutable_point -> float -> float -> unit = <fun>

    #let mypoint = { x = 0.0; y = 0.0 };;
    val mypoint : mutable_point = {x = 0.; y = 0.}

    #translate mypoint 1.0 2.0;;
    - : unit = ()

    #mypoint;;
    - : mutable_point = {x = 1.; y = 2.}

Caml has no built-in notion of variable -- identifiers whose current value can be changed by assignment. (The `let` binding is not an assignment, it introduces a new identifier with a new scope.) However, the standard library provides references, which are mutable indirection cells (or one-element arrays), with operators `!` to fetch the current contents of the reference and `:=` to assign the contents. Variables can then be emulated by `let`-binding a reference. For instance, here is an in-place insertion sort over arrays:

    #let insertion_sort a =
       for i = 1 to Array.length a - 1 do
         let val_i = a.(i) in
         let j = ref i in
         while !j > 0 && val_i < a.(!j - 1) do
           a.(!j) <- a.(!j - 1);
           j := !j - 1
         done;
         a.(!j) <- val_i
       done;;
    val insertion_sort : 'a array -> unit = <fun>

References are also useful to write functions that maintain a current state between two calls to the function. For instance, the following pseudo-random number generator keeps the last returned number in a reference:

    #let current_rand = ref 0;;
    val current_rand : int ref = {contents = 0}

    #let random () =
       current_rand := !current_rand * 25713 + 1345;
       !current_rand;;
    val random : unit -> int = <fun>

Again, there is nothing magic with references: they are implemented as a one-field mutable record, as follows.

    #type 'a ref = { mutable contents: 'a };;
    type 'a ref = { mutable contents : 'a; }

    #let (!) r = r.contents;;
    val ( ! ) : 'a ref -> 'a = <fun>

    #let (:=) r newval = r.contents <- newval;;
    val ( := ) : 'a ref -> 'a -> unit = <fun>

In some special cases, you may need to store a polymorphic function in a data structure, keeping its polymorphism. Without user-provided type annotations, this is not allowed, as polymorphism is only introduced on a global level. However, you can give explicitly polymorphic types to record fields.

    #type idref = { mutable id: 'a. 'a -> 'a };;
    type idref = { mutable id : 'a. 'a -> 'a; }

    #let r = {id = fun x -> x};;
    val r : idref = {id = <fun>}

    #let g s = (s.id 1, s.id true);;
    val g : idref -> int * bool = <fun>

    #r.id <- (fun x -> print_string "called id\n"; x);;
    - : unit = ()

    #g r;;
    called id
    called id
    - : int * bool = (1, true)

1.6 | Exceptions

Caml provides exceptions for signalling and handling exceptional conditions. Exceptions can also be used as a general-purpose non-local control structure. Exceptions are declared with the `exception` construct, and signalled with the `raise` operator. For instance, the function below for taking the head of a list uses an exception to signal the case where an empty list is given.

    #exception Empty_list;;
    exception Empty_list

    #let head l =
       match l with
         [] -> raise Empty_list
       | hd :: tl -> hd;;
    val head : 'a list -> 'a = <fun>

    #head [1;2];;
    - : int = 1

    #head [];;
    Exception: Empty_list.

Exceptions are used throughout the standard library to signal cases where the library functions cannot complete normally. For instance, the `List.assoc` function, which returns the data associated with a given key in a list of (key, data) pairs, raises the predefined exception `Not_found` when the key does not appear in the list:

    #List.assoc 1 [(0, "zero"); (1, "one")];;
    - : string = "one"

    #List.assoc 2 [(0, "zero"); (1, "one")];;
    Exception: Not_found.

Exceptions can be trapped with the `try`...`with` construct:

    #let name_of_binary_digit digit =
       try
         List.assoc digit [0, "zero"; 1, "one"]
       with Not_found ->
         "not a binary digit";;
    val name_of_binary_digit : int -> string = <fun>

    #name_of_binary_digit 0;;
    - : string = "zero"

    #name_of_binary_digit (-1);;
    - : string = "not a binary digit"

The `with` part is actually a regular pattern-matching on the exception value. Thus, several exceptions can be caught by one `try`...`with` construct. Also, finalization can be performed by trapping all exceptions, performing the finalization, then raising again the exception:

    #let temporarily_set_reference ref newval funct =
       let oldval = !ref in
       try
         ref := newval;
         let res = funct () in
         ref := oldval;
         res
       with x ->
         ref := oldval;
         raise x;;
    val temporarily_set_reference : 'a ref -> 'a -> (unit -> 'b) -> 'b = <fun>

1.7 | Symbolic processing of expressions

We finish this introduction with a more complete example representative of the use of Caml for symbolic processing: formal manipulations of arithmetic expressions containing variables. The following variant type describes the expressions we shall manipulate:

    #type expression =
         Const of float
       | Var of string
       | Sum of expression * expression    (* e1 + e2 *)
       | Diff of expression * expression   (* e1 - e2 *)
       | Prod of expression * expression   (* e1 * e2 *)
       | Quot of expression * expression   (* e1 / e2 *)
     ;;
    type expression =
        Const of float
      | Var of string
      | Sum of expression * expression
      | Diff of expression * expression
      | Prod of expression * expression
      | Quot of expression * expression

We first define a function to evaluate an expression given an environment that maps variable names to their values. For simplicity, the environment is represented as an association list.

    #exception Unbound_variable of string;;
    exception Unbound_variable of string

    #let rec eval env exp =
       match exp with
         Const c -> c
       | Var v ->
           (try List.assoc v env with Not_found -> raise(Unbound_variable v))
       | Sum(f, g) -> eval env f +. eval env g
       | Diff(f, g) -> eval env f -. eval env g
       | Prod(f, g) -> eval env f *. eval env g
       | Quot(f, g) -> eval env f /. eval env g;;
    val eval : (string * float) list -> expression -> float = <fun>

    #eval [("x", 1.0); ("y", 3.14)] (Prod(Sum(Var "x", Const 2.0), Var "y"));;
    - : float = 9.42

Now for a real symbolic processing, we define the derivative of an expression with respect to a variable `dv`:

    #let rec deriv exp dv =
       match exp with
         Const c -> Const 0.0
       | Var v -> if v = dv then Const 1.0 else Const 0.0
       | Sum(f, g) -> Sum(deriv f dv, deriv g dv)
       | Diff(f, g) -> Diff(deriv f dv, deriv g dv)
       | Prod(f, g) -> Sum(Prod(f, deriv g dv), Prod(deriv f dv, g))
       | Quot(f, g) -> Quot(Diff(Prod(deriv f dv, g), Prod(f, deriv g dv)),
                            Prod(g, g))
     ;;
    val deriv : expression -> string -> expression = <fun>

    #deriv (Quot(Const 1.0, Var "x")) "x";;
    - : expression =
    Quot (Diff (Prod (Const 0., Var "x"), Prod (Const 1., Const 1.)),
     Prod (Var "x", Var "x"))

1.8 | Pretty-printing and parsing

As shown in the examples above, the internal representation (also called abstract syntax) of expressions quickly becomes hard to read and write as the expressions get larger. We need a printer and a parser to go back and forth between the abstract syntax and the concrete syntax, which in the case of expressions is the familiar algebraic notation (e.g. `2*x+1`).
For the printing function, we take into account the usual precedence rules (i.e. `*` binds tighter than `+`) to avoid printing unnecessary parentheses. To this end, we maintain the current operator precedence and print parentheses around an operator only if its precedence is less than the current precedence.

    #let print_expr exp =
       (* Local function definitions *)
       let open_paren prec op_prec =
         if prec > op_prec then print_string "(" in
       let close_paren prec op_prec =
         if prec > op_prec then print_string ")" in
       let rec print prec exp =     (* prec is the current precedence *)
         match exp with
           Const c -> print_float c
         | Var v -> print_string v
         | Sum(f, g) ->
             open_paren prec 0;
             print 0 f; print_string " + "; print 0 g;
             close_paren prec 0
         | Diff(f, g) ->
             open_paren prec 0;
             print 0 f; print_string " - "; print 1 g;
             close_paren prec 0
         | Prod(f, g) ->
             open_paren prec 2;
             print 2 f; print_string " * "; print 2 g;
             close_paren prec 2
         | Quot(f, g) ->
             open_paren prec 2;
             print 2 f; print_string " / "; print 3 g;
             close_paren prec 2
       in print 0 exp;;
    val print_expr : expression -> unit = <fun>

    #let e = Sum(Prod(Const 2.0, Var "x"), Const 1.0);;
    val e : expression = Sum (Prod (Const 2., Var "x"), Const 1.)

    #print_expr e; print_newline();;
    2. * x + 1.
    - : unit = ()

    #print_expr (deriv e "x"); print_newline();;
    2. * 1. + 0. * x + 0.
    - : unit = ()

Parsing (transforming concrete syntax into abstract syntax) is usually more delicate. Caml offers several tools to help write parsers: on the one hand, Caml versions of the lexer generator Lex and the parser generator Yacc (see chapter 12), which handle LALR(1) languages using push-down automata; on the other hand, a predefined type of streams (of characters or tokens) and pattern-matching over streams, which facilitate the writing of recursive-descent parsers for LL(1) languages. An example using `ocamllex` and `ocamlyacc` is given in chapter 12. Here, we will use stream parsers. The syntactic support for stream parsers is provided by the Camlp4 preprocessor, which can be loaded into the interactive toplevel via the `#load` directive below.

    ##load "camlp4o.cma";;
    	Camlp4 Parsing version 3.07

    #open Genlex;;

     let lexer = make_lexer ["("; ")"; "+"; "-"; "*"; "/"];;
    val lexer : char Stream.t -> Genlex.token Stream.t = <fun>

For the lexical analysis phase (transformation of the input text into a stream of tokens), we use a ``generic'' lexer provided in the standard library module `Genlex`. The `make_lexer` function takes a list of keywords and returns a lexing function that ``tokenizes'' an input stream of characters. Tokens are either identifiers, keywords, or literals (integer, floats, characters, strings). Whitespace and comments are skipped.

    #let token_stream = lexer(Stream.of_string "1.0 +x");;
    val token_stream : Genlex.token Stream.t = <abstr>

    #Stream.next token_stream;;
    - : Genlex.token = Float 1.

    #Stream.next token_stream;;
    - : Genlex.token = Kwd "+"

    #Stream.next token_stream;;
    - : Genlex.token = Ident "x"

The parser itself operates by pattern-matching on the stream of tokens. As usual with recursive descent parsers, we use several intermediate parsing functions to reflect the precedence and associativity of operators. Pattern-matching over streams is more powerful than on regular data structures, as it allows recursive calls to parsing functions inside the patterns, for matching sub-components of the input stream. See chapter 7 for more details.
In order to use stream parsers at toplevel, we must first load the `camlp4` preprocessor.

    ##load"camlp4o.cma";;
    	Camlp4 Parsing version 3.07

Then we are ready to define our parser.

    #let rec parse_expr = parser
         [< e1 = parse_mult; e = parse_more_adds e1 >] -> e
     and parse_more_adds e1 = parser
         [< 'Kwd "+"; e2 = parse_mult; e = parse_more_adds (Sum(e1, e2)) >] -> e
       | [< 'Kwd "-"; e2 = parse_mult; e = parse_more_adds (Diff(e1, e2)) >] -> e
       | [< >] -> e1
     and parse_mult = parser
         [< e1 = parse_simple; e = parse_more_mults e1 >] -> e
     and parse_more_mults e1 = parser
         [< 'Kwd "*"; e2 = parse_simple; e = parse_more_mults (Prod(e1, e2)) >] -> e
       | [< 'Kwd "/"; e2 = parse_simple; e = parse_more_mults (Quot(e1, e2)) >] -> e
       | [< >] -> e1
     and parse_simple = parser
         [< 'Ident s >] -> Var s
       | [< 'Int i >] -> Const(float i)
       | [< 'Float f >] -> Const f
       | [< 'Kwd "("; e = parse_expr; 'Kwd ")" >] -> e;;
    val parse_expr : Genlex.token Stream.t -> expression = <fun>
    val parse_more_adds : expression -> Genlex.token Stream.t -> expression =
      <fun>
    val parse_mult : Genlex.token Stream.t -> expression = <fun>
    val parse_more_mults : expression -> Genlex.token Stream.t -> expression =
      <fun>
    val parse_simple : Genlex.token Stream.t -> expression = <fun>

    #let parse_expression = parser [< e = parse_expr; _ = Stream.empty >] -> e;;
    val parse_expression : Genlex.token Stream.t -> expression = <fun>

Composing the lexer and parser, we finally obtain a function to read an expression from a character string:

    #let read_expression s = parse_expression(lexer(Stream.of_string s));;
    val read_expression : string -> expression = <fun>

    #read_expression "2*(x+y)";;
    - : expression = Prod (Const 2., Sum (Var "x", Var "y"))

A small puzzle: why do we get different results in the following two examples?

    #read_expression "x - 1";;
    - : expression = Diff (Var "x", Const 1.)

    #read_expression "x-1";;
    Exception: Stream.Error "".

Answer: the generic lexer provided by `Genlex` recognizes negative integer literals as one integer token. Hence, `x-1` is read as the token `Ident "x"` followed by the token `Int(-1)`; this sequence does not match any of the parser rules. On the other hand, the second space in `x - 1` causes the lexer to return the three expected tokens: `Ident "x"`, then `Kwd "-"`, then `Int(1)`.

1.9 | Standalone Caml programs

All examples given so far were executed under the interactive system. Caml code can also be compiled separately and executed non-interactively using the batch compilers `ocamlc` or `ocamlopt`. The source code must be put in a file with extension `.ml`. It consists of a sequence of phrases, which will be evaluated at runtime in their order of appearance in the source file. Unlike in interactive mode, types and values are not printed automatically; the program must call printing functions explicitly to produce some output. Here is a sample standalone program to print Fibonacci numbers:

    (* File fib.ml *)
    let rec fib n =
      if n < 2 then 1 else fib(n-1) + fib(n-2);;
    let main () =
      let arg = int_of_string Sys.argv.(1) in
      print_int(fib arg);
      print_newline();
      exit 0;;
    main ();;

`Sys.argv` is an array of strings containing the command-line parameters. `Sys.argv.(1)` is thus the first command-line parameter. The program above is compiled and executed with the following shell commands:

    $ ocamlc -o fib fib.ml
    $ ./fib 10
    89
    $ ./fib 20
    10946