Basic functions

val init : int -> unit

Initialize the generator, using the argument as a seed. The same seed will always yield the same sequence of numbers.

val full_init : int array -> unit

Same as Random.init but takes more data as seed.

val self_init : unit -> unit

Initialize the generator with a random seed chosen in a system-dependent way. If /dev/urandom is available on the host machine, it is used to provide a highly random initial seed. Otherwise, a less random seed is computed from system parameters (current time, process IDs).

val bits : unit -> int

Return 30 random bits in a nonnegative integer.

Before3.12.0 used a different algorithm (affects all the following functions)

val int : int -> int

Random.int bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0 and less than 2^30.

val full_int : int -> int

Random.full_int bound returns a random integer between 0 (inclusive) and bound (exclusive). bound may be any positive integer.

If bound is less than 2^30, Random.full_int bound is equal to Random.int bound . If bound is greater than 2^30 (on 64-bit systems or non-standard environments, such as JavaScript), Random.full_int returns a value, where Random.int raises Invalid_argument .

Since 4.13.0

val int32 : Int32.t -> Int32.t

Random.int32 bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0.

val nativeint : Nativeint.t -> Nativeint.t

Random.nativeint bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0.

val int64 : Int64.t -> Int64.t

Random.int64 bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0.

val float : float -> float

Random.float bound returns a random floating-point number between 0 and bound (inclusive). If bound is negative, the result is negative or zero. If bound is 0, the result is 0.

val bool : unit -> bool

Random.bool () returns true or false with probability 0.5 each.

Advanced functions

The functions from module Random.State manipulate the current state of the random generator explicitly. This allows using one or several deterministic PRNGs, even in a multi-threaded program, without interference from other parts of the program.

module State : sig end

val get_state : unit -> State.t

Return the current state of the generator used by the basic functions.

val set_state : State.t -> unit

Set the state of the generator used by the basic functions.