Stdlib.RandomPseudo-random number generators (PRNG).
Initialize the generator, using the argument as a seed. The same seed will always yield the same sequence of numbers.
Same as Random.init but takes more data as seed.
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).
Random.int bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0 and less than 230.
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 230, Random.full_int bound is equal to Random.int bound. If bound is greater than 230 (on 64-bit systems or non-standard environments, such as JavaScript), Random.full_int returns a value, where Random.int raises Invalid_argument.
Random.int32 bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0.
val nativeint : Nativeint.t -> Nativeint.tRandom.nativeint bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0.
Random.int64 bound returns a random integer between 0 (inclusive) and bound (exclusive). bound must be greater than 0.
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 bits32 : unit -> Int32.tRandom.bits32 () returns 32 random bits as an integer between Int32.min_int and Int32.max_int.
val bits64 : unit -> Int64.tRandom.bits64 () returns 64 random bits as an integer between Int64.min_int and Int64.max_int.
val nativebits : unit -> Nativeint.tRandom.nativebits () returns 32 or 64 random bits (depending on the bit width of the platform) as an integer between Nativeint.min_int and Nativeint.max_int.
The functions from module 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 ... endval get_state : unit -> State.tReturn the current state of the generator used by the basic functions.
val set_state : State.t -> unitSet the state of the generator used by the basic functions.