Random(3)

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version 5.5.0 · 19 versions
, in arch rolling

🦋 Random(3) 🦋

okay so bestie, this is the Random page from the OCaml library, and it is literally the Pseudo-random number generators (PRNG) module. yes, the whole vibe is “give me a random number, but make it reproducible when I need it to be.” 💅

🧚 MODULE 🧚

Module Random : sig end

that’s the whole module declaration, bestie. short, iconic, to the point. ✨

🌈 DOCUMENTATION 🌈

Module Random : sig end

Pseudo-random number generators (PRNG). 🎀

With multiple domains, each domain has its own generator that evolves independently of the generators of other domains. When a domain is created, its generator is initialized by splitting the state of the generator associated with the parent domain. 🦋

In contrast, all threads within a domain share the same domain-local generator. Independent generators can be created with the Random.split function and used with the functions from the Random.State module. 🫶

⚠️ Before 5.0 Random value generation used a different algorithm. This affects all the functions in this module which return random values. no cap, this is the lore you need.

🍓 BASIC FUNCTIONS 🍓

  • val init : int -> unit — initialize the domain-local generator, using the argument as a seed. the same seed will always yield the same sequence of numbers. deterministic bestie behavior. 💖
  • val full_init : int array -> unit — same as Random.init but takes more data as seed. more seed energy, same idea. 🌸
  • val self_init : unit -> unit — initialize the domain-local generator with a random seed chosen in a system-dependent way. if a cryptographically secure pseudorandom number generator 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, domain-local state). she’s doing her best with what the system gives her. 😭
  • val bits : unit -> int — return 30 random bits in a nonnegative integer. thirty bits of pure chaos, but make it nonnegative. ✨
  • 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. 🎀

Raises Invalid_argument if bound <= 0 or bound >= 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 2^31, then Random.full_int bound yields identical output across systems with varying int sizes.

If bound is less than 2^30, then Random.full_int bound is equal to Random.int bound.

If bound is at least 2^30 (on 64-bit systems, or non-standard environments such as JavaScript), then Random.full_int returns a value whereas Random.int raises Invalid_argument.

Since 4.13.

Raises Invalid_argument if bound <= 0.

  • val int_in_range : min:int -> max:int -> int — Random.int_in_range ~min ~max returns a random integer between min (inclusive) and max (inclusive). both min and max are allowed to be negative; min must be less than or equal to max. inclusive on both ends, bestie, she’s generous. 💜

If both bounds fit in 32-bit signed integers (that is, if -2^31 <= min and max < 2^31), then int_in_range yields identical output across systems with varying int sizes.

Since 5.2.

Raises Invalid_argument if min > max.

  • 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. 🧚

Raises Invalid_argument if bound <= 0.

  • val int32_in_range : min:int32 -> max:int32 -> int32 — Random.int32_in_range ~min ~max returns a random integer between min (inclusive) and max (inclusive). both min and max are allowed to be negative; min must be less than or equal to max. 🎀

Since 5.2.

Raises Invalid_argument if min > max.

  • 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. 🌟

Raises Invalid_argument if bound <= 0.

  • val nativeint_in_range : min:nativeint -> max:nativeint -> nativeint — Random.nativeint_in_range ~min ~max returns a random integer between min (inclusive) and max (inclusive). both min and max are allowed to be negative; min must be less than or equal to max. 🦋

Since 5.2.

Raises Invalid_argument if min > max.

  • 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. 💫

Raises Invalid_argument if bound <= 0.

  • val int64_in_range : min:int64 -> max:int64 -> int64 — Random.int64_in_range ~min ~max returns a random integer between min (inclusive) and max (inclusive). both min and max are allowed to be negative; min must be less than or equal to max. 🔥

Since 5.2.

Raises Invalid_argument if min > max.

  • 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. floating-point randomness said “I contain multitudes.” 😭
  • val bool : unit -> bool — Random.bool () returns true or false with probability 0.5 each. a coin flip, but make it function-call shaped. 🪙
  • val bits32 : unit -> Int32.t — Random.bits32 () returns 32 random bits as an integer between Int32.min_int and Int32.max_int. ✨

Since 4.14.

  • val bits64 : unit -> Int64.t — Random.bits64 () returns 64 random bits as an integer between Int64.min_int and Int64.max_int. 🌈

Since 4.14.

  • val nativebits : unit -> Nativeint.t — Random.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. platform-dependent bit-width realness. 💅

Since 4.14.

🔥 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. advanced bestie mode: activated. 👑

  • module State : sig end — the state-handling submodule, bestie. this is where the explicit-state functions live. 🎀
  • val get_state : unit -> State.t — get_state() returns a fresh copy of the current state of the domain-local generator (which is used by the basic functions). 🌸

The current state is unchanged, which means that random numbers generated from multiple calls to get_state () will be identical.

See Random.State.make_self_init and Random.split instead to obtain a fresh state that is not shared.

  • val set_state : State.t -> unit — set_state s updates the current state of the domain-local generator (which is used by the basic functions) by copying the state s into it. slay, state transplant. 💖
  • val split : unit -> State.t — draw a fresh PRNG state from the current state of the domain-local generator used by the default functions. (The state of the domain-local generator is modified.) see Random.State.split. 🦋

Since 5.0.

🫶 that’s the whole page, bestie — brought to you by OCamldoc, dated 2026-06-22, for Random(3). mwah 💋
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