package neural_nets_lib

  1. Overview
  2. Docs
A from-scratch Deep Learning framework with an optimizing compiler, shape inference, concise syntax

Install 

dune-project
 Dependency

github.com Readme Changelog Edit opam file Versions (6)

Authors

  1. L
    Lukasz Stafiniak

Maintainers

  1. L
    Lukasz Stafiniak <lukstafi@gmail.com>

Sources

0.6.1.2.tar.gz
md5=529f0921963a6eee0194159a9c0fea41
sha512=fc16e8b6cd72cb2ae18277b3727d065fa6c1d137e3187f9586fb0bfe7edeb45597cb58f389e79c20d7e3ae80661e6f9f20e0b95dcbbf27ee5688bcc571d395dd

doc/src/neural_nets_lib.datasets/rand.ml.html

Source file rand.ml

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(** Random number generator library with low quality but very reliable reproducibility. *)

module type Random = sig
  val init : int -> unit
  val float_range : float -> float -> float
  val char : unit -> char
  val int : int -> int
end

module Random_for_tests : Random = struct
  let rand = ref (1l : Int32.t)

  let rand_int32 () =
    let open Int32 in
    rand := logxor !rand (shift_left !rand 13);
    rand := logxor !rand (shift_right_logical !rand 17);
    rand := logxor !rand (shift_left !rand 5);
    !rand

  let init seed = rand := Int32.(add (of_int seed) 1l)

  let float_range low high =
    let raw = Int32.(to_float @@ rem (rand_int32 ()) 10000l) in
    (raw /. 10000. *. (high -. low)) +. low

  let char () = Char.chr @@ Int32.(to_int @@ rem (rand_int32 ()) 256l)
  let int high = Int32.(to_int @@ rem (rand_int32 ()) @@ of_int high)
end

module Random_for_dummy_tests : Random = struct
  let rand = ref (1l : Int32.t)

  let rand_int32 () =
    let open Int32 in
    rand := add !rand 1l;
    if equal !rand 10000l then rand := 1l;
    !rand

  let init seed = rand := Int32.(add (of_int seed) 1l)

  let float_range low high =
    let raw = Int32.(to_float @@ rand_int32 ()) in
    (raw /. 10000. *. (high -. low)) +. low

  let char () = Char.chr @@ Int32.(to_int @@ rem (rand_int32 ()) 256l)
  let int high = Int32.(to_int @@ rem (rand_int32 ()) @@ of_int high)
end