package neural_nets_lib

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in-package search v0.2.0

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 (5)

Authors

L

Lukasz Stafiniak

Maintainers

L

Lukasz Stafiniak <lukstafi@gmail.com>

Sources

0.6.0.4.tar.gz

md5=5beaaa0b377bec3badffffbf9f4dec4a

sha512=a37a67452746143f0f5ba2e81f98d6fed31fb4397e0a85f4a35aedc805b4e0405ea89d465c6f80941c465fb61d5d6119806cb73b5c5ead925797eb80d19c5ade

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