mir-random 0.0.1-beta4

Dlang Random Number Generators


To use this package, run the following command in your project's root directory:

Manual usage
Put the following dependency into your project's dependences section:

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mir-random

Professional Random Number Generators

Documentation: http://docs.random.dlang.io/latest/index.html

import std.range, std.stdio;

import mir.random;
import mir.random.variable: NormalVariable;
import mir.random.algorithm: randomRange;

auto rng = Random(unpredictableSeed);        // Engines are allocated on stack or global
auto sample = rng                            // Engines are passed by reference to algorithms
    .randomRange(NormalVariable!double(0, 1))// Random variables are passed by value
    .take(1000)                              // Fix sample length to 1000 elements (Input Range API)
    .array;                                  // Allocates memory and performs computation

writeln(sample);                             

Comparison with Phobos

  • Does not depend on DRuntime (Better C concept)
random (new implementation and API)
  • Mir Random rand!float/rand!double/rand!real generates saturated real random numbers in (-1, 1). For example, rand!real can produce more then 2^78 unique numbers. In other hand, std.random.uniform01!real produces less then 2^31 unique numbers with default Engine.
  • Mir Random fixes Phobos integer underflow bugs.
  • Addition optization was added for enumerated types.
ramdom.variable (new)
  • Uniform
  • Exponential
  • Gamma
  • Normal
  • Cauchy
  • ...
random.algorithm (new)
  • Range API adaptors
random.engine.* (fixed, reworked)
  • opCall API instead of range interface is used (similar to C++)
  • No default and copy constructors are allowed for generators.
  • @RandomEngine UDA is used for for Engines instead of a enum flag.
  • unpredictableSeed has not state, returns size_t
  • Any unsigned generators are allowed.
  • min proporty was removed. Any integer generator can normalize its minimum down to zero.
  • Mt19937: +100% performance for initialization.
  • Mt19937: +54% performance for generation.
  • Mt19937: fixed to be more CPU cache friendly.
  • 64-bit Mt19937 initialization is fixed
  • 64-bit Mt19937 is default for 64-bit targets
  • [WIP] additional Engines, see https://github.com/libmir/mir-random/pulls
Authors:
  • Ilya Yaroshenko
  • Andrei Alexandrescu
  • Masahiro Nakagawa
Dependencies:
mir-math
Versions:
2.2.19 2021-Sep-01
2.2.18 2021-May-20
2.2.17 2021-May-20
2.2.16 2021-Mar-22
2.2.15 2020-Sep-01
Show all 68 versions
Download Stats:
  • 49 downloads today

  • 309 downloads this week

  • 1796 downloads this month

  • 291003 downloads total

Score:
4.7
Short URL:
mir-random.dub.pm