mir-random 0.0.1-beta5

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