Standard library header <random>

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This header is part of the pseudo-random number generation library.

Uniform random bit generator requirements
specifies that a type qualifies as a uniform random bit generator
(concept)
Random number engines
implements linear congruential algorithm
(class template)
implements Mersenne twister algorithm
(class template)
implements a subtract-with-carry ( lagged Fibonacci) algorithm
(class template)
Random number engine adaptors
discards some output of a random number engine
(class template)
packs the output of a random number engine into blocks of a specified number of bits
(class template)
delivers the output of a random number engine in a different order
(class template)
Predefined generators
minstd_rand0 std::linear_congruential_engine<std::uint_fast32_t, 16807, 0, 2147483647>

Discovered in 1969 by Lewis, Goodman and Miller, adopted as "Minimal standard" in 1988 by Park and Miller

minstd_rand std::linear_congruential_engine<std::uint_fast32_t, 48271, 0, 2147483647>

Newer "Minimum standard", recommended by Park, Miller, and Stockmeyer in 1993

mt19937

std::mersenne_twister_engine<std::uint_fast32_t, 32, 624, 397, 31,
0x9908b0df, 11,
0xffffffff, 7,
0x9d2c5680, 15,
0xefc60000, 18, 1812433253>

32-bit Mersenne Twister by Matsumoto and Nishimura, 1998

mt19937_64

std::mersenne_twister_engine<std::uint_fast64_t, 64, 312, 156, 31,
0xb5026f5aa96619e9, 29,
0x5555555555555555, 17,
0x71d67fffeda60000, 37,
0xfff7eee000000000, 43, 6364136223846793005>

64-bit Mersenne Twister by Matsumoto and Nishimura, 2000

ranlux24_base std::subtract_with_carry_engine<std::uint_fast32_t, 24, 10, 24>
ranlux48_base std::subtract_with_carry_engine<std::uint_fast64_t, 48, 5, 12>
ranlux24 std::discard_block_engine<std::ranlux24_base, 223, 23>

24-bit RANLUX generator by Martin Lscher and Fred James, 1994

ranlux48 std::discard_block_engine<std::ranlux48_base, 389, 11>

48-bit RANLUX generator by Martin Lscher and Fred James, 1994

knuth_b std::shuffle_order_engine<std::minstd_rand0, 256>
default_random_engine implementation-defined
Non-deterministic random numbers
non-deterministic random number generator using hardware entropy source
(class)
Uniform distributions
produces integer values evenly distributed across a range
(class template)
produces real values evenly distributed across a range
(class template)
Bernoulli distributions
produces bool values on a Bernoulli distribution.
(class)
produces integer values on a binomial distribution.
(class template)
produces integer values on a negative binomial distribution.
(class template)
produces integer values on a geometric distribution.
(class template)
Poisson distributions
produces integer values on a poisson distribution.
(class template)
produces real values on an exponential distribution.
(class template)
produces real values on an gamma distribution.
(class template)
produces real values on a Weibull distribution.
(class template)
produces real values on an extreme value distribution.
(class template)
Normal distributions
produces real values on a standard normal (Gaussian) distribution.
(class template)
produces real values on a lognormal distribution.
(class template)
produces real values on a chi-squared distribution.
(class template)
produces real values on a Cauchy distribution.
(class template)
produces real values on a Fisher's F-distribution.
(class template)
produces real values on a Student's t-distribution.
(class template)
Sampling distributions
produces random integers on a discrete distribution.
(class template)
produces real values distributed on constant subintervals.
(class template)
produces real values distributed on defined subintervals.
(class template)
Utilities
evenly distributes real values of given precision across [0, 1)
(function template)
(C++11)
general-purpose bias-eliminating scrambled seed sequence generator
(class)

Synopsis

#include <initializer_list>
namespace std {
  // uniform random bit generator requirements
  template<class G>
    concept UniformRandomBitGenerator = /*see below*/;

  // class template linear_congruential_engine
  template<class UIntType, UIntType a, UIntType c, UIntType m>
  class linear_congruential_engine;
  // class template mersenne_twister_engine
  template<class UIntType, size_t w, size_t n, size_t m, size_t r,
           UIntType a, size_t u, UIntType d, size_t s,
           UIntType b, size_t t, UIntType c, size_t l, UIntType f>
  class mersenne_twister_engine;
  // class template subtract_with_carry_engine
  template<class UIntType, size_t w, size_t s, size_t r>
  class subtract_with_carry_engine;
  // class template discard_block_engine
  template<class Engine, size_t p, size_t r>
  class discard_block_engine;
  // class template independent_bits_engine
  template<class Engine, size_t w, class UIntType>
  class independent_bits_engine;
  // class template shuffle_order_engine
  template<class Engine, size_t k>
  class shuffle_order_engine;
  // engines and engine adaptors with predefined parameters
  using minstd_rand0 = /*see description*/ ;
  using minstd_rand = /*see description*/ ;
  using mt19937 = /*see description*/ ;
  using mt19937_64 = /*see description*/ ;
  using ranlux24_base = /*see description*/ ;
  using ranlux48_base = /*see description*/ ;
  using ranlux24 = /*see description*/ ;
  using ranlux48 = /*see description*/ ;
  using knuth_b = /*see description*/ ;
  using default_random_engine = /*see description*/ ;
  // class random_device
  class random_device;

  // class seed_seq
  class seed_seq;
  // function template generate_canonical
  template<class RealType, size_t bits, class URBG>
  RealType generate_canonical(URBG& g);
  // class template uniform_int_distribution
  template<class IntType = int>
  class uniform_int_distribution;
  // class template uniform_real_distribution
  template<class RealType = double>
  class uniform_real_distribution;
  // class bernoulli_distribution
  class bernoulli_distribution;
  // class template binomial_distribution
  template<class IntType = int>
  class binomial_distribution;
  // class template geometric_distribution
  template<class IntType = int>
  class geometric_distribution;
  // class template negative_binomial_distribution
  template<class IntType = int>
  class negative_binomial_distribution;
  // class template poisson_distribution
  template<class IntType = int>
  class poisson_distribution;
  // class template exponential_distribution
  template<class RealType = double>
  class exponential_distribution;
  // class template gamma_distribution
  template<class RealType = double>
  class gamma_distribution;
  // class template weibull_distribution
  template<class RealType = double>
  class weibull_distribution;
  // class template extreme_value_distribution
  template<class RealType = double>
  class extreme_value_distribution;
  // class template normal_distribution
  template<class RealType = double>
  class normal_distribution;
  // class template lognormal_distribution
  template<class RealType = double>
  class lognormal_distribution;
  // class template chi_squared_distribution
  template<class RealType = double>
  class chi_squared_distribution;
  // class template cauchy_distribution
  template<class RealType = double>
  class cauchy_distribution;
  // class template fisher_f_distribution
  template<class RealType = double>
  class fisher_f_distribution;
  // class template student_t_distribution
  template<class RealType = double>
  class student_t_distribution;
  // class template discrete_distribution
  template<class IntType = int>
  class discrete_distribution;
  // class template piecewise_constant_distribution
  template<class RealType = double>
  class piecewise_constant_distribution;
  // class template piecewise_linear_distribution
  template<class RealType = double>
  class piecewise_linear_distribution;
}