2002-11-10

Document N1398=02-0056

`$Id: wg21-proposal.html,v 1.1.1.1 2003/03/29 07:50:24 tietew Exp $`

Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin.

John von Neumann, 1951

- 2002-11-10: Publication in the Post-Santa Cruz mailing.
- The
`seed(first, last)`

interface now needs "unsigned long" values. - Introduce "variate_generator", adjust distribution interface accordingly.
- Add "add-on packages" discussion.
- All distribution parameters must be defaulted.
- Add "target audience" subsection to "motivation" section.
- Add discussion of manager class.
- Engines are independent of distributions, thus consider respective lifetimes.
- Add "sharing of engines" as a major requirement.
- Add some open issues.
- 2002-10-11: First publication on the C++ committee's library reflector.

Computers are deterministic machines by design: equal input data results in equal output, given the same internal state. Sometimes, applications require seemingly non-deterministic behaviour, usually provided by generating random numbers. Such applications include:Why is this important? What kinds of problems does it address, and what kinds of programmers, is it intended to support? Is it based on existing practice?

- numerics (simulation, Monte-Carlo integration)
- games (shuffling card decks, non-deterministic enemy behavior)
- testing (generation of test input data for good coverage)
- security (generation of cryptographic keys)

Programmers in all of the above areas have to find ways to generate random numbers. However, the difficulty to find generators that are both efficient and have good quality is often underestimated, and so ad-hoc implementations often fail to meet either or both of these goals.

The C++ standard library includes `std::rand`

, inherited
from the C standard library, as the only facility to generate
pseudo-random numbers. It is underspecified, because the generation
function is not defined, and indeed early C standard library
implementations provided surprisingly bad generators. Furthermore,
the interface relies on global state, making it difficult or
inefficient to provide for correct operation for simultaneous
invocations in multi-threaded applications.

There is a lot of existing practice in this area. A multitude of libraries, usually implemented in C or Fortran, is available from the scientific community. Some implement just one random number engine, others seek to provide a full framework. I know of no comprehensive C++ framework for generating random numbers that adheres to the design principles put forth in section III.

Random number generators are appropriate for this TR because they fall into one of the domains (numerics) identified in N1314 as a target for the TR.

- programmers that provide additional engines
- programmers that provide additional distributions
- programmers that provide generic add-on packages
- programmers that need random numbers

This proposal is a pure library extension. It does not require changes to any standard classes or functions. It does not require changes to any of the standard requirement tables. It does not require any changes in the core language, and it has been implemented in standard C++ as per ISO 14882:1998.What does it depend on, and what depends on it? Is it a pure extension, or does it require changes to standard components? Does it require core language changes?

The ISO C99 extension that specify integral types having a given
minimum or exact bitwidth (e.g. `int32_t`

) aids in
implementing this proposal, however these types (or the equivalent
thereof under another name) can be defined with template
metaprogramming in standard C++, so these are not strictly necessary.

In case the ISO C99 extensions become part of the TR, section IV should be reviewed whether some requirements could be reformulated with the ISO C99 extensions.

In case a standard reference-counted smart pointer becomes part of
the TR, section IV should be reviewed and instances of the smart
pointer be added to the acceptable template parameters for a
`variate_generator`

.

The design decisions are compared to those in the following libraries:Why did you choose the specific design that you did? What alternatives did you consider, and what are the tradeoffs? What are the consequences of your choice, for users and implementors? What decisions are left up to implementors? If there are any similar libraries in use, how do their design decisions compare to yours?

- CLHEP (original at http://wwwinfo.cern.ch/asd/lhc++/clhep/index.html, modifications from FermiLab at (anonymous CVS) :pserver:anonymous@zoomcvs.fnal.gov:/usr/people/cvsuser/repository)
- crng 1.1: Random-number generators (RNGs) implemented as Python extension types coded in C (at http://www.sbc.su.se/~per/crng/)
- Swarm 2.1.1 (multi-agent simulation of complex systems), random number package, using a Smalltalk-like programming language (at http://www.santafe.edu/projects/swarm/swarmdocs/set/swarm.random.sgml.reference.html)
- GNU Scientific Library: general scientific computing library implemented in C, comprehensive coverage of random number engines and distributions (at http://sources.redhat.com/gsl)

- Donald E. Knuth, "The Art of Computer Programming Vol. 2"
- William H. Press et al., "Numerical Recipes in C"

- allows users to choose in speed / size / quality trade-offs
- has a tight enough specification to get reliable cross-platform results
- allows storage of state on non-volatile media (e.g., in a disk file) to resume computation later
- does not impede sequence "jump-ahead" for parallel computation
- provides a variety of base engines, not just one
- allows the user to write its own base engines and use it with the library-provided distributions
- provides the most popular distributions
- allows the user to write its own distributions and use it with the library-provided engines
- allows sharing of engines by several distributions
- does not prevent implementations with utmost efficiency
- provides both pseudo-random number engines (for simulations etc.) and "true" non-deterministic random numbers (for cryptography)

In this proposal, a *pseudo-random number engine* is defined as
an initial internal state x(0), a function f that
moves from one internal state to the next x(i+1) := f(x(i)), and an
output function o that produces the output o(x(i)) of the generator.
This is an entirely deterministic process, it is determined by the
initial state x(0) and functions f and o only.
The initial state x(0) is determined from a seed. Apparent randomness
is achieved only because the user has limited perception.

A *non-deterministic random-number engine* provides a
sequence of random numbers x(i) that cannot be foreseen. Examples are
certain quantum-level physics experiments, measuring the time
difference between radioactive decay of individual atoms or noise of a
Zehner diode. Relatively unforeseeable random sources are also (the
low bits of) timing between key touches, mouse movements, Ethernet
packet arrivals, etc. An estimate for the amount of
unforeseeability is the entropy, a concept from information theory.
Completely foreseeable sequences (e.g., from pseudo-random number
engines) have entropy 0, if all bits are unforeseeable, the entropy is
equal to the number of bits in each number.

Pseudo-random number engines are usually much faster than non-deterministic random-number engines, because the latter require I/O to query some randomness device outside of the computer. However, there is a common interface feature subset of both pseudo-random and non-deterministic random-number engines. For example, a non-deterministic random-number engine could be employed to produce random numbers with normal distribution; I believe this to be an unlikely scenario in practice.

Other libraries, including those mentioned above, only provide either pseudo-random numbers, suitable for simulations and games, or non-deterministic random numbers, suitable for cryptographic applications.

This proposal honours this conceptual separation, and provides a class template to merge an arbitrary engine with an arbitrary distribution on top. To this end, this proposal sets up requirements for engines so that each of them can be used to provide uniformly distributed random numbers for any of the distributions. The resulting freedom of combination allows for the utmost re-use.

Engines have usually been analyzed with all mathematical and empirical tools currently available. Nonetheless, those tools show the absence of a particular weakness only, and are not exhaustive. Albeit unlikely, a new kind of test (for example, a use of random numbers in a new kind of simulation or game) could show serious weaknesses in some engines that were not known before.

This proposal attempts to specify the engines precisely; two different implementations, with the same seed, should return the same output sequence. This forces implementations to use the well-researched engines specified hereinafter, and users can have confidence in their quality and the limits thereof.

On the other hand, the specifications for the distributions only define the statistical result, not the precise algorithm to use. This is different from engines, because for distribution algorithms, rigorous proofs of their correctness are available, usually under the precondition that the input random numbers are (truely) uniformly distributed. For example, there are at least a handful of algorithms known to produce normally distributed random numbers from uniformly distributed ones. Which one of these is most efficient depends on at least the relative execution speeds for various transcendental functions, cache and branch prediction behaviour of the CPU, and desired memory use. This proposal therefore leaves the choice of the algorithm to the implementation. It follows that output sequences for the distributions will not be identical across implementations. It is expected that implementations will carefully choose the algorithms for distributions up front, since it is certainly surprising to customers if some distribution produces different numbers from one implementation version to the next.

Other libraries usually provide the same differentiation between engines and distributions. Libraries rarely have a wrapper around both engine and distribution, but it turns out that this can hide some complexities from the authors of distributions, since some facitilies need to be provided only once. A previous version of this proposal had distributions directly exposed to the user, and the distribution type dependent on the engine type. In various discussions, this was considered as too much coupling.

Since other libraries do not aim to provide a portable specification framework, engines are sometimes only described qualitatively without giving the exact parameterization. Also, distributions are given as specific functions or classes, so the quality-of-implementation question which distribution algorithm to employ does not need to be addressed.

For virtual functions in a class hierarchy, the core language requires a (nearly) exact type match for a function in a derived classes overriding a function in a base class. This seems to be unnecessarily restrictive, because engines can sometimes benefit from using different integral base types. Also, with current compiler technology, virtual functions prevent inlining when a pointer to the base class is used to call a virtual function that is overridden in some derived class. In particular with applications such as simulations that sometimes use millions of pseudo-random numbers per second, losing significant amounts of performance due to missed inlining opportunities appears to not be acceptable.

The CLHEP library bases all its engines on the abstract base class
`HepRandomEngine`

. Specific engines derive from this class
and override its pure virtual functions. Similarly, all
distributions are based on the base class `HepRandom`

.
Specific distributions derive from this class, override operator(),
and provide a number of specific non-virtual functions.

The GNU Scientific Library, while coded in C, adheres to the principles of object-structuring; all engines can be used with any of the distributions. The technical implementation is by mechanisms similar to virtual functions.

Since random-number engines are mathematically designed with computer implementation in mind, parameters are usually integers representable in a machine word, which usually coincides nicely with a C++ built-in type. The parameters could either be given as (compile-time) template arguments or as (run-time) constructor arguments.

Providing parameters as template arguments allows for providing predefined parameterizations as simple "typedef"s. Furthermore, the parameters appear as integral constants, so the compiler can value-check the given constants against the engine's base type. Also, the library implementor can choose different implementations depending on the values of the parameters, without incurring any runtime overhead. For example, there is an efficient method to compute (a*x) mod m, provided that a certain magnitude of m relative to the underlying type is not exceeded. Additionally, the compiler's optimizer can benefit from the constants and potentially produce better code, for example by unrolling loops with fixed loop count.

As an alternative, providing parameters as constructor arguments allows for more flexibility for the library user, for example when experimenting with several parameterizations. Predefined parameterizations can be provided by defining wrapper types which default the constructor parameters.

Other libraries have hard-coded the parameters of their engines and do not allow the user any configuration of them at all. If the user wishes to change the parameters, he has to re-implement the engine's algorithm. In my opinion, this approach unnecessarily restricts re-use.

Regarding initialization, this proposal chooses to provide
"deterministic seeding" with the default constructor and the
`seed`

function without parameters: Two engines constructed
using the default constructor will output the same sequence. In
contrast, the CLHEP library's default constructed engines will take a
fresh seed from a seed table for each instance. While this approach
may be convenient for a certain group of users, it relies on global
state and can easily be emulated by appropriately wrapping engines
with deterministic seeding.

In addition to the default constructor, all engines provide a
constructor and `seed`

function taking an iterator range
[it1,it2) pointing to unsigned integral values. An engine initializes its state by successively consuming
values from the iterator range, then returning the advanced iterator it1.
This approach has the advantage that the user can completely exploit
the large state of some engines for initialization. Also, it allows
to initialize compound engines in a uniform manner. For example, a
compound engine consisting of two simpler engines would initialize the
first engine with its [it1,it2). The first engine returns a smaller
iterator range that it has not consumed yet. This can be used to
initialize the second engine.

The iterator range [it1,it2) is specified to point to unsigned
long values. There is no way to determine from a generic user
program how the initialization values will be treated and what range
of bits must be provided, except by enumerating all engines, e.g. in
template specializations. The problem is that a given generator might
have differing requirements on the values of the seed range even
within one `seed`

call.

For example, imagine a

xor_combine<lagged_fibonacci<...>, mersenne_twister<...> >generator. For this,

`seed(first, last)`

will consume
values as follows: First, seed the state of the
`lagged_fibonacci`

generator by consuming one item from
[first, last) for each word of state. The values are reduced to
(e.g.) 24 bits to fit the `lagged_fibonacci`

state
requirements. Then, seed the state of the
`mersenne_twister`

by consuming some number of items from
the remaining [first, last). The values are reduced to 32 bits to fit
the `mersenne_twister`

state requirements.
How does a concise programming interface for those increasingly complex and varying requirements on [first, last) look like? I don't know, and I don't want to complicate the specification by inventing something complicated here.

Thus, the specification says for each generator how it uses the seed values, and how many are consumed. Additional features are left to the user.

In a way, this is similar to STL containers: It is intended that the user
can exchange iterators to various containers in generic algorithms,
but the container itself is not meant to be exchanged, i.e. having a
Container template parameter is often not adequate. That is analogous
to the random number case: The user can pass an engine around and use its
`operator()`

and `min`

and `max`

functions generically. However, the user can't generically query the
engine attributes and parameters, simply because most are entirely
different in semantics for each engine.

The `seed(first, last)`

interface can serve two purposes:

- In a generic context, the user can pass several integer values >= 1
for seeding. It is unlikely that the user explores the full state space
with the seeds she provides, but she can be reasonably sure that her
seeds aren't entirely incorrect. (There is no formal guarantee for that, except that
the ability to provide bad seeds usually means the parameterization of
the engine is bad, e.g. a non-prime modulus for a linear congruential
engine.) For example, if the user wants a
`seed(uint32_t)`

on top of`seed(first, last)`

, one option is to use a`linear_congruential`

generator that produces the values required for`seed(first, last)`

. When the user defines the iterator type for`first`

and`last`

so that it encapsulates the`linear_congruential`

engine in`operator++`

, the user doesn't even need to know beforehand how many values`seed(first, last)`

will need. - If the user is in a non-generic context, he knows the specific
template type of the engine (probably not the template value-based
parameterization, though). The precise specification for
`seed(first, last)`

allows to know what values need to be passed in so that a specific initial state is attained, for example to compare one implementation of the engine with another one that uses different seeding. - If the user requires both, he needs to inject knowledge into (1) so that he is in the position of (2). One way to inject the knowledge is to use (partial) template specialization to add the knowledge. The specific parameterization of some engine can then be obtained by querying the data members of the engines.

I haven't seen the iterator-based approach to engine initialization in other libraries; most initialization approaches rely on a either a single value or on per-engine specific approaches to initialization.

An alternative approach is to pass a zero-argument function object ("generator") for seeding. It is trivial to implement a generator from a given iterator range, but it is more complicated to implement an iterator range from a generator. Also, the exception object that is specified to be thrown when the iterator range is exhausted could be configured in a user-provided iterator to generator mapping. With this approach, some engines would have three one-argument constructors: One taking a single integer for seeding, one taking a (reference?) to a (templated) generator, and the copy constructor. It appears that the opportunities for ambiguities or choosing the wrong overload are too confusing to the unsuspecting user.

`float`

,
`double`

, `long double`

) that the user desires.
The probability density functions of distributions usually have parameters. These are mapped to constructor parameters, to be set at runtime by the library user according to her requirements. The parameters for a distribution object cannot change after its construction. When constructing the distribution, this allows to pre-compute some data according to the parameters given without risk of inadvertently invalidating them later.

Distributions may implement `operator()(T x)`

, for
arbitrary type `T`

, to meet special needs, for example a
"one-shot" mode where each invocation uses different distribution
parameters.

`double`

using `std::numeric_limits<>`

. However,
engines and distributions cannot be simple types, so it does not
appear to be necessary to separate the properties into separate traits
classes. Instead, compile-time properties are given as members types
and static member constants.
Engines may be categorized according to the following dimensions.

- integers or floating-point numbers produced (Some engines produce
uniformly distributed integers in the range [min,max], however, most
distribution functions expect uniformly distributed floating-point
numbers in the range [0,1) as the input sequence. The obvious
conversion requires a relatively costly integer to floating-point
conversion plus a floating-point multiplication by
(max-min+1)
^{-1}for each random number used. To save the multiplication, some engines can directly produce floating-point numbers in the range [0,1) by maintaining the state x(i) in an appropriately normalized form, given a sufficiently good implementation of basic floating-point operations (e.g. IEEE 754). - quality of random numbers produced (What is the cycle length? Does the engine pass all relevant statistical tests? Up to what dimension are numbers equidistributed?)
- speed of generation (How many and what kind of operations have to be performed to produce one random number, on average?)
- size of state (How may machine words of storage are required to hold the state x(i) of the random engine?)
- option for independent subsequences (Is it possible to move from x(i) to x(i+k) with at most O(log(k)) steps? This allows to efficiently use subsequences x(0)...x(k-1), x(k)...x(2k-1), ..., x(jk)...x((j+1)k-1), ..., for example for parallel computation, where each of the m processors gets assigned the (independent) subsequence starting at x(jk) (0 <= k < m).)

engine | int / float | quality | speed | size of state | subsequences | comments |
---|---|---|---|---|---|---|

linear_congruential | int | medium | medium | 1 word | yes | cycle length is limited to the maximum value representable in one machine word, passes most statisticial tests with chosen parameters. |

mersenne_twister | int | good | fast | 624 words | no | long cycles, passes all statistical tests, good equidistribution in high dimensions |

subtract_with_carry | both | medium | fast | 25 words | no | very long cycles possible, fails some statistical tests. Can be improved with the discard_block compound engine. |

discard_block | both | good | slow | base engine + 1 word | no | compound engine that removes correlation provably by throwing away significant chunks of the base engine's sequence, the resulting speed is reduced to 10% to 3% of the base engine's. |

xor_combine | int | good | fast | base engines | yes, if one of the base engines | compound engine that XOR-combines the sequences of two base engines |

Some engines were considered for inclusion, but left out for the following reasons:

engine | int / float | quality | speed | size of state | subsequences | comments |
---|---|---|---|---|---|---|

shuffle_output | int | good | fast | base engine + 100 words | no | compound engine that reorders the base engine's output, little overhead for generation (one multiplication) |

lagged_fibonacci | both | medium | fast | up to 80,000 words | no | very long cycles possible, fails birthday spacings test. Same
principle of generation as `subtract_with_carry` , i.e. x(i)
= x(i-s) (*) x(i-r), where (*) is either of +, -, xor with or without
carry. |

inversive_congruential (Hellekalek 1995) | int | good | slow | 1 word | no | x(i+1) = a x(i)^{-1} + c. Good equidistribution in
several dimensions. Provides no apparent advantage compared to
ranlux; the latter can produce floating-point numbers directly. |

additive_combine (L'Ecuyer 1988) | int | good | medium | 2 words | yes | Combines two linear congruential generators. Same principle
of combination as `xor_combine` , i.e. z(i) = x(i) (*) y(i),
where (*) is one of +, -, xor. |

R250 (Kirkpatrick and Stoll) | int | bad | fast | ~ 20 words | no | General Feedback Shift Register with two taps: Easily exploitable correlation. |

linear_feedback_shift | int | medium | fast | 1 word | no | cycle length is limited to the maximum value representable in one machine word, fails some statistical tests, can be improved with the xor_combine compound engine. |

The GNU Scientific Library and Swarm have additional engine that are not mentioned in the table below.

Engine | this proposal | CLHEP | crng | GNU Scientific Library | Swarm | Numerical Recipes | Knuth |
---|---|---|---|---|---|---|---|

LCG(2^{31}-1, 16807) |
minstd_rand0 | - | ParkMiller | ran0, minstd | - | ran0 | p106, table 1, line 19 |

LCG(2^{32}, a=1664525, c=1013904223) |
linear_congruential< ..., 1664525, 1013904223, (1 << 32) > | - | - | - | LCG1gen | - | p106, table 1, line 16 |

LCG1 + LCG2 + LCG3 | - | - | WichmannHill | - | - | - | - |

(LCG1 - LCG2 + LCG3 - LCG4) mod m0 | - | - | - | - | C4LCGXgen | - | - |

LCG(2^{31}-1, 16807) with Bays/Durham shuffle |
shuffle_output<minstd_rand0, 32> (shuffle_output not in this proposal) | - | - | ran1 | PMMLCG1gen | ran1 | Algorithm "B" |

(LCG(2^{31}-85, 40014) + LCG(2^{31}-249, 40692))
mod 2^{31}-85 |
ecuyer1988 (additive_combine not in this proposal) | Ranecu | LEcuyer | - | C2LCGXgen | - | - |

(LCG(2^{31}-85, 40014) with Bays/Durham shuffle +
LCG(2^{31}-249, 40692)) mod 2^{31}-85 |
additive_combine<
shuffle_output< linear_congruential<int, 40014, 0, 2147483563>, 32>, linear_congruential<int, 40692, 0, 2147483399> > (additive_combine and shuffle_output not in this proposal) |
- | - | ran2 | - | ran2 | - |

X(i) = (X(i-55) - X(i-33)) mod 10^{9} |
- | - | - | ran3 | ~SCGgen | ran3 | - |

X(i) = (X(i-100) - X(i-37)) mod 2^{30} |
- | - | - | - | - | - | ran_array |

X(i) = (X(i-55) + X(i-24)) mod 2^{32} |
lagged_fibonacci< ..., 32, 55, 24, ...> (lagged_fibonacci not in this proposal) | - | - | - | ACGgen | - | - |

DEShash(i,j) | - | - | - | - | - | ran4 | - |

MT | mt19937 | MTwistEngine | MT19937 | mt19937 | MT19937gen | - | - |

X(i) = (X(i-37) - X(i-24) - carry) mod 2^{32} |
subtract_with_carry< ..., (1<<32), 37, 24, ...> | - | - | - | SWB1gen | - | - |

X(i) = (X(i-43) - X(i-22) - carry) mod 2^{32}-5 |
subtract_with_carry< ..., (1<<32)-5, 43, 22, ...> | - | - | - | PSWBgen | - | - |

RCARRY with block discard by Lüscher | discard_block< subtract_with_carry<...>, ...> | RanluxEngine, Ranlux64Engine | Ranlux | ranlx* | - | - | - |

Hurd | - | Hurd160, Hurd288 | - | - | - | - | - |

physical model by Ranshi | - | Ranshi | - | - | - | - | - |

return predefined data | - | NonRandom | - | - | - | - | - |

RANMAR: z(i) = (z(i-97) - z(i-33)) mod 2^{24}; y(i+1) =
(y(i)-c) mod 2^{24}-3; X(i) = (z(i) - y(i)) mod
2^{24} |
additive_combine< lagged_fibonacci< (1<<24), 97, 33, ... >, linear_congruential< (1<<24)-3, 1, c, ...> (additive_combine and lagged_fibonacci not in this proposal) | JamesRandom | - | ranmar | - | - | - |

Taus88 | taus88 = xor_combine ... | - | Taus88 | taus, taus2 | - | - | - |

Taus60 | xor_combine< linear_feedback_shift< 31, 13, 12 >, 0, linear_feedback_shift< 29, 2, 4 >, 2, 0> (linear_feedback_shift not in this proposal) | - | - | - | C2TAUSgen | - | - |

GFSR, 4-tap | - | - | - | gfsr4 | - | - | - |

MRG32k3a | - | - | MRG32k3a | - | - | - | - |

- Integer uniform
- Floating-point uniform
- Exponential
- Normal
- Gamma
- Poisson
- Binomial
- Geometric
- Bernoulli

Distribution | this proposal | CLHEP | crng | GNU Scientific Library | Swarm | Numerical Recipes | Knuth |
---|---|---|---|---|---|---|---|

uniform (int) | uniform_int | - | - | - | UniformIntegerDist | - | - |

uniform (float) | uniform_real | RandFlat | UniformDeviate | flat | UniformDoubleDist | - | uniform |

exponential | exponential_distribution | RandExponential | ExponentialDeviate | exponential | ExponentialDist | exponential | exponential |

normal | normal_distribution | RandGauss* | NormalDeviate | gaussian | NormalDist | normal (gaussian) | normal |

lognormal | - | - | - | lognormal | LogNormalDist | - | - |

gamma | gamma_distribution | RandGamma | GammaDeviate | gamma | GammaDist | gamma | gamma |

beta | - | - | BetaDeviate | beta | - | - | beta |

poisson | poisson_distribution | Poisson | PoissonDeviate | poisson | PoissonDist | poisson | poisson |

binomial | binomial_distribution | RandBinomial | BinomialDeviate | binomial | - | binomial | binomial |

geometric | geometric_distribution | - | GeometricDeviate | geometric | - | - | geometric |

bernoulli | bernoulli_distribution | - | BernoulliDeviate | bernoulli | BernoulliDist | - | - |

random bit | - | RandBit | - | - | RandomBitDist | - | - |

breit-wigner | - | RandBreitWigner | - | - | - | - | - |

chi-square | - | RandChiSquare | - | chisq | - | - | chi-square |

landau | - | Landau | - | landau | - | - | - |

F | - | - | - | F | - | - | F (variance-ratio) |

t | - | - | - | t | - | - | t |

NumberGenerator ---- UniformRandomNumberGenerator ---- PseudoRandomNumberGenerator \--- RandomDistribution

This is considered an important feature for library implementors and serious users to check whether the provided library on the given platform returns the correct numbers. It could be argued that a library implementor should provide a correct implementation of some standard feature in any case.

No other library I have encountered provides explicit validation values in either their specification or their implementation, although some of them claim to be widely portable.

Another design option for validation that was part of early drafts of
this proposal is moving the reference number (10000th value in the
sequence) from specification space to implementation space, thus
providing a `validation(x)`

static member function for each
engine that compares the hard-coded 10000th value of the sequence with
some user-provided value `x`

presumeably obtained by
actually invoking the random-number engine object 10000 times. Due to
the template-based design, this amounted to a "val" template value
parameter for each engine, and the `validation(x)`

function
reduced to the trivial comparison "val == x". Handling validation for
floating-point engines required more machinery, because template value
parameters cannot be of floating-point type. Also, from a conceptual
perspective, it seemed odd to demand a validation decision from the
very entitiy which one wanted to validate.

`std::ostream`

in textual form and recover it from an
appropriate `std::istream`

. Each engine specifies how its
internal state is represented. The specific algorithm of a
distribution is left implementation-defined, thus no specifics about
the representation of its internal state are given. A store operation
must not affect the number sequence produced. It is expected
that such external storage happens rarely as opposed to producing
random numbers, thus no particular attention to performance is paid.
Engines and distributions use the usual idioms of `operator<<`

and
`operator>>`

. If the user needs additional
processing before or after storage on non-volatile media, there is
always the option to use a temporary `std::stringstream`

.

Distributions sometimes store values from their associated source of
random numbers across calls to their `operator()`

. For example, a
common method for generating normally distributed random numbers is to
retrieve two uniformly distributed random numbers and compute two
normally distributed random numbers out of them.
In order to reset the distribution's random number cache to a defined
state, each distribution has a `reset`

member function. It
should be called on a distribution whenever its associated engine is
exchanged or restored.

`shuffle_output`

and
`discard_block`

contain a base engine by value, because
compounding is not intended to be used by reference to an existing
(re-used) engine object.
The wrapper `variate_generator`

can store engines either by
value or by reference, explicitly chosen by the template parameters.
This allows to use references to a single engine in several
`variate_generator`

, but makes it explicit to the user that
he is responsible for the management of the lifetime of the engine.

The precise state-holding base data types for the various engines are
left implementation-defined, because engines are usually optimized for
binary integers with 32 bits of word size. The specification in this
proposal cannot foresee whether a 32 bit quantity on the machine is
available in C++ as short, int, long, or not at all. It is up to the
implementation to decide which data type fits best. The
implementation is required to document the choice of data type, so
that users can (non-portably) rely on the precise type, for example
for further computation. Should the ISO C99 extensions become part of
ISO C++, the implementation-defined types could be replaced by
e.g. `int_least32_t`

.

The method how to produce non-deterministic random numbers is
considered implementation-defined, because it inherently depends on
the implementation and possibly even on the runtime environment:
Imagine a platform that has operating system support for randomness
collection, e.g. from user keystrokes and Ethernet inter-packet
arrival timing (Linux `/dev/random`

does this). If, in some
installation, access to the operating system functions providing these
services has been restricted, the C++ non-deterministic random number
engine has been deprived of its randomness. An implementation is
required to document how it obtains the non-deterministic random
numbers, because only then can users' confidence in them grow.
Confidence is of particular concern in the area of cryptography.

The algorithms how to produce the various distributions are specified as implementation-defined, because there is a vast variety of algorithms known for each distribution. Each has a different trade-off in terms of speed, adaptation to recent computer architectures, and memory use. The implementation is required to document its choice so that the user can judge whether it is acceptable quality-wise.

`min()`

and `max()`

return
the lower and upper bounds of a UniformRandomNumberGenerator. This
could be a random-number engine or one of the `uniform_int`

and `uniform_real`

distributions.
Those bounds are not specified to be tight, because for some engines,
the bounds depend on the seeds. The seed can be changed during the
lifetime of the engine object, while the values returned by
`min()`

and `max()`

are invariant. Therefore,
`min()`

and `max()`

must return conservative
bounds that are independent of the seed.

`variate_generator`

, after extensive discussion with some
members of the computing division of Fermi National Accelerator
Laboratory. User-written and library-provided engines and
distributions plug in to the manager class. The approach is remotely
similar to the locale design in the standard library, where
(user-written) facets plug in to the (library-provided) locale class.
Earlier versions of this propsoal made (potentially user-written) distributions directly visible to (some other) user that wants to get random numbers distributed accordingly ("client"), there was no additional management layer between the distribution and the client.

The following additional features could be provided by the management layer:

- The management layer contains an adaptor (to convert the engine's output into the distribution's input) in addition to the engine and the distribution.
- Adaptors and distributions do not store state, but instead, in each invocation, consume an arbitrary number of input values and produce a fixed number of output values. The management layer is responsible for connecting the engine - adaptor - distribution chain, invoking each part when more numbers are needed from the next part of the chain.
- On request, the management layer is responsible for saving and restoring the buffers that exist between engine, adaptor, and distribution.
- On request, the management layer shares engines with another instance of the management layer.

- Retrieve a uniform integer with value either 1 or 2.
- If 1, return a number with normal distribution.
- If 2, return a number with gamma distribution.

The ability to share engines is important. This proposal makes
lifetime management issues explicit by requiring pointer or reference
types in the template instantiation of `variate_generator`

if reference semantics are
desired. Without a management class, providing this features is
much more cumbersome and imposes additional burden on the programmers
that produce distributions. Alternatively, reference semantics could
always be used, but this is an
error-prone approach due to the lack of a standard reference-counted
smart pointer. I believe it is impossible to add a reference-counted
sharing mechanism in a manager-class-free design without giving its precise
type. And that would certainly conflict in semantic scope with a
smart pointer that will get into the standard eventually.

The management layer allows for a few common features to be factored out, in particular access to the engine and some member typedefs.

Comparison of other differing features between manager and non-manager designs:

- When passing a
`variate_generator`

as a an argument to a function, the design in this proposal allows selecting only those function signatures during overload resolution that are intended to be called with a`variate_generator`

. In contrast, misbehaviour is possible without a manager class, similar to iterators in function signatures:`template<class BiIter> void f(BiIter it)`

matches`f(5)`

, without regard to the bidirectional iterator requirements. An error then happens when instantiating f. The situation worsens when several competing function templates are available and the wrong one is chosen accidentally. - With the engine passed into the distribution's constructor, the
full type hierarchy of engine (and possibly adaptor) are available to
the distribution, allowing to cache information derived from the
engine (e.g. its value range) . Also, (partial) specialization
of distributions could be written that optimize the interaction with a
specific engine and/or adaptor. In this proposal's design,
this information is available in the
`variate_generator`

template only. However, optimizations by specialization for the engine/adaptor combination are perceived to possibly have high benefit, while those for the (engine+adaptor) / distribution combination are presumed to be much less beneficial. - Having distribution classes directly exposed to the client easily
allows that the user writes a distribution with an additional
arbitrary member function declaration, intended to produce random
numbers while taking additional parameters into account. In this
proposal's design, this is possible by using the
generic
`operator()(T x)`

forwarding function.

- unique seeding: Make it easy for the user to provide a unique seed
for each instance of a pseudo-random number engine. Design idea:
class unique_seed; template<class Engine> Engine seed(unique_seed&);

The "seed" function retrieves some unique seed from the unique_seed class and then uses the`seed(first, last)`

interface of an engine to implant that unique seed. Specific seeding requirements for some engine can be met by (partial) template specialization. - runtime-replaceable distributions and associated save/restore functionality: Provide a class hierarchy that invokes distributions by means of a virtual function, thereby allowing for runtime replacement of the actual distribution. Provide a factory function to reconstruct the distribution instance after saving it to some non-volatile media.

`variate_generator`

as the
engine. The `variate_generator`

will only apply automatic adaptations
if the output type of the engine is integers and the input type for
the distribution is floating-point or vice versa.
- Some engines require non-negative template arguments, usually bit counts. Should these be given as "int" or "unsigned int"? Using "unsigned int" sometimes adds significant clutter to the presentation. Or "size_t", but this is probably too large a type?

This subclause defines a facility for generating random numbers.

In the following table, `X`

denotes a number generator
class returning objects of type `T`

, and `u`

is
a (possibly `const`

) value of `X`

.

Number generator requirements (in addition to function object) | |||
---|---|---|---|

expression | return type | pre/post-condition | complexity |

`X::result_type` |
T | `std::numeric_limits<T>::is_specialized` is
`true` |
compile-time |

In the following table, `X`

denotes a uniform random number
generator class returning objects of type `T`

,
`u`

is a value of `X`

and `v`

is a
(possibly `const`

) value of `X`

.

Uniform random number generator requirements (in addition to number generator) | |||
---|---|---|---|

expression | return type | pre/post-condition | complexity |

`u()` |
T | - | amortized constant |

`v.min()` |
`T` |
Returns some l where l is less than or equal to all values
potentially returned by `operator()` .
The return value of this function shall not change during the lifetime
of `v` . |
constant |

`v.max()` |
`T` |
If `std::numeric_limits<T>::is_integer` , returns
l where l is less than or equal to all values potentially returned by
`operator()` , otherwise, returns l where l is strictly less than all
values potentially returned by `operator()` . In any case,
the return value of this function shall not change during the lifetime
of `v` . |
constant |

In the following table, `X`

denotes a pseudo-random number
engine class returning objects of type `T`

, `t`

is a value of `T`

, `u`

is a value of
`X`

, `v`

is an lvalue of `X`

,
`it1`

is an lvalue and `it2`

is a (possibly
`const`

) value of an input iterator type `It`

having an unsigned integral value type, `x`

, `y`

are (possibly `const`

) values of
`X`

, `os`

is convertible to an lvalue of type
`std::ostream`

, and `is`

is convertible to an
lvalue of type `std::istream`

.

A pseudo-random number engine x has a state x(i) at any given time.
The specification of each pseudo-random number engines defines the size of its
state in multiples of the size of its `result_type`

, given
as an integral constant expression.

Pseudo-random number engine requirements
(in addition to uniform random number generator,
`CopyConstructible` , and `Assignable` ) |
|||
---|---|---|---|

expression | return type | pre/post-condition | complexity |

`X()` |
- | creates an engine with the same initial state as all other
default-constructed engines of type `X` in the
program. |
O(size of state) |

`X(it1, it2)` |
- | creates an engine with the initial state given by the range
`[it1,it2)` . `it1` is advanced by the size of
state. If the size of the range [it1,it2) is insufficient, leaves
`it1 == it2` and throws `invalid_argument` . |
O(size of state) |

`u.seed()` |
void | post: `u == X()` |
O(size of state) |

`u.seed(it1, it2)` |
void | post: If there are sufficiently many values in [it1, it2) to
initialize the state of `u` , then ```
u ==
X(it1,it2)
``` . Otherwise, `it1 == it2` , throws
`invalid_argument` , and further use of `u`
(except destruction) is undefined until a `seed` member
function has been executed without throwing an exception. |
O(size of state) |

`u()` |
`T`
| given the state u(i) of the engine, computes u(i+1), sets the state to u(i+1), and returns some output dependent on u(i+1) | amortized constant |

`x == y` |
`bool` |
`==` is an equivalence relation. The current state x(i)
of x is equal to the current state y(j) of y. |
O(size of state) |

`x != y` |
`bool` |
`!(x == y)` |
O(size of state) |

`os << x` |
`std::ostream&` |
writes the textual representation of the state x(i) of
`x` to `os` , with
`os.` set to
`ios_base::dec|ios_base::fixed|ios_base::left` and the fill
character set to the space character. In the output, adjacent numbers
are separated by one or more space characters.
post: The `os.` and fill character are
unchanged. |
O(size of state) |

`is >> v` |
`std::istream&` |
sets the state v(i) of `v` as determined by reading its
textual representation from `is` .
post: The `is.` are unchanged. |
O(size of state) |

In the following table, `X`

denotes a random distribution
class returning objects of type `T`

, `u`

is a
value of `X`

, `x`

is a (possibly const)
value of `X`

, and `e`

is an lvalue of an
arbitrary type that meets the requirements of a uniform random number
generator, returning values of type `U`

.

Random distribution requirements
(in addition to number generator,
`CopyConstructible` , and `Assignable` ) |
|||
---|---|---|---|

expression | return type | pre/post-condition | complexity |

`X::input_type` |
U | - | compile-time |

`u.reset()` |
`void` |
subsequent uses of `u` do not depend on values
produced by `e` prior to invoking `reset` . |
constant |

`u(e)` |
`T` |
the sequence of numbers returned by successive invocations with
the same object `e` is randomly distributed with some
probability density function p(x) |
amortized constant number of invocations of `e` |

`os << x` |
`std::ostream&` |
writes a textual representation for the parameters and additional
internal data of the distribution `x` to `os` .
post: The `os.` and fill character are
unchanged. |
O(size of state) |

`is >> u` |
`std::istream&` |
restores the parameters and additional internal data of the
distribution `u` .
pre: `is` provides a textual representation that was
previously written by `operator<<`
post: The `is.` are unchanged. |
O(size of state) |

Additional requirements: The sequence of numbers produced by
repeated invocations of `x(e)`

does not change whether or
not `os << x`

is invoked between any of the
invocations `x(e)`

. If a textual representation
is written using `os << x`

and that representation
is restored into the same or a different object `y`

of the
same type using `is >> y`

, repeated invocations of
`y(e)`

produce the same sequence of random numbers as would
repeated invocations of `x(e)`

.

In the following subclauses, a template parameter named
`UniformRandomNumberGenerator`

shall denote a class that
satisfies all the requirements of a uniform random number generator.
Moreover, a template parameter named `Distribution`

shall
denote a type that satisfies all the requirements of a random
distribution.
Furthermore, a template parameter named `RealType`

shall
denote a type that holds an approximation to a real number. This type
shall meet the requirements for a numeric type (26.1
[lib.numeric.requirements]), the binary operators +, -, *, / shall be
applicable to it, a conversion from `double`

shall exist,
and function signatures corresponding to those
for type `double`

in subclause 26.5 [lib.c.math] shall be
available by argument-dependent lookup (3.4.2 [basic.lookup.koenig]).
*[Note: The built-in floating-point types float
and double meet these requirements.]*

`<random>`

synopsisnamespace std { template<class UniformRandomNumberGenerator, class Distribution> class variate_generator; template<class IntType, IntType a, IntType c, IntType m> class linear_congruential; template<class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> class mersenne_twister; template<class IntType, IntType m, int s, int r> class subtract_with_carry; template<class RealType, int w, int s, int r> class subtract_with_carry_01; template<class UniformRandomNumberGenerator, int p, int r> class discard_block; template<class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> class xor_combine; class random_device; template<class IntType = int> class uniform_int; template<class RealType = double> class bernoulli_distribution; template<class IntType = int, class RealType = double> class geometric_distribution; template<class IntType = int, class RealType = double> class poisson_distribution; template<class IntType = int, class RealType = double> class binomial_distribution; template<class RealType = double> class uniform_real; template<class RealType = double> class exponential_distribution; template<class RealType = double> class normal_distribution; template<class RealType = double> class gamma_distribution; } // namespace std

`variate_generator`

`variate_generator`

produces random numbers, drawing
randomness from an underlying uniform random number generator and
shaping the distribution of the numbers corresponding to a
distribution function.
template<class Engine, class Distribution> class variate_generator { public: typedef Engine engine_type; typedef /*The template argument for the parameterimplementation defined*/ engine_value_type; typedef Distribution distribution_type; typedef typename Distribution::result_type result_type; variate_generator(engine_type eng, distribution_type d); result_type operator()(); template<class T> result_type operator()(T value); engine_value_type& engine(); const engine_value_type& engine() const; distribution_type& distribution(); const distribution_type& distribution() const; result_type min() const; result_type max() const; };

`Engine`

shall be
of the form *U*

, *U*&

, or
*U**

, where *U*

denotes a
class that satisfies all the requirements of a uniform random number
generator. The member `engine_value_type`

shall name
*U*

.
Specializations of `variate_generator`

satisfy the
requirements of CopyConstructible. They also satisfy the requirements
of Assignable unless the template parameter `Engine`

is of
the form

.
*U*&

The complexity of all functions specified in this section is constant. No function described in this section except the constructor throws an exception.

variate_generator(engine_type eng, distribution_type d)

`variate_generator`

object with the associated uniform random number generator
`eng`

and the associated random distribution
`d`

.
result_type operator()()

`distribution()(e)`

`e`

, s`eng`

, s`numeric_limits<`*T*>::is_integer

for
*T*

both `Distribution::input_type`

and
`engine_value_type::result_type`

. If the values for both
types are `true`

, then s`false`

, then the numbers in s`engine().max()-engine().min()`

to obtain the
numbers in s`engine_value_type::result_type`

is `true`

and
the value for `Distribution::input_type`

is
`false`

, then the numbers in s`engine().max()-engine().min()+1`

to obtain the
numbers in s`engine_value_type::result_type`

to
`Distribution::input_type`

is performed. If such a
conversion does not exist, the program is ill-formed.
template<class T> result_type operator()(T value)

`distribution()(e, value)`

. For
the semantics of `e`

, see the description of
`operator()()`

.
engine_value_type& engine()

const engine_value_type& engine() const

distribution_type& distribution()

const distribution_type& distribution() const

result_type min() const

`distribution().min()`

is
well-formed
`distribution().min()`

result_type max() const

`distribution().max()`

is
well-formed
`distribution().max()`

The class templates specified in this section satisfy all the requirements of a pseudo-random number engine (given in tables in section x.x), except where specified otherwise. Descriptions are provided here only for operations on the engines that are not described in one of these tables or for operations where there is additional semantic information.

All members declared `static const`

in any of the following
class templates shall be defined in such a way that they are usable as
integral constant expressions.

`linear_congruential`

`linear_congruential`

engine produces random numbers
using a linear function x(i+1) := (a * x(i) + c) mod m.
namespace std { template<class IntType, IntType a, IntType c, IntType m> class linear_congruential { public: //The template parametertypestypedef IntType result_type; //parameter valuesstatic const IntType multiplier = a; static const IntType increment = c; static const IntType modulus = m; //constructors and member functionexplicit linear_congruential(IntType x0 = 1); template<class In> linear_congruential(In& first, In last); void seed(IntType x0 = 1); template<class In> void seed(In& first, In last); result_type min() const; result_type max() const; result_type operator()(); }; template<class IntType, IntType a, IntType c, IntType m> bool operator==(const linear_congruential<IntType, a, c, m>& x, const linear_congruential<IntType, a, c, m>& y); template<class IntType, IntType a, IntType c, IntType m> bool operator!=(const linear_congruential<IntType, a, c, m>& x, const linear_congruential<IntType, a, c, m>& y); template<class CharT, class traits, class IntType, IntType a, IntType c, IntType m> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const linear_congruential<IntType, a, c, m>& x); template<class CharT, class traits, class IntType, IntType a, IntType c, IntType m> basic_istream<CharT, traits>& operator>>(basic_istream<CharT, traits>& is, linear_congruential<IntType, a, c, m>& x); }

`IntType`

shall denote an integral
type large enough to store values up to (m-1). If the template
parameter `m`

is 0, the behaviour is
implementation-defined. Otherwise, the template parameters
`a`

and `c`

shall be less than m.
The size of the state x(i) is 1.

explicit linear_congruential(IntType x0 = 1)

`c > 0 || (x0 % m) > 0`

`linear_congruential`

engine with state x(0) :=
`x0`

mod m.
void seed(IntType x0 = 1)

`c > 0 || (x0 % m) > 0`

`x0`

mod m.
template<class In> linear_congruential(In& first, In last)

`c > 0 || *first > 0`

`*first`

mod m.
`*first`

.
template<class CharT, class traits, class IntType, IntType a, IntType c, IntType m> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const linear_congruential<IntType, a, c, m>& x);

`os`

.
`mersenne_twister`

`mersenne_twister`

engine produces random numbers
o(x(i)) using the following computation, performed modulo
2`um`

is a value with only the upper
`w-r`

bits set in its binary representation.
`lm`

is a value with only its lower `r`

bits set
in its binary representation. - y(i) = (x(i-n)
*bitand*um) | (x(i-(n-1))*bitand*lm) - If the lowest bit of the binary representation of y(i) is set,
x(i) = x(i-(n-m))
*xor*(y(i)*rshift*1)*xor*a; otherwise x(i) = x(i-(n-m))*xor*(y(i)*rshift*1). - z1(i) = x(i)
*xor*( x(i)*rshift*u ) - z2(i) = z1(i)
*xor*( (z1(i)*lshift*s)*bitand*b ) - z3(i) = z2(i)
*xor*( (z2(i)*lshift*t)*bitand*c ) - o(x(i)) = z3(i)
*xor*( z3(i)*rshift*l )

namespace std { template<class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> class mersenne_twister { public: //The template parametertypestypedef UIntType result_type; //parameter valuesstatic const int word_size = w; static const int state_size = n; static const int shift_size = m; static const int mask_bits = r; static const UIntType parameter_a = a; static const int output_u = u; static const int output_s = s; static const UIntType output_b = b; static const int output_t = t; static const UIntType output_c = c; static const int output_l = l; //constructors and member functionmersenne_twister(); explicit mersenne_twister(UIntType value); template<class In> mersenne_twister(In& first, In last); void seed(); void seed(UIntType value); template<class In> void seed(In& first, In last); result_type min() const; result_type max() const; result_type operator()(); }; template<class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> bool operator==(const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& y, const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& x); template<class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> bool operator!=(const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& y, const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& x); template<class CharT, class traits, class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& x); template<class CharT, class traits, class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> basic_istream<CharT, traits>& operator>>(basic_istream<CharT, traits>& is, mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& x); }

`UIntType`

shall denote an unsigned
integral type large enough to store values up to
2
The size of the state x(i) is `n`

.

mersenne_twister()

`mersenne_twister`

engine and invokes `seed()`

.
explicit mersenne_twister(result_type value)

`mersenne_twister`

engine and invokes `seed(value)`

.
template<class In> mersenne_twister(In& first, In last)

`mersenne_twister`

engine and invokes `seed(first, last)`

.
void seed()

`seed(4357)`

.
void seed(result_type value)

`value > 0`

`value`

, sets x(-n) ... x(-1)
to l(1) ... l(n), respectively.
template<class In> void seed(In& first, In last)

`n`

dereferences of
`first`

.
template<class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> bool operator==(const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& y, const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& x)

`x`

is
o(x(i)) and the next output of `y`

is o(y(j)).
template<class CharT, class traits, class UIntType, int w, int n, int m, int r, UIntType a, int u, int s, UIntType b, int t, UIntType c, int l> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const mersenne_twister<UIntType, w, n, m, r, a, u, s, b, t, c, l>& x)

`os`

, in that order.
`subtract_with_carry`

`subtract_with_carry`

engine produces integer random numbers
using x(i) = (x(i-s) - x(i-r) - carry(i-1)) mod m; carry(i) = 1 if
x(i-s) - x(i-r) - carry(i-1) < 0, else carry(i) = 0.

namespace std { template<class IntType, IntType m, int s, int r> class subtract_with_carry { public: //The template parametertypestypedef IntType result_type; //parameter valuesstatic const IntType modulus = m; static const int long_lag = r; static const int short_lag = s; //constructors and member functionsubtract_with_carry(); explicit subtract_with_carry(IntType value); template<class In> subtract_with_carry(In& first, In last); void seed(IntType value = 19780503); template<class In> void seed(In& first, In last); result_type min() const; result_type max() const; result_type operator()(); }; template<class IntType, IntType m, int s, int r> bool operator==(const subtract_with_carry<IntType, m, s, r> & x, const subtract_with_carry<IntType, m, s, r> & y); template<class IntType, IntType m, int s, int r> bool operator!=(const subtract_with_carry<IntType, m, s, r> & x, const subtract_with_carry<IntType, m, s, r> & y); template<class CharT, class Traits, class IntType, IntType m, int s, int r> std::basic_ostream<CharT,Traits>& operator<<(std::basic_ostream<CharT,Traits>& os, const subtract_with_carry<IntType, m, s, r>& f); template<class CharT, class Traits, class IntType, IntType m, int s, int r> std::basic_istream<CharT,Traits>& operator>>(std::basic_istream<CharT,Traits>& is, subtract_with_carry<IntType, m, s, r>& f); }

`IntType`

shall denote a signed
integral type large enough to store values up to m-1. The following
relation shall hold: 0<s<r. Let w the number of bits in the
binary representation of m.
The size of the state is `r`

.

subtract_with_carry()

`subtract_with_carry`

engine and invokes `seed()`

.
explicit subtract_with_carry(IntType value)

`subtract_with_carry`

engine and invokes `seed(value)`

.
template<class In> subtract_with_carry(In& first, In last)

`subtract_with_carry`

engine and invokes `seed(first, last)`

.
void seed(IntType value = 19780503)

`value > 0`

`value`

, sets x(-r) ... x(-1)
to l(1) mod m ... l(r) mod m, respectively. If x(-1) == 0, sets
carry(-1) = 1, else sets carry(-1) = 0.
template<class In> void seed(In& first, In last)

`r*n`

dereferences of
`first`

.
template<class IntType, IntType m, int s, int r> bool operator==(const subtract_with_carry<IntType, m, s, r> & x, const subtract_with_carry<IntType, m, s, r> & y)

`x`

is
x(i) and the next output of `y`

is y(j).
template<class CharT, class Traits, class IntType, IntType m, int s, int r> std::basic_ostream<CharT,Traits>& operator<<(std::basic_ostream<CharT,Traits>& os, const subtract_with_carry<IntType, m, s, r>& f)

`os`

, in that order.
`subtract_with_carry_01`

`subtract_with_carry_01`

engine produces floating-point
random numbers using x(i) = (x(i-s) - x(i-r) - carry(i-1)) mod 1;
carry(i) = 2

namespace std { template<class RealType, int w, int s, int r> class subtract_with_carry_01 { public: //The following relation shall hold: 0<s<r.typestypedef RealType result_type; //parameter valuesstatic const int word_size = w; static const int long_lag = r; static const int short_lag = s; //constructors and member functionsubtract_with_carry_01(); explicit subtract_with_carry_01(unsigned int value); template<class In> subtract_with_carry_01(In& first, In last); void seed(unsigned int value = 19780503); template<class In> void seed(In& first, In last); result_type min() const; result_type max() const; result_type operator()(); }; template<class RealType, int w, int s, int r> bool operator==(const subtract_with_carry_01<RealType, w, s, r> x, const subtract_with_carry_01<RealType, w, s, r> y); template<class RealType, int w, int s, int r> bool operator!=(const subtract_with_carry_01<RealType, w, s, r> x, const subtract_with_carry_01<RealType, w, s, r> y); template<class CharT, class Traits, class RealType, int w, int s, int r> std::basic_ostream<CharT,Traits>& operator<<(std::basic_ostream<CharT,Traits>& os, const subtract_with_carry_01<RealType, w, s, r>& f); template<class CharT, class Traits, class RealType, int w, int s, int r> std::basic_istream<CharT,Traits>& operator>>(std::basic_istream<CharT,Traits>& is, subtract_with_carry_01<RealType, w, s, r>& f); }

The size of the state is `r`

.

subtract_with_carry_01()

`subtract_with_carry_01`

engine and invokes `seed()`

.
explicit subtract_with_carry_01(unsigned int value)

`subtract_with_carry_01`

engine and invokes `seed(value)`

.
template<class In> subtract_with_carry_01(In& first, In last)

`subtract_with_carry_01`

engine and invokes `seed(first, last)`

.
void seed(unsigned int value = 19780503)

`value`

, sets x(-r) ... x(-1) to (l(1)*2template<class In> void seed(In& first, In last)

template<class RealType, int w, int s, int r> bool operator==(const subtract_with_carry<RealType, w, s, r> x, const subtract_with_carry<RealType, w, s, r> y);

template<class CharT, class Traits, class RealType, int w, int s, int r> std::basic_ostream<CharT,Traits>& operator<<(std::basic_ostream<CharT,Traits>& os, const subtract_with_carry<RealType, w, s, r>& f);

`os`

,
in that order.
`discard_block`

`discard_block`

engine produces random numbers from some
base engine by discarding blocks of data.

namespace std { template<class UniformRandomNumberGenerator, int p, int r> class discard_block { public: //The template parametertypestypedef UniformRandomNumberGenerator base_type; typedef typename base_type::result_type result_type; //parameter valuesstatic const int block_size = p; static const int used_block = r; //constructors and member functiondiscard_block(); explicit discard_block(const base_type & rng); template<class In> discard_block(In& first, In last); void seed(); template<class In> void seed(In& first, In last); const base_type& base() const; result_type min() const; result_type max() const; result_type operator()(); private: // base_type b;exposition only// int n;exposition only}; template<class UniformRandomNumberGenerator, int p, int r> bool operator==(const discard_block<UniformRandomNumberGenerator,p,r> & x, (const discard_block<UniformRandomNumberGenerator,p,r> & y); template<class UniformRandomNumberGenerator, int p, int r, typename UniformRandomNumberGenerator::result_type val> bool operator!=(const discard_block<UniformRandomNumberGenerator,p,r> & x, (const discard_block<UniformRandomNumberGenerator,p,r> & y); template<class CharT, class traits, class UniformRandomNumberGenerator, int p, int r> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const discard_block<UniformRandomNumberGenerator,p,r> & x); template<class CharT, class traits, class UniformRandomNumberGenerator, int p, int r> basic_istream<CharT, traits>& operator>>(basic_istream<CharT, traits>& is, discard_block<UniformRandomNumberGenerator,p,r> & x); }

`UniformRandomNumberGenerator`

shall
denote a class that satisfies all the requirements of a uniform random
number generator, given in tables in section x.x. r <= p. The size
of the state is the size of *b*

plus 1.
discard_block()

`discard_block`

engine. To construct the subobject `n = 0`

.
explicit discard_block(const base_type & rng)

`discard_block`

engine. Initializes `rng`

.
Sets `n = 0`

.
template<class In> discard_block(In& first, In last)

`discard_block`

engine. To construct the subobject `b(first, last)`

constructor. Sets `n = 0`

.
void seed()

*b*.seed()

and sets `n = 0`

.
template<class In> void seed(In& first, In last)

*b*.seed(first,
last)

and sets `n = 0`

.
const base_type& base() const

result_type operator()()

*b*

(p-r) times, discards the values returned,
and sets `n = 0`

. In any case, then increments
`n`

and returns *b()*

.
template<class CharT, class traits, class UniformRandomNumberGenerator, int p, int r> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const discard_block<UniformRandomNumberGenerator,p,r> & x);

*b*

, then
*n*

to `os`

.
`xor_combine`

`xor_combine`

engine produces random numbers from two
integer base engines by merging their random values with bitwise
exclusive-or.

namespace std { template<class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> class xor_combine { public: //The template parameterstypestypedef UniformRandomNumberGenerator1 base1_type; typedef UniformRandomNumberGenerator2 base2_type; typedef typename base_type::result_type result_type; //parameter valuesstatic const int shift1 = s1; static const int shift2 = s2; //constructors and member functionxor_combine(); xor_combine(const base1_type & rng1, const base2_type & rng2); template<class In> xor_combine(In& first, In last); void seed(); template<class In> void seed(In& first, In last); const base1_type& base1() const; const base2_type& base2() const; result_type min() const; result_type max() const; result_type operator()(); private: // base1_type b1;exposition only// base2_type b2;exposition only}; template<class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> bool operator==(const xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & x, (const xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & y); template<class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> bool operator!=(const xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & x, (const xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & y); template<class CharT, class traits, class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & x); template<class CharT, class traits, class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> basic_istream<CharT, traits>& operator>>(basic_istream<CharT, traits>& is, xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & x); }

`UniformRandomNumberGenerator1`

and
`UniformRandomNumberGenerator1`

shall denote classes that
satisfy all the requirements of a uniform random number generator,
given in tables in section x.x . The size of the state is
the size of *b1*

plus the size of
*b2*

.
xor_combine()

`xor_combine`

engine. To construct each of the subobjects xor_combine(const base1_type & rng1, const base2_type & rng2)

`xor_combine`

engine. Initializes `rng1`

and
`rng2`

.
template<class In> xor_combine(In& first, In last)

`xor_combine`

engine. To construct the subobject `b1(first, last)`

constructor. Then, to construct the
subobject `b2(first, last)`

constructor.
void seed()

*b1*.seed()

and *b2*.seed()

.
template<class In> void seed(In& first, In last)

*b1*.seed(first,
last)

, then invokes *b2*.seed(first, last)

.
const base1_type& base1() const

const base2_type& base2() const

result_type operator()()

*b1*() << s1) ^
(*b2*() << s2)

.
template<class CharT, class traits, class UniformRandomNumberGenerator1, int s1, class UniformRandomNumberGenerator2, int s2> basic_ostream<CharT, traits>& operator<<(basic_ostream<CharT, traits>& os, const xor_combine<UniformRandomNumberGenerator1, s1, UniformRandomNumberGenerator2, s2> & x);

*b1*

, then
*b2*

to `os`

.
namespace std { typedef linear_congruential</*For a default-constructedimplementation defined*/, 16807, 0, 2147483647> minstd_rand0; typedef linear_congruential</*implementation defined*/, 48271, 0, 2147483647> minstd_rand; typedef mersenne_twister</*implementation defined*/,32,624,397,31,0x9908b0df,11,7,0x9d2c5680,15,0xefc60000,18> mt19937; typedef subtract_with_carry_01ranlux_base_01; typedef subtract_with_carry_01 ranlux64_base_01; typedef discard_block<subtract_with_carry</* implementation defined*/, (1<<24), 10, 24>, 223, 24> ranlux3; typedef discard_block<subtract_with_carry</*implementation defined*/, (1<<24), 10, 24>, 389, 24> ranlux4; typedef discard_block<subtract_with_carry_01<float, 24, 10, 24>, 223, 24> ranlux3_01; typedef discard_block<subtract_with_carry_01<float, 24, 10, 24>, 389, 24> ranlux4_01; }

`minstd_rand0`

object, x(10000) =
1043618065. For a default-constructed `minstd_rand`

object, x(10000) = 399268537.
For a default-constructed `mt19937`

object, x(10000) =
3346425566.

For a default-constructed `ranlux3`

object, x(10000) =
5957620. For a default-constructed `ranlux4`

object,
x(10000) = 8587295. For a default-constructed `ranlux3_01`

object, x(10000) = 5957620 * 2^{-24}. For a
default-constructed `ranlux4_01`

object, x(10000) = 8587295
* 2^{-24}.

`random_device`

`random_device`

produces non-deterministic random
numbers. It satisfies all the requirements of a uniform random number
generator (given in tables in section x.x). Descriptions are provided
here only for operations on the engines that are not described in one
of these tables or for operations where there is additional semantic
information.
If implementation limitations prevent generating non-deterministic random numbers, the implementation can employ a pseudo-random number engine.

namespace std { class random_device { public: //typestypedef unsigned int result_type; //constructors, destructors and member functionsexplicit random_device(const std::string& token = /*implementation-defined*/); result_type min() const; result_type max() const; double entropy() const; result_type operator()(); private: random_device(const random_device& ); void operator=(const random_device& ); }; }

explicit random_device(const std::string& token = /*implementation-defined*/)

`random_device`

non-deterministic random number engine. The semantics and default
value of the `token`

parameter are implementation-defined.
[Footnote: The parameter is intended to allow an implementation to
differentiate between different sources of randomness.]
`exception`

if the `random_device`

could not be
initialized.
result_type min() const

`numeric_limits<result_type>::min()`

result_type max() const

`numeric_limits<result_type>::max()`

double entropy() const

`min()`

to
log`max()`

+1). A deterministic random
number generator (e.g. a pseudo-random number engine) has entropy 0.
result_type operator()()

`min()`

and `max()`

,
inclusive. It is implementation-defined how these values are
generated.
`exception`

if a random number could not be obtained.
A template parameter named `IntType`

shall denote a type
that represents an integer number. This type shall meet the
requirements for a numeric type (26.1 [lib.numeric.requirements]), the
binary operators +, -, *, /, % shall be applicable to it, and a
conversion from `int`

shall exist. *[Footnote: The
built-in types int and long meet these
requirements.]*

Given an object whose type is specified in this subclause, if the lifetime of the uniform random number generator referred to in the constructor invocation for that object has ended, any use of that object is undefined.

No function described in this section throws an exception, unless an
operation on values of `IntType`

or `RealType`

throws an exception. *[Note: Then, the effects are undefined,
see [lib.numeric.requirements]. ]*

The algorithms for producing each of the specified distributions are implementation-defined.

`uniform_int`

`uniform_int`

random distribution produces integer random
numbers x in the range min <= x <= max, with equal probability.
min and max are the parameters of the distribution.
A `uniform_int`

random distribution satisfies all the
requirements of a uniform random number generator (given in tables in
section x.x).

namespace std { template<class IntType = int> class uniform_int { public: //typestypedef IntType input_type; typedef IntType result_type; //constructors and member functionexplicit uniform_int(IntType min = 0, IntType max = 9); result_type min() const; result_type max() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng, result_type n); }; }

uniform_int(IntType min = 0, IntType max = 9)

`uniform_int`

object. `min`

and `max`

are the parameters of
the distribution.
result_type min() const

result_type max() const

result_type operator()(UniformRandomNumberGenerator& urng, result_type n)

`variate_generator`

object with a `uniform_int`

distribution to be used with std::random_shuffe, see
[lib.alg.random.shuffle]. ]`bernoulli_distribution`

`bernoulli_distribution`

random distribution produces
`bool`

values distributed with probabilities p(true) = p
and p(false) = 1-p. p is the parameter of the distribution.
namespace std { template<class RealType = double> class bernoulli_distribution { public: //typestypedef int input_type; typedef bool result_type; //constructors and member functionexplicit bernoulli_distribution(const RealType& p = RealType(0.5)); RealType p() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

bernoulli_distribution(const RealType& p = RealType(0.5))

`bernoulli_distribution`

object. `p`

is the
parameter of the distribution.
RealType p() const

`geometric_distribution`

`geometric_distribution`

random distribution produces
integer values namespace std { template<class IntType = int, class RealType = double> class geometric_distribution { public: //typestypedef RealType input_type; typedef IntType result_type; //constructors and member functionexplicit geometric_distribution(const RealType& p = RealType(0.5)); RealType p() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

geometric_distribution(const RealType& p = RealType(0.5))

`geometric_distribution`

object; `p`

is the
parameter of the distribution.
RealType p() const

`poisson_distribution`

`poisson_distribution`

random distribution produces
integer values namespace std { template<class IntType = int, class RealType = double> class poisson_distribution { public: //typestypedef RealType input_type; typedef IntType result_type; //constructors and member functionexplicit poisson_distribution(const RealType& mean = RealType(1)); RealType mean() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

poisson_distribution(const RealType& mean = RealType(1))

`poisson_distribution`

object; `mean`

is the
parameter of the distribution.
RealType mean() const

`binomial_distribution`

`binomial_distribution`

random distribution produces
integer values namespace std { template<class IntType = int, class RealType = double> class binomial_distribution { public: //typestypedef /*implementation-defined*/ input_type; typedef IntType result_type; //constructors and member functionexplicit binomial_distribution(IntType t = 1, const RealType& p = RealType(0.5)); IntType t() const; RealType p() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

binomial_distribution(IntType t = 1, const RealType& p = RealType(0.5))

`binomial_distribution`

object; `t`

and
`p`

are the parameters of the distribution.
IntType t() const

RealType p() const

`uniform_real`

`uniform_real`

random distribution produces
floating-point random numbers x in the range min <= x <= max,
with equal probability. min and max are the parameters of the
distribution.
A `uniform_real`

random distribution satisfies all the
requirements of a uniform random number generator (given in tables in
section x.x).

namespace std { template<class RealType = double> class uniform_real { public: //typestypedef RealType input_type; typedef RealType result_type; //constructors and member functionexplicit uniform_real(RealType min = RealType(0), RealType max = RealType(1)); result_type min() const; result_type max() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

uniform_real(RealType min = RealType(0), RealType max = RealType(1))

`uniform_real`

object; `min`

and
`max`

are the parameters of the distribution.
result_type min() const

result_type max() const

`exponential_distribution`

`exponential_distribution`

random distribution produces
random numbers x > 0 distributed with probability density function
p(x) = lambda * exp(-lambda * x), where lambda is the parameter of the
distribution.
namespace std { template<class RealType = double> class exponential_distribution { public: //typestypedef RealType input_type; typedef RealType result_type; //constructors and member functionexplicit exponential_distribution(const result_type& lambda = result_type(1)); RealType lambda() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

exponential_distribution(const result_type& lambda = result_type(1))

`exponential_distribution`

object with `rng`

as
the reference to the underlying source of random
numbers. `lambda`

is the parameter for the distribution.
RealType lambda() const

`normal_distribution`

`normal_distribution`

random distribution produces
random numbers x distributed with probability density function
p(x) = 1/sqrt(2*pi*sigma) * exp(- (x-mean)namespace std { template<class RealType = double> class normal_distribution { public: //typestypedef RealType input_type; typedef RealType result_type; //constructors and member functionexplicit normal_distribution(base_type & rng, const result_type& mean = 0, const result_type& sigma = 1); RealType mean() const; RealType sigma() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

explicit normal_distribution( const result_type& mean = 0, const result_type& sigma = 1);

`normal_distribution`

object; `mean`

and
`sigma`

are the parameters for the distribution.
RealType mean() const

RealType sigma() const

`gamma_distribution`

`gamma_distribution`

random distribution produces
random numbers x distributed with probability density function
p(x) = 1/Gamma(alpha) * xnamespace std { template<class RealType = double> class gamma_distribution { public: //typestypedef RealType input_type; typedef RealType result_type; //constructors and member functionexplicit gamma_distribution(const result_type& alpha = result_type(1)); RealType alpha() const; void reset(); template<class UniformRandomNumberGenerator> result_type operator()(UniformRandomNumberGenerator& urng); }; }

explicit gamma_distribution(const result_type& alpha = result_type(1));

`gamma_distribution`

object; `alpha`

is the
parameter for the distribution.
RealType alpha() const

- Thanks to Walter Brown, Mark Fischler and Marc Paterno from Fermilab for input about the requirements of high-energy physics.
- Thanks to David Abrahams for additional comments on the design.
- Thanks to the Boost community for a platform for experimentation.

- William H. Press, Saul A. Teukolsky, William A. Vetterling, Brian P. Flannery, "Numerical Recipes in C: The art of scientific computing", 2nd ed., 1992, pp. 274-328
- Bruce Schneier, "Applied Cryptography", 2nd ed., 1996, ch. 16-17. [I haven't read this myself. Yet.]
- D. H. Lehmer, "Mathematical methods in large-scale computing units", Proc. 2nd Symposium on Large-Scale Digital Calculating Machines, Harvard University Press, 1951, pp. 141-146
- P.A. Lewis, A.S. Goodman, J.M. Miller, "A pseudo-random number generator for the System/360", IBM Systems Journal, Vol. 8, No. 2, 1969, pp. 136-146
- Stephen K. Park and Keith W. Miller, "Random Number Generators: Good ones are hard to find", Communications of the ACM, Vol. 31, No. 10, October 1988, pp. 1192-1201
- Makoto Matsumoto and Takuji Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudo-random number generator", ACM Transactions on Modeling and Computer Simulation: Special Issue on Uniform Random Number Generation, Vol. 8, No. 1, January 1998, pp. 3-30. http://www.math.keio.ac.jp/matumoto/emt.html
- Donald E. Knuth, "The Art of Computer Programming, Vol. 2", 3rd ed., 1997, pp. 1-193.
- Carter Bays and S.D. Durham, "Improving a poor random number generator", ACM Transactions on Mathematical Software, Vol. 2, 1979, pp. 59-64.
- Martin Lüscher, "A portable high-quality random number generator for lattice field theory simulations.", Computer Physics Communications, Vol. 79, 1994, pp. 100-110.
- William J. Hurd, "Efficient Generation of Statistically Good Pseudonoise by Linearly Interconnected Shift Registers", Technical Report 32-1526, Volume XI, The Deep Space Network Progress Report for July and August 1972, NASA Jet Propulsion Laboratory, 1972 and IEEE Transactions on Computers Vol. 23, 1974.
- Pierre L'Ecuyer, "Efficient and Portable Combined Random Number Generators", Communications of the ACM, Vol. 31, pp. 742-749+774, 1988.
- Pierre L'Ecuyer, "Maximally equidistributed combined Tausworthe generators", Mathematics of Computation Vol. 65, pp. 203-213, 1996.
- Pierre L'Ecuyer, "Good parameters and implementations for combined multple recursive random number generators", Operations Research Vol. 47, pp. 159-164, 1999.
- S. Kirkpatrick and E. Stoll, "A very fast shift-register sequence random number generator", Journal of Computational Physics, Vol. 40, pp. 517-526, 1981.
- R. C. Tausworthe, "Random numbers generated by iinear recurrence modulo two", Mathematics of Computation, Vol. 19, pp. 201-209, 1965.
- George Marsaglia and Arif Zaman, "A New Class of Random Number Generators", Annals of Applied Probability, Vol. 1, No. 3, 1991.