随机数

Random number generation in Julia uses the Xoshiro256++ algorithm by default, with per-Task state. Other RNG types can be plugged in by inheriting the AbstractRNG type; they can then be used to obtain multiple streams of random numbers. Besides the default TaskLocalRNG type, the Random package also provides MersenneTwister, RandomDevice (which exposes OS-provided entropy), and Xoshiro (for explicitly-managed Xoshiro256++ streams).

Most functions related to random generation accept an optional AbstractRNG object as first argument. Some also accept dimension specifications dims... (which can also be given as a tuple) to generate arrays of random values. In a multi-threaded program, you should generally use different RNG objects from different threads or tasks in order to be thread-safe. However, the default RNG is thread-safe as of Julia 1.3 (using a per-thread RNG up to version 1.6, and per-task thereafter).

The provided RNGs can generate uniform random numbers of the following types: Float16, Float32, Float64, BigFloat, Bool, Int8, UInt8, Int16, UInt16, Int32, UInt32, Int64, UInt64, Int128, UInt128, BigInt (or complex numbers of those types). Random floating point numbers are generated uniformly in $[0, 1)$. As BigInt represents unbounded integers, the interval must be specified (e.g. rand(big.(1:6))).

另外，正态和指数分布是针对某些 AbstractFloat 和 Complex 类型，详细内容见 randn 和 randexp。

Warning

Because the precise way in which random numbers are generated is considered an implementation detail, bug fixes and speed improvements may change the stream of numbers that are generated after a version change. Relying on a specific seed or generated stream of numbers during unit testing is thus discouraged - consider testing properties of the methods in question instead.

Random numbers module

Random.Random — Module

  1. Random

Support for generating random numbers. Provides rand, randn, AbstractRNG, MersenneTwister, and RandomDevice.

Random generation functions

Base.rand — Function

  1. rand([rng=GLOBAL_RNG], [S], [dims...])

Pick a random element or array of random elements from the set of values specified by S; S can be

  • an indexable collection (for example 1:9 or ('x', "y", :z)),
  • an AbstractDict or AbstractSet object,
  • a string (considered as a collection of characters), or
  • a type: the set of values to pick from is then equivalent to typemin(S):typemax(S) for integers (this is not applicable to BigInt), to $[0, 1)$ for floating point numbers and to $[0, 1)+i[0, 1)$ for complex floating point numbers;

S defaults to Float64. When only one argument is passed besides the optional rng and is a Tuple, it is interpreted as a collection of values (S) and not as dims.

Julia 1.1

Support for S as a tuple requires at least Julia 1.1.

Examples

  1. julia> rand(Int, 2)
  2. 2-element Array{Int64,1}:
  3.  1339893410598768192
  4.  1575814717733606317
  6. julia> using Random
  8. julia> rand(MersenneTwister(0), Dict(1=>2, 3=>4))
  9. 1=>2
  11. julia> rand((2, 3))
  12. 3
  14. julia> rand(Float64, (2, 3))
  15. 2×3 Array{Float64,2}:
  16.  0.999717  0.0143835  0.540787
  17.  0.696556  0.783855   0.938235

Note

The complexity of rand(rng, s::Union{AbstractDict,AbstractSet}) is linear in the length of s, unless an optimized method with constant complexity is available, which is the case for Dict, Set and BitSet. For more than a few calls, use rand(rng, collect(s)) instead, or either rand(rng, Dict(s)) or rand(rng, Set(s)) as appropriate.

Random.rand! — Function

  1. rand!([rng=GLOBAL_RNG], A, [S=eltype(A)])

Populate the array A with random values. If S is specified (S can be a type or a collection, cf. rand for details), the values are picked randomly from S. This is equivalent to copyto!(A, rand(rng, S, size(A))) but without allocating a new array.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> rand!(rng, zeros(5))
  4. 5-element Vector{Float64}:
  5.  0.5908446386657102
  6.  0.7667970365022592
  7.  0.5662374165061859
  8.  0.4600853424625171
  9.  0.7940257103317943

Random.bitrand — Function

  1. bitrand([rng=GLOBAL_RNG], [dims...])

Generate a BitArray of random boolean values.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> bitrand(rng, 10)
  4. 10-element BitVector:
  5.  0
  6.  0
  7.  0
  8.  0
  9.  1
  10.  0
  11.  0
  12.  0
  13.  1
  14.  1

Base.randn — Function

  1. randn([rng=GLOBAL_RNG], [T=Float64], [dims...])

Generate a normally-distributed random number of type T with mean 0 and standard deviation 1. Optionally generate an array of normally-distributed random numbers. The Base module currently provides an implementation for the types Float16, Float32, and Float64 (the default), and their Complex counterparts. When the type argument is complex, the values are drawn from the circularly symmetric complex normal distribution of variance 1 (corresponding to real and imaginary part having independent normal distribution with mean zero and variance 1/2).

Examples

  1. julia> using Random
  3. julia> rng = MersenneTwister(1234);
  5. julia> randn(rng, ComplexF64)
  6. 0.6133070881429037 - 0.6376291670853887im
  8. julia> randn(rng, ComplexF32, (2, 3))
  9. 2×3 Matrix{ComplexF32}:
  10.  -0.349649-0.638457im  0.376756-0.192146im  -0.396334-0.0136413im
  11.   0.611224+1.56403im   0.355204-0.365563im  0.0905552+1.31012im

Random.randn! — Function

  1. randn!([rng=GLOBAL_RNG], A::AbstractArray) -> A

Fill the array A with normally-distributed (mean 0, standard deviation 1) random numbers. Also see the rand function.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> randn!(rng, zeros(5))
  4. 5-element Vector{Float64}:
  5.   0.8673472019512456
  6.  -0.9017438158568171
  7.  -0.4944787535042339
  8.  -0.9029142938652416
  9.   0.8644013132535154

Random.randexp — Function

  1. randexp([rng=GLOBAL_RNG], [T=Float64], [dims...])

Generate a random number of type T according to the exponential distribution with scale 1. Optionally generate an array of such random numbers. The Base module currently provides an implementation for the types Float16, Float32, and Float64 (the default).

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> randexp(rng, Float32)
  4. 2.4835055f0
  6. julia> randexp(rng, 3, 3)
  7. 3×3 Matrix{Float64}:
  8.  1.5167    1.30652   0.344435
  9.  0.604436  2.78029   0.418516
  10.  0.695867  0.693292  0.643644

Random.randexp! — Function

  1. randexp!([rng=GLOBAL_RNG], A::AbstractArray) -> A

Fill the array A with random numbers following the exponential distribution (with scale 1).

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> randexp!(rng, zeros(5))
  4. 5-element Vector{Float64}:
  5.  2.4835053723904896
  6.  1.516703605376473
  7.  0.6044364871025417
  8.  0.6958665886385867
  9.  1.3065196315496677

Random.randstring — Function

  1. randstring([rng=GLOBAL_RNG], [chars], [len=8])

Create a random string of length len, consisting of characters from chars, which defaults to the set of upper- and lower-case letters and the digits 0-9. The optional rng argument specifies a random number generator, see Random Numbers.

Examples

  1. julia> Random.seed!(3); randstring()
  2. "Lxz5hUwn"
  4. julia> randstring(MersenneTwister(3), 'a':'z', 6)
  5. "ocucay"
  7. julia> randstring("ACGT")
  8. "TGCTCCTC"

Note

chars can be any collection of characters, of type Char or UInt8 (more efficient), provided rand can randomly pick characters from it.

Subsequences, permutations and shuffling

Random.randsubseq — Function

  1. randsubseq([rng=GLOBAL_RNG,] A, p) -> Vector

Return a vector consisting of a random subsequence of the given array A, where each element of A is included (in order) with independent probability p. (Complexity is linear in p*length(A), so this function is efficient even if p is small and A is large.) Technically, this process is known as “Bernoulli sampling” of A.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> randsubseq(rng, 1:8, 0.3)
  4. 2-element Vector{Int64}:
  5.  7
  6.  8

Random.randsubseq! — Function

  1. randsubseq!([rng=GLOBAL_RNG,] S, A, p)

Like randsubseq, but the results are stored in S (which is resized as needed).

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> S = Int64[];
  5. julia> randsubseq!(rng, S, 1:8, 0.3)
  6. 2-element Vector{Int64}:
  7.  7
  8.  8
  10. julia> S
  11. 2-element Vector{Int64}:
  12.  7
  13.  8

Random.randperm — Function

  1. randperm([rng=GLOBAL_RNG,] n::Integer)

Construct a random permutation of length n. The optional rng argument specifies a random number generator (see Random Numbers). The element type of the result is the same as the type of n.

To randomly permute an arbitrary vector, see shuffle or shuffle!.

Julia 1.1

In Julia 1.1 randperm returns a vector v with eltype(v) == typeof(n) while in Julia 1.0 eltype(v) == Int.

Examples

  1. julia> randperm(MersenneTwister(1234), 4)
  2. 4-element Vector{Int64}:
  3.  2
  4.  1
  5.  4
  6.  3

Random.randperm! — Function

  1. randperm!([rng=GLOBAL_RNG,] A::Array{<:Integer})

Construct in A a random permutation of length length(A). The optional rng argument specifies a random number generator (see Random Numbers). To randomly permute an arbitrary vector, see shuffle or shuffle!.

Examples

  1. julia> randperm!(MersenneTwister(1234), Vector{Int}(undef, 4))
  2. 4-element Vector{Int64}:
  3.  2
  4.  1
  5.  4
  6.  3

Random.randcycle — Function

  1. randcycle([rng=GLOBAL_RNG,] n::Integer)

Construct a random cyclic permutation of length n. The optional rng argument specifies a random number generator, see Random Numbers. The element type of the result is the same as the type of n.

Julia 1.1

In Julia 1.1 randcycle returns a vector v with eltype(v) == typeof(n) while in Julia 1.0 eltype(v) == Int.

Examples

  1. julia> randcycle(MersenneTwister(1234), 6)
  2. 6-element Vector{Int64}:
  3.  3
  4.  5
  5.  4
  6.  6
  7.  1
  8.  2

Random.randcycle! — Function

  1. randcycle!([rng=GLOBAL_RNG,] A::Array{<:Integer})

Construct in A a random cyclic permutation of length length(A). The optional rng argument specifies a random number generator, see Random Numbers.

Examples

  1. julia> randcycle!(MersenneTwister(1234), Vector{Int}(undef, 6))
  2. 6-element Vector{Int64}:
  3.  3
  4.  5
  5.  4
  6.  6
  7.  1
  8.  2

Random.shuffle — Function

  1. shuffle([rng=GLOBAL_RNG,] v::AbstractArray)

Return a randomly permuted copy of v. The optional rng argument specifies a random number generator (see Random Numbers). To permute v in-place, see shuffle!. To obtain randomly permuted indices, see randperm.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> shuffle(rng, Vector(1:10))
  4. 10-element Vector{Int64}:
  5.   6
  6.   1
  7.  10
  8.   2
  9.   3
  10.   9
  11.   5
  12.   7
  13.   4
  14.   8

Random.shuffle! — Function

  1. shuffle!([rng=GLOBAL_RNG,] v::AbstractArray)

In-place version of shuffle: randomly permute v in-place, optionally supplying the random-number generator rng.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> shuffle!(rng, Vector(1:16))
  4. 16-element Vector{Int64}:
  5.   2
  6.  15
  7.   5
  8.  14
  9.   1
  10.   9
  11.  10
  12.   6
  13.  11
  14.   3
  15.  16
  16.   7
  17.   4
  18.  12
  19.   8
  20.  13

Generators (creation and seeding))

Random.seed! — Function

  1. seed!([rng=GLOBAL_RNG], seed) -> rng
  2. seed!([rng=GLOBAL_RNG]) -> rng

Reseed the random number generator: rng will give a reproducible sequence of numbers if and only if a seed is provided. Some RNGs don’t accept a seed, like RandomDevice. After the call to seed!, rng is equivalent to a newly created object initialized with the same seed.

If rng is not specified, it defaults to seeding the state of the shared task-local generator.

Examples

  1. julia> Random.seed!(1234);
  3. julia> x1 = rand(2)
  4. 2-element Array{Float64,1}:
  5.  0.590845
  6.  0.766797
  8. julia> Random.seed!(1234);
  10. julia> x2 = rand(2)
  11. 2-element Array{Float64,1}:
  12.  0.590845
  13.  0.766797
  15. julia> x1 == x2
  16. true
  18. julia> rng = MersenneTwister(1234); rand(rng, 2) == x1
  19. true
  21. julia> MersenneTwister(1) == Random.seed!(rng, 1)
  22. true
  24. julia> rand(Random.seed!(rng), Bool) # not reproducible
  25. true
  27. julia> rand(Random.seed!(rng), Bool)
  28. false
  30. julia> rand(MersenneTwister(), Bool) # not reproducible either
  31. true

Random.AbstractRNG — Type

  1. AbstractRNG

Supertype for random number generators such as MersenneTwister and RandomDevice.

Random.TaskLocalRNG — Type

  1. TaskLocalRNG

The TaskLocalRNG has state that is local to its task, not its thread. It is seeded upon task creation, from the state of its parent task. Therefore, task creation is an event that changes the parent’s RNG state.

As an upside, the TaskLocalRNG is pretty fast, and permits reproducible multithreaded simulations (barring race conditions), independent of scheduler decisions. As long as the number of threads is not used to make decisions on task creation, simulation results are also independent of the number of available threads / CPUs. The random stream should not depend on hardware specifics, up to endianness and possibly word size.

Using or seeding the RNG of any other task than the one returned by current_task() is undefined behavior: it will work most of the time, and may sometimes fail silently.

Random.Xoshiro — Type

  1. Xoshiro

Xoshiro256++ is a fast pseudorandom number generator originally developed by Sebastian Vigna. Reference implementation is available at http://prng.di.unimi.it

Apart from the high speed, Xoshiro has a small memory footprint, making it suitable for applications where many different random states need to be held for long time.

Julia’s Xoshiro implementation has a bulk-generation mode; this seeds new virtual PRNGs from the parent, and uses SIMD to generate in parallel (i.e. the bulk stream consists of multiple interleaved xoshiro instances). The virtual PRNGs are discarded once the bulk request has been serviced (and should cause no heap allocations).

Random.MersenneTwister — Type

  1. MersenneTwister(seed)
  2. MersenneTwister()

Create a MersenneTwister RNG object. Different RNG objects can have their own seeds, which may be useful for generating different streams of random numbers. The seed may be a non-negative integer or a vector of UInt32 integers. If no seed is provided, a randomly generated one is created (using entropy from the system). See the seed! function for reseeding an already existing MersenneTwister object.

Examples

  1. julia> rng = MersenneTwister(1234);
  3. julia> x1 = rand(rng, 2)
  4. 2-element Vector{Float64}:
  5.  0.5908446386657102
  6.  0.7667970365022592
  8. julia> rng = MersenneTwister(1234);
  10. julia> x2 = rand(rng, 2)
  11. 2-element Vector{Float64}:
  12.  0.5908446386657102
  13.  0.7667970365022592
  15. julia> x1 == x2
  16. true

Random.RandomDevice — Type

  1. RandomDevice()

Create a RandomDevice RNG object. Two such objects will always generate different streams of random numbers. The entropy is obtained from the operating system.

Hooking into the Random API

There are two mostly orthogonal ways to extend Random functionalities:

  1. generating random values of custom types
  2. creating new generators

The API for 1) is quite functional, but is relatively recent so it may still have to evolve in subsequent releases of the Random module. For example, it’s typically sufficient to implement one rand method in order to have all other usual methods work automatically.

The API for 2) is still rudimentary, and may require more work than strictly necessary from the implementor, in order to support usual types of generated values.

Generating random values of custom types

Generating random values for some distributions may involve various trade-offs. Pre-computed values, such as an alias table for discrete distributions, or “squeezing” functions for univariate distributions, can speed up sampling considerably. How much information should be pre-computed can depend on the number of values we plan to draw from a distribution. Also, some random number generators can have certain properties that various algorithms may want to exploit.

The Random module defines a customizable framework for obtaining random values that can address these issues. Each invocation of rand generates a sampler which can be customized with the above trade-offs in mind, by adding methods to Sampler, which in turn can dispatch on the random number generator, the object that characterizes the distribution, and a suggestion for the number of repetitions. Currently, for the latter, Val{1} (for a single sample) and Val{Inf} (for an arbitrary number) are used, with Random.Repetition an alias for both.

The object returned by Sampler is then used to generate the random values. When implementing the random generation interface for a value X that can be sampled from, the implementor should define the method

  1. rand(rng, sampler)

for the particular sampler returned by Sampler(rng, X, repetition).

Samplers can be arbitrary values that implement rand(rng, sampler), but for most applications the following predefined samplers may be sufficient:

  1. SamplerType{T}() can be used for implementing samplers that draw from type T (e.g. rand(Int)). This is the default returned by Sampler for types.

  2. SamplerTrivial(self) is a simple wrapper for self, which can be accessed with []. This is the recommended sampler when no pre-computed information is needed (e.g. rand(1:3)), and is the default returned by Sampler for values.

  3. SamplerSimple(self, data) also contains the additional data field, which can be used to store arbitrary pre-computed values, which should be computed in a custom method of Sampler.

We provide examples for each of these. We assume here that the choice of algorithm is independent of the RNG, so we use AbstractRNG in our signatures.

Random.Sampler — Type

  1. Sampler(rng, x, repetition = Val(Inf))

Return a sampler object that can be used to generate random values from rng for x.

When sp = Sampler(rng, x, repetition), rand(rng, sp) will be used to draw random values, and should be defined accordingly.

repetition can be Val(1) or Val(Inf), and should be used as a suggestion for deciding the amount of precomputation, if applicable.

Random.SamplerType and Random.SamplerTrivial are default fallbacks for types and values, respectively. Random.SamplerSimple can be used to store pre-computed values without defining extra types for only this purpose.

Random.SamplerType — Type

  1. SamplerType{T}()

A sampler for types, containing no other information. The default fallback for Sampler when called with types.

Random.SamplerTrivial — Type

  1. SamplerTrivial(x)

Create a sampler that just wraps the given value x. This is the default fall-back for values. The eltype of this sampler is equal to eltype(x).

The recommended use case is sampling from values without precomputed data.

Random.SamplerSimple — Type

  1. SamplerSimple(x, data)

Create a sampler that wraps the given value x and the data. The eltype of this sampler is equal to eltype(x).

The recommended use case is sampling from values with precomputed data.

Decoupling pre-computation from actually generating the values is part of the API, and is also available to the user. As an example, assume that rand(rng, 1:20) has to be called repeatedly in a loop: the way to take advantage of this decoupling is as follows:

  1. rng = MersenneTwister()
  2. sp = Random.Sampler(rng, 1:20) # or Random.Sampler(MersenneTwister, 1:20)
  3. for x in X
  4.     n = rand(rng, sp) # similar to n = rand(rng, 1:20)
  5.     # use n
  6. end

This is the mechanism that is also used in the standard library, e.g. by the default implementation of random array generation (like in rand(1:20, 10)).

Generating values from a type

Given a type T, it’s currently assumed that if rand(T) is defined, an object of type T will be produced. SamplerType is the default sampler for types. In order to define random generation of values of type T, the rand(rng::AbstractRNG, ::Random.SamplerType{T}) method should be defined, and should return values what rand(rng, T) is expected to return.

Let’s take the following example: we implement a Die type, with a variable number n of sides, numbered from 1 to n. We want rand(Die) to produce a Die with a random number of up to 20 sides (and at least 4):

  1. struct Die
  2.     nsides::Int # number of sides
  3. end
  5. Random.rand(rng::AbstractRNG, ::Random.SamplerType{Die}) = Die(rand(rng, 4:20))
  7. # output

Scalar and array methods for Die now work as expected:

  1. julia> rand(Die)
  2. Die(7)
  4. julia> rand(MersenneTwister(0), Die)
  5. Die(11)
  7. julia> rand(Die, 3)
  8. 3-element Vector{Die}:
  9.  Die(13)
  10.  Die(8)
  11.  Die(20)
  13. julia> a = Vector{Die}(undef, 3); rand!(a)
  14. 3-element Vector{Die}:
  15.  Die(4)
  16.  Die(14)
  17.  Die(10)

A simple sampler without pre-computed data

Here we define a sampler for a collection. If no pre-computed data is required, it can be implemented with a SamplerTrivial sampler, which is in fact the default fallback for values.

In order to define random generation out of objects of type S, the following method should be defined: rand(rng::AbstractRNG, sp::Random.SamplerTrivial{S}). Here, sp simply wraps an object of type S, which can be accessed via sp[]. Continuing the Die example, we want now to define rand(d::Die) to produce an Int corresponding to one of d‘s sides:

  1. julia> Random.rand(rng::AbstractRNG, d::Random.SamplerTrivial{Die}) = rand(rng, 1:d[].nsides);
  3. julia> rand(Die(4))
  4. 1
  6. julia> rand(Die(4), 3)
  7. 3-element Vector{Any}:
  8.  3
  9.  2
  10.  4

Given a collection type S, it’s currently assumed that if rand(::S) is defined, an object of type eltype(S) will be produced. In the last example, a Vector{Any} is produced; the reason is that eltype(Die) == Any. The remedy is to define Base.eltype(::Type{Die}) = Int.

Generating values for an AbstractFloat type

AbstractFloat types are special-cased, because by default random values are not produced in the whole type domain, but rather in [0,1). The following method should be implemented for T <: AbstractFloat: Random.rand(::AbstractRNG, ::Random.SamplerTrivial{Random.CloseOpen01{T}})

An optimized sampler with pre-computed data

Consider a discrete distribution, where numbers 1:n are drawn with given probabilities that sum to one. When many values are needed from this distribution, the fastest method is using an alias table. We don’t provide the algorithm for building such a table here, but suppose it is available in make_alias_table(probabilities) instead, and draw_number(rng, alias_table) can be used to draw a random number from it.

Suppose that the distribution is described by

  1. struct DiscreteDistribution{V <: AbstractVector}
  2.     probabilities::V
  3. end

and that we always want to build an alias table, regardless of the number of values needed (we learn how to customize this below). The methods

  1. Random.eltype(::Type{<:DiscreteDistribution}) = Int
  3. function Random.Sampler(::Type{<:AbstractRNG}, distribution::DiscreteDistribution, ::Repetition)
  4.     SamplerSimple(disribution, make_alias_table(distribution.probabilities))
  5. end

should be defined to return a sampler with pre-computed data, then

  1. function rand(rng::AbstractRNG, sp::SamplerSimple{<:DiscreteDistribution})
  2.     draw_number(rng, sp.data)
  3. end

will be used to draw the values.

Custom sampler types

The SamplerSimple type is sufficient for most use cases with precomputed data. However, in order to demonstrate how to use custom sampler types, here we implement something similar to SamplerSimple.

Going back to our Die example: rand(::Die) uses random generation from a range, so there is an opportunity for this optimization. We call our custom sampler SamplerDie.

  1. import Random: Sampler, rand
  3. struct SamplerDie <: Sampler{Int} # generates values of type Int
  4.     die::Die
  5.     sp::Sampler{Int} # this is an abstract type, so this could be improved
  6. end
  8. Sampler(RNG::Type{<:AbstractRNG}, die::Die, r::Random.Repetition) =
  9.     SamplerDie(die, Sampler(RNG, 1:die.nsides, r))
  10. # the `r` parameter will be explained later on
  12. rand(rng::AbstractRNG, sp::SamplerDie) = rand(rng, sp.sp)

It’s now possible to get a sampler with sp = Sampler(rng, die), and use sp instead of die in any rand call involving rng. In the simplistic example above, die doesn’t need to be stored in SamplerDie but this is often the case in practice.

Of course, this pattern is so frequent that the helper type used above, namely Random.SamplerSimple, is available, saving us the definition of SamplerDie: we could have implemented our decoupling with:

  1. Sampler(RNG::Type{<:AbstractRNG}, die::Die, r::Random.Repetition) =
  2.     SamplerSimple(die, Sampler(RNG, 1:die.nsides, r))
  4. rand(rng::AbstractRNG, sp::SamplerSimple{Die}) = rand(rng, sp.data)

Here, sp.data refers to the second parameter in the call to the SamplerSimple constructor (in this case equal to Sampler(rng, 1:die.nsides, r)), while the Die object can be accessed via sp[].

Like SamplerDie, any custom sampler must be a subtype of Sampler{T} where T is the type of the generated values. Note that SamplerSimple(x, data) isa Sampler{eltype(x)}, so this constrains what the first argument to SamplerSimple can be (it’s recommended to use SamplerSimple like in the Die example, where x is simply forwarded while defining a Sampler method). Similarly, SamplerTrivial(x) isa Sampler{eltype(x)}.

Another helper type is currently available for other cases, Random.SamplerTag, but is considered as internal API, and can break at any time without proper deprecations.

Using distinct algorithms for scalar or array generation

In some cases, whether one wants to generate only a handful of values or a large number of values will have an impact on the choice of algorithm. This is handled with the third parameter of the Sampler constructor. Let’s assume we defined two helper types for Die, say SamplerDie1 which should be used to generate only few random values, and SamplerDieMany for many values. We can use those types as follows:

  1. Sampler(RNG::Type{<:AbstractRNG}, die::Die, ::Val{1}) = SamplerDie1(...)
  2. Sampler(RNG::Type{<:AbstractRNG}, die::Die, ::Val{Inf}) = SamplerDieMany(...)

Of course, rand must also be defined on those types (i.e. rand(::AbstractRNG, ::SamplerDie1) and rand(::AbstractRNG, ::SamplerDieMany)). Note that, as usual, SamplerTrivial and SamplerSimple can be used if custom types are not necessary.

Note: Sampler(rng, x) is simply a shorthand for Sampler(rng, x, Val(Inf)), and Random.Repetition is an alias for Union{Val{1}, Val{Inf}}.

Creating new generators

The API is not clearly defined yet, but as a rule of thumb:

  1. any rand method producing “basic” types (isbitstype integer and floating types in Base) should be defined for this specific RNG, if they are needed;
  2. other documented rand methods accepting an AbstractRNG should work out of the box, (provided the methods from 1) what are relied on are implemented), but can of course be specialized for this RNG if there is room for optimization;
  3. copy for pseudo-RNGs should return an independent copy that generates the exact same random sequence as the original from that point when called in the same way. When this is not feasible (e.g. hardware-based RNGs), copy must not be implemented.

Concerning 1), a rand method may happen to work automatically, but it’s not officially supported and may break without warnings in a subsequent release.

To define a new rand method for an hypothetical MyRNG generator, and a value specification s (e.g. s == Int, or s == 1:10) of type S==typeof(s) or S==Type{s} if s is a type, the same two methods as we saw before must be defined:

  1. Sampler(::Type{MyRNG}, ::S, ::Repetition), which returns an object of type say SamplerS
  2. rand(rng::MyRNG, sp::SamplerS)

It can happen that Sampler(rng::AbstractRNG, ::S, ::Repetition) is already defined in the Random module. It would then be possible to skip step 1) in practice (if one wants to specialize generation for this particular RNG type), but the corresponding SamplerS type is considered as internal detail, and may be changed without warning.

Specializing array generation

In some cases, for a given RNG type, generating an array of random values can be more efficient with a specialized method than by merely using the decoupling technique explained before. This is for example the case for MersenneTwister, which natively writes random values in an array.

To implement this specialization for MyRNG and for a specification s, producing elements of type S, the following method can be defined: rand!(rng::MyRNG, a::AbstractArray{S}, ::SamplerS), where SamplerS is the type of the sampler returned by Sampler(MyRNG, s, Val(Inf)). Instead of AbstractArray, it’s possible to implement the functionality only for a subtype, e.g. Array{S}. The non-mutating array method of rand will automatically call this specialization internally.

Reproducibility

By using an RNG parameter initialized with a given seed, you can reproduce the same pseudorandom number sequence when running your program multiple times. However, a minor release of Julia (e.g. 1.3 to 1.4) may change the sequence of pseudorandom numbers generated from a specific seed, in particular if MersenneTwister is used. (Even if the sequence produced by a low-level function like rand does not change, the output of higher-level functions like randsubseq may change due to algorithm updates.) Rationale: guaranteeing that pseudorandom streams never change prohibits many algorithmic improvements.

If you need to guarantee exact reproducibility of random data, it is advisable to simply save the data (e.g. as a supplementary attachment in a scientific publication). (You can also, of course, specify a particular Julia version and package manifest, especially if you require bit reproducibility.)

Software tests that rely on specific “random” data should also generally either save the data, embed it into the test code, or use third-party packages like StableRNGs.jl. On the other hand, tests that should pass for most random data (e.g. testing A \ (A*x) ≈ x for a random matrix A = randn(n,n)) can use an RNG with a fixed seed to ensure that simply running the test many times does not encounter a failure due to very improbable data (e.g. an extremely ill-conditioned matrix).

The statistical distribution from which random samples are drawn is guaranteed to be the same across any minor Julia releases.