Julia> rand(Xoshiro(), Bool) # not reproducible either Julia> rand(ed!(rng), Bool) # not reproducible either Julia> rand(ed!(rng), Bool) # not reproducible Julia> rng = Xoshiro(1234) rand(rng, 2) = x1 If rng is not specified, it defaults to seeding the state of the shared task-local generator. After the call to seed!, rng is equivalent to a newly created object initialized with the same seed. Some RNGs don't accept a seed, like RandomDevice. Reseed the random number generator: rng will give a reproducible sequence of numbers if and only if a seed is provided. To generate random numbers from other distributions, see the Distributions.jl package. rand(big.(1:6))).Īdditionally, normal and exponential distributions are implemented for some AbstractFloat and Complex types, see randn and randexp for details. As BigInt represents unbounded integers, the interval must be specified (e.g. Random floating point numbers are generated uniformly in $[0, 1)$. 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). 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). In a multi-threaded program, you should generally use different RNG objects from different threads or tasks in order to be thread-safe. (which can also be given as a tuple) to generate arrays of random values. Some also accept dimension specifications dims. Most functions related to random generation accept an optional AbstractRNG object as first argument.
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