This package provides bindings to the Intel Vector Statistics Library.
]add VSLJulia v1.7+ is required to install VSL.jl. and MKL_jll.jl
will be downloaded automatically.
VSL.jl provides several basic random number generators (BRNGs) and distributions, and each distribution has at least
one method to generate random number. After VSL.jl loaded, you can use the distributions such like the followings:
julia> using VSL, Random
julia> brng = BasicRandomNumberGenerator(VSL_BRNG_MT19937, 12345);
# A BRNG created, in which 12345 is the random seed.
julia> u = Uniform(brng, 0.0, 1.0); # Create a uniform distribution between 0.0 and 1.0.
julia> rand(u) # Generate one random number.
0.41661986871622503
julia> rand(u, 2, 3) # Generate an random 2*3 array.
2×3 Array{Float64,2}:
0.732685 0.820175 0.802848
0.0101692 0.825207 0.29864
julia> A = Array{Float64}(undef, 3, 4);
julia> rand!(u, A) # Fill an array with random numbers.
3×4 Array{Float64,2}:
0.855138 0.193661 0.436228 0.124267
0.368412 0.270245 0.161688 0.874174
0.931785 0.566008 0.373064 0.432936Use the Enum BRNGType to set the type of BRNG.
| BRNGType Enum |
|---|
VSL_BRNG_MCG31 |
VSL_BRNG_R250 |
VSL_BRNG_MRG32K3A |
VSL_BRNG_MCG59 |
VSL_BRNG_WH |
VSL_BRNG_SOBOL |
VSL_BRNG_NIEDERR |
VSL_BRNG_MT19937 |
VSL_BRNG_MT2203 |
VSL_BRNG_SFMT19937 |
VSL_BRNG_NONDETERM |
VSL_BRNG_ARS5 |
VSL_BRNG_PHILOX4X32X10 |
Contigurous: Uniform, Gaussian, GaussianMV, Exponential, Laplace,
Weibull, Cauchy, Rayleigh, Lognormal, Gumbel, Gamma, Beta
Discrete: UniformDiscrete, UniformBits, UniformBits32, UniformBits64, Bernoulli,
Geometric, Binomial, Hypergeometric, Poisson, PoissonV, NegBinomial
Most of the discrete distributions return values of 32-bit integer. Please be careful when using those distributions.
For more information, please refer to the Intel® Math Kernel Library Developer Reference