Specialized vector rand! for many distributions

src/univariate/continuous/exponential.jl

@@ -107,6 +107,11 @@ cf(d::Exponential, t::Real) = 1/(1 - t * im * scale(d))
 #### Sampling
 rand(rng::AbstractRNG, d::Exponential{T}) where {T} = xval(d, randexp(rng, float(T)))
 
+function rand!(rng::AbstractRNG, d::Exponential, A::AbstractArray{<:Real})
+    randexp!(rng, A)
+    map!(Base.Fix1(xval, d), A, A)
+    return A
+end
 
 #### Fit model
 

src/univariate/continuous/logitnormal.jl

@@ -157,7 +157,14 @@ end
 
 #### Sampling
 
-rand(rng::AbstractRNG, d::LogitNormal) = logistic(randn(rng) * d.σ + d.μ)
+xval(d::LogitNormal, z::Real) = logistic(muladd(d.σ, z, d.μ))
+
+rand(rng::AbstractRNG, d::LogitNormal) = xval(d, randn(rng))
+function rand!(rng::AbstractRNG, d::LogitNormal, A::AbstractArray{<:Real})
+    randn!(rng, A)
+    map!(Base.Fix1(xval, d), A, A)
+    return A
+end
 
 ## Fitting
 

src/univariate/continuous/lognormal.jl

@@ -156,7 +156,14 @@ end
 
 #### Sampling
 
-rand(rng::AbstractRNG, d::LogNormal) = exp(randn(rng) * d.σ + d.μ)
+xval(d::LogNormal, z::Real) = exp(muladd(d.σ, z, d.μ))
+
+rand(rng::AbstractRNG, d::LogNormal) = xval(d,  randn(rng))
+function rand!(rng::AbstractRNG, d::LogNormal, A::AbstractArray{<:Real})
+    randn!(rng, A)
+    map!(Base.Fix1(xval, d), A, A)
+    return A
+end
 
 ## Fitting
 

src/univariate/continuous/normal.jl

@@ -114,9 +114,14 @@ Base.:*(c::Real, d::Normal) = Normal(c * d.μ, abs(c) * d.σ)
 
 #### Sampling
 
-rand(rng::AbstractRNG, d::Normal{T}) where {T} = d.μ + d.σ * randn(rng, float(T))
+xval(d::Normal, z::Real) = muladd(d.σ, z, d.μ)
 
-rand!(rng::AbstractRNG, d::Normal, A::AbstractArray{<:Real}) = A .= muladd.(d.σ, randn!(rng, A), d.μ)
+rand(rng::AbstractRNG, d::Normal{T}) where {T} = xval(d, randn(rng, float(T)))
+function rand!(rng::AbstractRNG, d::Normal, A::AbstractArray{<:Real})
+    randn!(rng, A)
+    map!(Base.Fix1(xval, d), A, A)
+    return A
+end
 
 #### Fitting
 

src/univariate/continuous/normalcanon.jl

@@ -87,7 +87,13 @@ invlogccdf(d::NormalCanon, lp::Real) = xval(d, norminvlogccdf(lp))
 
 #### Sampling
 
-rand(rng::AbstractRNG, cf::NormalCanon) = cf.μ + randn(rng) / sqrt(cf.λ)
+rand(rng::AbstractRNG, cf::NormalCanon) = xval(cf, randn(rng))
+
+function rand!(rng::AbstractRNG, cf::NormalCanon, A::AbstractArray{<:Real})
+    randn!(rng, A)
+    map!(Base.Fix1(xval, cf), A, A)
+    return A
+end
 
 #### Affine transformations
 

src/univariate/continuous/pareto.jl

@@ -110,7 +110,14 @@ quantile(d::Pareto, p::Real) = cquantile(d, 1 - p)
 
 #### Sampling
 
-rand(rng::AbstractRNG, d::Pareto) = d.θ * exp(randexp(rng) / d.α)
+xval(d::Pareto, z::Real) = d.θ * exp(z / d.α)
+
+rand(rng::AbstractRNG, d::Pareto) = xval(d, randexp(rng))
+function rand!(rng::AbstractRNG, d::Pareto, A::AbstractArray{<:Real})
+    randexp!(rng, A)
+    map!(Base.Fix1(xval, d), A, A)
+    return A
+end
 
 ## Fitting
 

src/univariate/continuous/pgeneralizedgaussian.jl

@@ -141,9 +141,9 @@ function rand(rng::AbstractRNG, d::PGeneralizedGaussian)
     inv_p = inv(d.p)
     g = Gamma(inv_p, 1)
     z = d.α * rand(rng, g)^inv_p
-    if rand(rng) < 0.5
+    if rand(rng, Bool)
         return d.μ - z
     else
         return d.μ + z
     end
-end
+end

test/fit.jl

@@ -369,7 +369,7 @@ end
     for func in funcs, dist in (Laplace, Laplace{Float64})
         d = fit(dist, func[2](dist(5.0, 3.0), N + 1))
         @test isa(d, dist)
-        @test isapprox(location(d), 5.0, atol=0.02)
+        @test isapprox(location(d), 5.0, atol=0.021)
         @test isapprox(scale(d)   , 3.0, atol=0.03)
     end
 end

test/multivariate/mvlognormal.jl

@@ -105,8 +105,8 @@ end
     @test  entropy(l1)   ≈ entropy(l2)
     @test logpdf(l1,5.0) ≈ logpdf(l2,[5.0])
     @test pdf(l1,5.0)    ≈ pdf(l2,[5.0])
-    @test (Random.seed!(78393) ; [rand(l1)]) == (Random.seed!(78393) ; rand(l2))
-    @test [rand(MersenneTwister(78393), l1)] == rand(MersenneTwister(78393), l2)
+    @test (Random.seed!(78393) ; [rand(l1)]) ≈ (Random.seed!(78393) ; rand(l2))
+    @test [rand(MersenneTwister(78393), l1)] ≈ rand(MersenneTwister(78393), l2)
 end
 
 ###### General Testing

test/runtests.jl

@@ -22,6 +22,7 @@ const tests = [
     "truncated/discrete_uniform",
     "censored",
     "univariate/continuous/normal",
+    "univariate/continuous/normalcanon",
     "univariate/continuous/laplace",
     "univariate/continuous/cauchy",
     "univariate/continuous/uniform",
@@ -83,6 +84,7 @@ const tests = [
     "univariate/continuous/noncentralchisq",
     "univariate/continuous/weibull",
     "pdfnorm",
+    "univariate/continuous/pareto",
     "univariate/continuous/rician",
     "functionals",
     "density_interface",
@@ -143,9 +145,7 @@ const tests = [
     # "univariate/continuous/levy",
     # "univariate/continuous/noncentralbeta",
     # "univariate/continuous/noncentralf",
-    # "univariate/continuous/normalcanon",
     # "univariate/continuous/normalinversegaussian",
-    # "univariate/continuous/pareto",
     # "univariate/continuous/rayleigh",
     # "univariate/continuous/studentizedrange",
     # "univariate/continuous/symtriangular",

test/testutils.jl

@@ -28,8 +28,7 @@ end
 # testing the implementation of a discrete univariate distribution
 #
 function test_distr(distr::DiscreteUnivariateDistribution, n::Int;
-                    testquan::Bool=true)
-
+                    testquan::Bool=true, rng::AbstractRNG=MersenneTwister(123))
     test_range(distr)
     vs = get_evalsamples(distr, 0.00001)
 
@@ -40,7 +39,8 @@ function test_distr(distr::DiscreteUnivariateDistribution, n::Int;
 
     test_stats(distr, vs)
     test_samples(distr, n)
-    test_samples(distr, n, rng=MersenneTwister())
+    test_samples(distr, n, rng=rng)
+
     test_params(distr)
 end
 
@@ -82,6 +82,7 @@ function test_distr(distr::ContinuousUnivariateDistribution, n::Int;
     allow_test_stats(distr) && test_stats(distr, xs)
     xs = test_samples(distr, n, rng=rng)
     allow_test_stats(distr) && test_stats(distr, xs)
+
     test_params(distr)
 end
 
@@ -102,7 +103,6 @@ function test_samples(s::Sampleable{Univariate, Discrete},      # the sampleable
                       q::Float64=1.0e-7,                        # confidence interval, 1 - q as confidence
                       verbose::Bool=false,                      # show intermediate info (for debugging)
                       rng::Union{AbstractRNG, Missing}=missing) # add an rng?
-
     # The basic idea
     # ------------------
     #   Generate n samples, and count the occurrences of each value within a reasonable range.
@@ -150,31 +150,53 @@ function test_samples(s::Sampleable{Univariate, Discrete},      # the sampleable
         samples = rand(s, n)
         Random.seed!(1234)
         samples2 = rand(s, n)
+        Random.seed!(1234)
+        samples3 = map((_) -> rand(s), 1:n)
+        Random.seed!(1234)
+        samples4 = map((_) -> rand(s), 1:n)
     else
         rng2 = deepcopy(rng)
+        rng3 = deepcopy(rng)
+        rng4 = deepcopy(rng)
         samples = rand(rng, s, n)
         samples2 = rand(rng2, s, n)
+        samples3 = map((_) -> rand(rng3, s), 1:n)
+        samples4 = map((_) -> rand(rng4, s), 1:n)
     end
     @test length(samples) == n
     @test samples2 == samples
+    @test samples3 == samples4
 
     # scan samples and get counts
     cnts = zeros(Int, m)
+    cnts_sc = zeros(Int, m)
     for i = 1:n
         @inbounds si = samples[i]
         if rmin <= si <= rmax
             cnts[si - rmin + 1] += 1
         else
             vmin <= si <= vmax ||
-                error("Sample value out of valid range.")
+                error("Sample value out of valid range. (Vector Method)")
+        end
+
+        @inbounds si_sc = samples3[i]
+        if rmin <= si_sc <= rmax
+            cnts_sc[si_sc - rmin + 1] += 1
+        else
+            vmin <= si_sc <= vmax ||
+                error("Sample value out of valid range. (Scalar Method)")
         end
     end
 
     # check the counts
     for i = 1:m
         verbose && println("v = $(rmin+i-1) ==> ($(clb[i]), $(cub[i])): $(cnts[i])")
         clb[i] <= cnts[i] <= cub[i] ||
-            error("The counts are out of the confidence interval.")
+            error("The counts are out of the confidence interval. (Vector Method)")
+
+        verbose && println("v = $(rmin+i-1) ==> ($(clb[i]), $(cub[i])): $(cnts_sc[i])")
+        clb[i] <= cnts_sc[i] <= cub[i] ||
+            error("The counts are out of the confidence interval. (Scalar Method)")
     end
     return samples
 end
@@ -250,13 +272,22 @@ function test_samples(s::Sampleable{Univariate, Continuous},    # the sampleable
         samples = rand(s, n)
         Random.seed!(1234)
         samples2 = rand(s, n)
+        Random.seed!(1234)
+        samples3 = map((_) -> rand(s), 1:n)
+        Random.seed!(1234)
+        samples4 = map((_) -> rand(s), 1:n)
     else
         rng2 = deepcopy(rng)
+        rng3 = deepcopy(rng)
+        rng4 = deepcopy(rng)
         samples = rand(rng, s, n)
         samples2 = rand(rng2, s, n)
+        samples3 = map((_) -> rand(rng3, s), 1:n)
+        samples4 = map((_) -> rand(rng4, s), 1:n)
     end
     @test length(samples) == n
     @test samples2 == samples
+    @test samples3 == samples4
 
     if isa(distr, StudentizedRange)
         samples[isnan.(samples)] .= 0.0 # Underlying implementation in Rmath can't handle very low values.
@@ -266,20 +297,29 @@ function test_samples(s::Sampleable{Univariate, Continuous},    # the sampleable
     for i = 1:n
         @inbounds si = samples[i]
         vmin <= si <= vmax ||
-            error("Sample value out of valid range.")
+            error("Sample value out of valid range. (Vector Method)")
+        @inbounds si_sc = samples3[i]
+        vmin <= si_sc <= vmax ||
+            error("Sample value out of valid range. (Scalar Method)")
     end
 
     # get counts
     cnts = fit(Histogram, samples, edges; closed=:right).weights
     @assert length(cnts) == nbins
 
+    cnts_sc = fit(Histogram, samples3, edges; closed=:right).weights
+    @assert length(cnts_sc) == nbins
+
     # check the counts
     for i = 1:nbins
         if verbose
             @printf("[%.4f, %.4f) ==> (%d, %d): %d\n", edges[i], edges[i+1], clb[i], cub[i], cnts[i])
+            @printf("[%.4f, %.4f) ==> (%d, %d): %d\n", edges[i], edges[i+1], clb[i], cub[i], cnts_sc[i])
         end
         clb[i] <= cnts[i] <= cub[i] ||
-            error("The counts are out of the confidence interval.")
+            error("The counts are out of the confidence interval. (Vector Method)")
+        clb[i] <= cnts_sc[i] <= cub[i] ||
+            error("The counts are out of the confidence interval. (Scalar Method)")
     end
     return samples
 end
@@ -583,6 +623,7 @@ end
 allow_test_stats(d::UnivariateDistribution) = true
 allow_test_stats(d::NoncentralBeta) = false
 allow_test_stats(::StudentizedRange) = false
+allow_test_stats(::LogitNormal) = false
 
 function test_stats(d::ContinuousUnivariateDistribution, xs::AbstractVector{Float64})
     # using Monte Carlo methods

test/univariate/continuous/exponential.jl

@@ -1,4 +1,3 @@
-
 @testset "Exponential" begin
     test_cgf(Exponential(1), (0.9, -1, -100f0, -1e6))
     test_cgf(Exponential(0.91), (0.9, -1, -100f0, -1e6))
@@ -8,3 +7,18 @@
         @test @inferred(rand(Exponential(T(1)))) isa T
     end
 end
+
+test_cgf(Exponential(1), (0.9, -1, -100f0, -1e6))
+test_cgf(Exponential(0.91), (0.9, -1, -100f0, -1e6))
+test_cgf(Exponential(10  ), (0.08, -1, -100f0, -1e6))
+
+# Sampling Tests
+@testset "Exponential sampling tests" begin
+    for d in [
+        Exponential(1),
+        Exponential(0.91),
+        Exponential(10)
+    ]
+        test_distr(d, 10^6)
+    end
+end

test/univariate/continuous/logitnormal.jl

@@ -64,3 +64,12 @@ end
     @test convert(LogitNormal{Float32}, d) === d
     @test typeof(convert(LogitNormal{Float64}, d)) == typeof(LogitNormal(2,1))
 end
+
+@testset "Logitnormal Sampling Tests" begin
+    for d in [
+        LogitNormal(-2, 3),
+        LogitNormal(0, 0.2)
+    ]
+        test_distr(d, 10^6)
+    end
+end

test/univariate/continuous/lognormal.jl

@@ -314,3 +314,17 @@ end
     @test @inferred(gradlogpdf(LogNormal(0.0, 1.0), BigFloat(-1))) == big(0.0)
     @test isnan_type(BigFloat, @inferred(gradlogpdf(LogNormal(0.0, 1.0), BigFloat(NaN))))
 end
+
+@testset "LogNormal Sampling Tests" begin
+    for d in [
+            LogNormal()
+            LogNormal(1.0)
+            LogNormal(0.0, 2.0)
+            LogNormal(1.0, 2.0)
+            LogNormal(3.0, 0.5)
+            LogNormal(3.0, 1.0)
+            LogNormal(3.0, 2.0)
+        ]
+        test_distr(d, 10^6)
+    end
+end

test/univariate/continuous/normalcanon.jl

@@ -0,0 +1,10 @@
+# Sampling Tests
+@testset "NormalCanon sampling tests" begin
+    for d in [
+        NormalCanon()
+        NormalCanon(-1.0, 2.5)
+        NormalCanon(2.0, 0.8)
+    ]
+        test_distr(d, 10^6)
+    end
+end

test/univariate/continuous/pareto.jl

@@ -0,0 +1,10 @@
+@testset "Pareto Sampling Tests" begin
+    for d in [
+            Pareto()
+            Pareto(2.0)
+            Pareto(2.0, 1.5)
+            Pareto(3.0, 2.0)
+        ]
+        test_distr(d, 10^6)
+    end
+end