fix: type parameters

Author: ymtoo

src/AlphaStableDistributions.jl

@@ -188,7 +188,7 @@ skewness parameter, scale parameter (dispersion^1/α) and location parameter res
 
 α, β, c and δ are computed based on McCulloch (1986) fractile.
 """
-function Distributions.fit(::Type{<:AlphaStable}, x, alg=QuickSort)
+function Distributions.fit(::Type{<:AlphaStable}, x::AbstractArray{T}, alg=QuickSort) where {T}
     sx = sort(x, alg=alg)
     p = quantile.(Ref(sx), (0.05, 0.25, 0.28, 0.5, 0.72, 0.75, 0.95), sorted=true)
     να = (p[7]-p[1]) / (p[6]-p[2])
@@ -212,7 +212,7 @@ function Distributions.fit(::Type{<:AlphaStable}, x, alg=QuickSort)
     else
         δ = ζ - β * c * tan(π*α/2)
     end
-    return AlphaStable(α=α, β=β, scale=c, location=oftype(α, δ))
+    return AlphaStable(α=T(α), β=T(β), scale=T(c), location=T(δ))
 end
 
 Base.@kwdef struct SymmetricAlphaStable{T} <: Distributions.ContinuousUnivariateDistribution
@@ -235,12 +235,12 @@ returns `SymmetricAlphaStable`
 scale is computed based on Fama & Roll (1971) fractile.
 location is the 50% trimmed mean of the sample.
 """
-function Distributions.fit(::Type{<:SymmetricAlphaStable}, x, alg=QuickSort)
+function Distributions.fit(::Type{<:SymmetricAlphaStable}, x::AbstractArray{T}, alg=QuickSort) where {T}
     sx = sort(x, alg=alg)
     δ = mean(@view(sx[end÷4:(3*end)÷4]))
     p = quantile.(Ref(sx), (0.05, 0.25, 0.28, 0.72, 0.75, 0.95), sorted=true)
-    c = (p[4]-p[3])/1.654
-    an = (p[6]-p[1])/(p[5]-p[2])
+    c = (p[4]-p[3]) / 1.654
+    an = (p[6]-p[1]) / (p[5]-p[2])
     if an < 2.4388
         α = 2.
     else
@@ -253,7 +253,7 @@ function Distributions.fit(::Type{<:SymmetricAlphaStable}, x, alg=QuickSort)
     if α < 0.5
         α = 0.5
     end
-    return SymmetricAlphaStable(α=α, scale=c, location=oftype(α, δ))
+    return SymmetricAlphaStable(α=T(α), scale=T(c), location=T(δ))
 end
 
 """
@@ -322,16 +322,12 @@ The maximum acceptable size of `R` is `10x10`
 julia> x = rand(AlphaSubGaussian(n=1000))
 ```
 """
-Base.@kwdef struct AlphaSubGaussian{T<:AbstractFloat} <: Distributions.ContinuousUnivariateDistribution
+Base.@kwdef struct AlphaSubGaussian{T<:AbstractFloat,M<:AbstractMatrix} <: Distributions.ContinuousUnivariateDistribution
     α::T = 1.50
-    R::AbstractMatrix{T} = SMatrix{5,5}(collect(SymmetricToeplitz([1.0000, 0.5804, 0.2140, 0.1444, -0.0135])))
+    R::M = SMatrix{5,5}(collect(SymmetricToeplitz([1.0000, 0.5804, 0.2140, 0.1444, -0.0135])))
     n::Int
 end
 
-AlphaSubGaussian(α::T, n::Int) where {T<:AbstractFloat} = AlphaSubGaussian(α=α, 
-                                                                           R=SMatrix{5,5}(T.(collect(SymmetricToeplitz([1.0000, 0.5804, 0.2140, 0.1444, -0.0135])))),
-                                                                           n=n)
-
 """
 Generates the conditional probability f(X2|X1) if [X1, X2] is a sub-Gaussian
 stable random vector such that X1(i)~X2~S(alpha,delta) and rho is the correlation
@@ -377,7 +373,7 @@ end
 function Random.rand!(rng::AbstractRNG, d::AlphaSubGaussian{T}, x::AbstractArray{T}) where {T<:Real}
     α=d.α; R=d.R; n=d.n
     length(x) >= n || throw(ArgumentError("length of x must be at least n"))
-    α ∈ T.(1.10:0.01:1.98) || throw(DomainError(α, "α must lie within `1.10:0.01:1.98`"))
+    α ∈ 1.10:0.01:1.98 || throw(DomainError(α, "α must lie within `1.10:0.01:1.98`"))
     m = size(R, 1)-1
     funk1 = x -> (2^α)*sin(π*α/2)*gamma((α+2)/2)*gamma((α+x)/2)/(gamma(x/2)*π*α/2)
     funk2 = x -> 4*gamma(x/α)/((α*2^2)*gamma(x/2)^2)
@@ -430,7 +426,7 @@ end
 
 
 Base.rand(rng::AbstractRNG, d::AlphaSubGaussian) = rand!(rng, d, zeros(eltype(d), d.n))
-Base.eltype(::Type{<:AlphaSubGaussian{T}}) where {T} = T
+Base.eltype(::Type{<:AlphaSubGaussian}) = Float64
 
 """
     fit(d::Type{<:AlphaSubGaussian}, x, m; p=1.0)

test/runtests.jl

@@ -10,10 +10,11 @@ using Test, Random, Distributions
         for (i, stabletype) in enumerate(stabletypes)
             for α in αs[i]
                 d1 = AlphaStable(α=sampletype(α))
-                s = rand(d1, 100000)
+                s = rand(d1, 200000)
                 @test eltype(s) == sampletype
 
                 d2 = fit(stabletype, s)
+                @test typeof(d2.α) == sampletype 
 
                 @test d1.α ≈ d2.α rtol=0.1
                 stabletype != SymmetricAlphaStable && @test d1.β ≈ d2.β atol=0.2
@@ -35,32 +36,31 @@ using Test, Random, Distributions
             @test d.scale ≈ 4 rtol=0.2
             @test d.location ≈ 3 rtol=0.1
         end
+    end
 
-        for α in 1.1:0.1:1.9
-            d = AlphaSubGaussian(sampletype(α), 96000)
-            x = rand(d)
-            @test eltype(x) == sampletype
-            x2 = copy(x)
-            rand!(d, x2)
-            @test x != x2
+    for α in 1.1:0.1:1.9
+        d = AlphaSubGaussian(α=α, n=96000)
+        x = rand(d)
+        x2 = copy(x)
+        rand!(d, x2)
+        @test x != x2
 
-            d3 = fit(AlphaStable, x)
-            @test d3.α ≈ α rtol=0.2
-            @test d3.β ≈ 0 atol=0.2
-            @test d3.scale ≈ 1 rtol=0.2
-            @test d3.location ≈ 0 atol=0.1
-        end
+        d3 = fit(AlphaStable, x)
+        @test d3.α ≈ α rtol=0.2
+        @test d3.β ≈ 0 atol=0.2
+        @test d3.scale ≈ 1 rtol=0.2
+        @test d3.location ≈ 0 atol=0.1
+    end
 
-        d4 = AlphaSubGaussian(sampletype(1.5), 96000)
-        m = size(d4.R, 1) - 1
-        x = rand(d4)
-        @test eltype(x) == sampletype
-        d5 = fit(AlphaSubGaussian, x, m, p=1.0)
-        @test d4.α ≈ d5.α rtol=0.1
-        @test d4.R ≈ d5.R rtol=0.1
+    d4 = AlphaSubGaussian(α=1.5, n=96000)
+    m = size(d4.R, 1) - 1
+    x = rand(d4)
+    d5 = fit(AlphaSubGaussian, x, m, p=1.0)
+    @test d4.α ≈ d5.α rtol=0.1
+    @test d4.R ≈ d5.R rtol=0.1
 
-    end
 end
+
 # 362.499 ms (4620903 allocations: 227.64 MiB)
 # 346.520 ms (4621052 allocations: 209.62 MiB) # StaticArrays in outer fun
 # 345.925 ms (4225524 allocations: 167.66 MiB) # tempind to tuple