start changing code for ::Real type

README.md

@@ -902,39 +902,6 @@ julia> quantile.(Levy(0, 1), U)
  0.582946    3.52799
 ```
 
-## Old dispatching, will be removed in future
-
-```julia
-julia> gausscopulagen(t::Int, Σ::Matrix{Float64})
-```
-
-```julia
-julia> archcopulagen(t::Int, n::Int, θ::Union{Float64, Int}, copula::String; rev::Bool = false, cor::String = "")
-```
-
-```julia
-julia> nestedarchcopulagen(t::Int, n::Vector{Int}, ϕ::Vector{Float64}, θ::Float64, copula::String, m::Int = 0)
-```
-
-```julia
-julia> chaincopulagen(t::Int, θ::Union{Vector{Float64}, Vector{Int}}, copula::Vector{String}; rev::Bool = false, cor::String = "")
-```
-
-```julia
-julia> marshallolkincopulagen(t::Int, λ::Vector{Float64})
-```
-
-```julia
-julia> frechetcopulagen(t::Int, n::Int, α::Union{Int, Float64})
-```
-
-```julia
-julia> frechetcopulagen(t::Int, n::Int, α::Union{Int, Float64}, β::Union{Int, Float64})
-```
-
-```julia
-julia> chainfrechetcopulagen(t::Int, α::Vector{Float64}, β::Vector{Float64} = zeros(α))
-```
 
 # Citing this work
 

src/DatagenCopulaBased.jl

@@ -15,7 +15,7 @@ module DatagenCopulaBased
   include("sampleunivdists.jl")
 
   # dispatching of the generator
-  include("copulagendat.jl")
+  #include("copulagendat.jl")
 
   # axiliary function for correlqations
   include("corgen.jl")
@@ -47,5 +47,5 @@ module DatagenCopulaBased
   export simulate_copula, simulate_copula!
 
   # obsolete implemntations
-  export tstudentcopulagen, gausscopulagen, frechetcopulagen, marshallolkincopulagen
+  #export tstudentcopulagen, gausscopulagen, frechetcopulagen, marshallolkincopulagen
 end

src/add_higher_order_cors.jl

@@ -36,7 +36,7 @@ julia> gcop2tstudent(x, [1,2], 6)
  -0.710786   0.239012   -1.54419
 ```
 """
-function gcop2tstudent(x::Matrix{Real}, ind::Vector{Int}, ν::Int; naive::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2tstudent(x::Matrix{T}, ind::Vector{Int}, ν::Int; naive::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   unique(ind) == ind || throw(AssertionError("indices must not repeat"))
   y = copy(x)
   Σ = cov(x)
@@ -97,7 +97,7 @@ julia> gcop2arch(x, ["clayton" => [1,2]]; naive::Bool = false, notnested::Bool =
  -0.657297  -0.339814  -1.54419
 ```
 """
-function gcop2arch(x::Matrix{Real}, inds::VP; naive::Bool = false, notnested::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2arch(x::Matrix{T}, inds::VP; naive::Bool = false, notnested::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   testind(inds)
   S = transpose(sqrt.(diag(cov(x))))
   μ = mean(x, dims=1)
@@ -154,7 +154,7 @@ julia> gcop2frechet(x, [1,2])
  -0.7223     -0.172507   -1.54419
 ```
 """
-function gcop2frechet(x::Matrix{Real}, inds::Vector{Int}; naive::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2frechet(x::Matrix{T}, inds::Vector{Int}; naive::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   unique(inds) == inds || throw(AssertionError("indices must not repeat"))
   S = transpose(sqrt.(diag(cov(x))))
   μ = mean(x, dims = 1)
@@ -202,7 +202,7 @@ julia> gcop2marshallolkin(x, [1,2], 1., 1.5; naive = false)
  -0.867606  -0.589929  -1.54419
 ```
 """
-function gcop2marshallolkin(x::Matrix{Real}, inds::Vector{Int}, λ1::Real = 1., λ2::Real = 1.5; naive::Bool = false, rng = Random.GLOBAL_RNG)
+function gcop2marshallolkin(x::Matrix{T}, inds::Vector{Int}, λ1::T = 1., λ2::T = 1.5; naive::Bool = false, rng = Random.GLOBAL_RNG) where T <: Real
   unique(inds) == inds || throw(AssertionError("indices must not repeat"))
   length(inds) == 2 || throw(AssertionError("not supported for |inds| > 2"))
   λ1 >= 0 || throw(DomainError("not supported for λ1 < 0"))
@@ -238,7 +238,7 @@ end
 
 Return uniformly distributed data from x[:,i] given a copula familly.
 """
-function norm2unifind(x::Matrix{Real}, i::Vector{Int}, cop::String = "")
+function norm2unifind(x::Matrix{T}, i::Vector{Int}, cop::String = "") where T <: Real
   x = (cop == "frechet") ? x[:,i] : hcat(x[:,i], randn(size(x,1),1))
   a, s = eigen(cor(x))
   w = x*s./transpose(sqrt.(a))
@@ -259,14 +259,14 @@ julia> meanΣ(s)
 0.3
 ```
 """
-meanΣ(Σ::Matrix{Real}) = mean(abs.(Σ[findall(tril(Σ-Matrix(I, size(Σ))).!=0)]))
+meanΣ(Σ::Matrix{T}) where T <: Real = mean(abs.(Σ[findall(tril(Σ-Matrix(I, size(Σ))).!=0)]))
 
 """
   mean_outer(Σ::Matrix{Real}, part::Vector{Vector{Int}})
 
 returns a mean correlation excluding internal one is subsets determined by part
 """
-function mean_outer(Σ::Matrix{Real}, part::Vector{Vector{Int}})
+function mean_outer(Σ::Matrix{T}, part::Vector{Vector{Int}}) where T <: Real
   Σ_copy = copy(Σ)-Matrix(I, size(Σ))
   for ind=part
     Σ_copy[ind,ind] = zeros(length(ind),length(ind))
@@ -280,7 +280,7 @@ end
 Returns parametrization by correlation for data `x` and partition `part` for nested copulas.
 
 """
-function parameters(Σ::Matrix{Real}, part::Vector{Vector{Int}})
+function parameters(Σ::Matrix{T}, part::Vector{Vector{Int}}) where T <: Real
   ϕ = [meanΣ(Σ[ind,ind]) for ind=part]
   θ = mean_outer(Σ, part)
   ϕ, θ
@@ -291,7 +291,7 @@ end
 
 tests sufficient nesting condition given parameters, returns bool
 """
-function are_parameters_good(ϕ::Vector{Real}, θ::Real)
+function are_parameters_good(ϕ::Vector{T}, θ::T) where T <: Real
   θ < minimum(filter(x->!isnan(x), ϕ))
 end
 
@@ -301,7 +301,7 @@ end
 returns a matrix indicating a theoretical correlation according togiven parameters
 and partition
 """
-function Σ_theor(ϕ::Vector{Real}, θ::Real, part::Vector{Vector{Int}})
+function Σ_theor(ϕ::Vector{T}, θ::T, part::Vector{Vector{Int}}) where T <: Real
   n = sum(length.(part))
   result = fill(θ, n, n)
   for (ϕind,ind)=zip(ϕ,part)
@@ -318,7 +318,7 @@ end
 
 clusters data on a basis of a correlation
 """
-function getcors_advanced(x::Matrix{Real})
+function getcors_advanced(x::Matrix{T}) where T <: Real
   Σ = corspearman(x)
   var_no = size(Σ, 1)
   partitions = collect(Combinatorics.SetPartitions(1:var_no))[2:end-1] #TODO popraw first is trivial, last is nested

src/archcopcorrelations.jl

@@ -19,7 +19,7 @@ Debye(x::Real, k::Int=1) = k/x^k*(quadgk(i -> i^k/(exp(i)-1), 0, x)[1])
 
 Returns Float, a single parameter of Archimedean copula, given the Kenalss τ correlation
 """
-function τ2θ(τ::Real, copula::String)
+function τ2θ(τ::T, copula::String) where T <: Real
   if copula == "gumbel"
     return 1/(1-τ)
   elseif copula == "clayton"

src/archcopulagendat.jl

@@ -68,7 +68,9 @@ Given a vector of random numbers r of size n+1, return the sample from the Clayt
 copula parametrised by θ of the size n.
 """
 function clayton_gen(r::Vector{T}, θ::T) where T <: Real
-    u = -log.(r[1:end-1])./quantile.(Gamma{T}(1/θ, 1), r[end])
+    gamma_vec = quantile.(Gamma{T}(1/θ, 1), r[end])
+    #gamma_vec = [gamma_inc_inv(1/θ, T(1.), q) for q in r[end]]
+    u = -log.(r[1:end-1])./gamma_vec
     return (1 .+ u).^(-1/θ)
 end
 

src/chaincopulagendat.jl

@@ -105,7 +105,7 @@ struct Chain_of_Archimedeans{T}
       copula in ["clayton", "amh", "frank"] || throw(AssertionError("$(copula) copula is not supported"))
       new{T}(n, θ, [copula for i in 1:n-1])
   end
-  function(::Type{Chain_of_Archimedeans})(θ::Vector{Real}, copula::String, cor::Type{<:CorrelationType}) where T <: Real
+  function(::Type{Chain_of_Archimedeans})(θ::Vector{T}, copula::String, cor::Type{<:CorrelationType}) where T <: Real
       n = length(θ)+1
       map(i -> testbivθ(θ[i], copula), 1:n-1)
       copula in ["clayton", "amh", "frank"] || throw(AssertionError("$(copula) copula is not supported"))
@@ -321,7 +321,7 @@ function simulate_copula(t::Int, copula::Chain_of_Frechet{T}; rng::AbstractRNG =
   α = copula.α
   β = copula.β
   n = copula.n
-  fncopulagen(α, β, rand(rng, t, n))
+  fncopulagen(α, β, rand(rng, T, t, n))
 end
 
 """

src/eliptic_fr_mo_copulas.jl

@@ -56,7 +56,7 @@ julia> simulate_copula(10, Gaussian_cop([1. 0.5; 0.5 1.]))
  0.190283  0.594451
 ```
 """
-function simulate_copula(t::Int, copula::Gaussian_cop; rng::AbstractRNG = Random.GLOBAL_RNG)
+function simulate_copula(t::Int, copula::Gaussian_cop{T}; rng::AbstractRNG = Random.GLOBAL_RNG) where T <: Real
   Σ = copula.Σ
   z = transpose(rand(rng, MvNormal(Σ),t))
   for i in 1:size(Σ, 1)
@@ -254,7 +254,7 @@ function simulate_copula!(U::Matrix{T}, copula::Frechet_cop{T}; rng::AbstractRNG
   if (β > 0) & (n == 2)
     for j in 1:size(U,1)
       u_el = rand(rng, T, n)
-      frechet_el2!(u_el, α, β, rand(rng))
+      frechet_el2!(u_el, α, β, rand(rng, T))
       U[j,:] = u_el
     end
   else

src/nestedarchcopulagendat.jl

@@ -349,7 +349,7 @@ julia> simulate_copula(4, cp)
 function simulate_copula(t::Int, copula::Nested_AMH_cop{T}; rng::AbstractRNG = Random.GLOBAL_RNG) where T <: Real
      n = [ch.n for ch in copula.children]
      n2 = sum(n)+copula.m
-     U = zeros(t, n2)
+     U = zeros(T, t, n2)
      simulate_copula!(U, copula; rng = rng)
      return U
 end
@@ -728,7 +728,7 @@ function simulate_copula!(U::Matrix{T}, copula::Double_Nested_Gumbel_cop{T}; rng
     size(U, 2) == dims || throw(AssertionError("n.o. margins in pre allocated output and copula not equal"))
 
     for j in 1:size(U,1)
-        X = Real[]
+        X = T[]
         for k in 1:length(v)
             n = ns[k]
             n1 = vcat([collect(1:n[1])], [collect(cumsum(n)[i]+1:cumsum(n)[i+1]) for i in 1:length(n)-1])
@@ -810,8 +810,8 @@ julia> simulate_copula(3, c)
  0.73617   0.347349  0.168348   0.410963
 ```
 """
-function simulate_copula(t::Int, copula::Hierarchical_Gumbel_cop; rng::AbstractRNG = Random.GLOBAL_RNG)
-    U = zeros(t, copula.n)
+function simulate_copula(t::Int, copula::Hierarchical_Gumbel_cop{T}; rng::AbstractRNG = Random.GLOBAL_RNG) where T <: Real
+    U = zeros(T, t, copula.n)
     simulate_copula!(U, copula; rng = rng)
     return U
 end