Merge branch 'master' into dw/mixture_inference

Author: devmotion

Project.toml

@@ -30,11 +30,10 @@ Calculus = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["StableRNGs", "Calculus", "Distributed", "FiniteDifferences", "ForwardDiff", "JSON", "StaticArrays", "HypothesisTests", "Test"]
+test = ["StableRNGs", "Calculus", "Distributed", "FiniteDifferences", "ForwardDiff", "JSON", "StaticArrays", "Test"]

src/univariate/continuous/locationscale.jl

@@ -133,6 +133,16 @@ end
 ccdf(d::DiscreteNonParametric{T}, x::Integer) where T = _ccdf(d, convert(T, x))
 ccdf(d::DiscreteNonParametric{T}, x::Real) where T = _ccdf(d, convert(T, x))
 
+# fix incorrect defaults
+for f in (:cdf, :ccdf)
+    _f = Symbol(:_, f)
+    logf = Symbol(:log, f)
+    @eval begin
+        $logf(d::DiscreteNonParametric{T}, x::Integer) where T = log($_f(d, convert(T, x)))
+        $logf(d::DiscreteNonParametric{T}, x::Real) where T = log($_f(d, convert(T, x)))
+    end
+end
+
 function quantile(d::DiscreteNonParametric, q::Real)
     0 <= q <= 1 || throw(DomainError())
     x = support(d)

src/univariate/discrete/discretenonparametric.jl

@@ -0,0 +1,132 @@
+"""
+    LocationScale(μ,σ,ρ)
+
+A location-scale transformed distribution with location parameter `μ`,
+scale parameter `σ`, and given univariate distribution `ρ`.
+
+If ``Z`` is a random variable with distribution `ρ`, then the distribution of the random
+variable
+```math
+X = μ + σ Z
+```
+is the location-scale transformed distribution with location parameter `μ` and scale
+parameter `σ`.
+
+If `ρ` is a discrete distribution, the probability mass function of
+the transformed distribution is given by
+```math
+P(X = x) = P\\left(Z = \\frac{x-μ}{σ} \\right).
+```
+If `ρ` is a continuous distribution, the probability density function of
+the transformed distribution is given by
+```math
+f(x) = \\frac{1}{σ} ρ \\! \\left( \\frac{x-μ}{σ} \\right).
+```
+
+```julia
+LocationScale(μ,σ,ρ) # location-scale transformed distribution
+params(d)            # Get the parameters, i.e. (μ, σ, and the base distribution)
+location(d)          # Get the location parameter
+scale(d)             # Get the scale parameter
+```
+
+External links
+[Location-Scale family on Wikipedia](https://en.wikipedia.org/wiki/Location%E2%80%93scale_family)
+"""
+struct LocationScale{T<:Real, S<:ValueSupport, D<:UnivariateDistribution{S}} <: UnivariateDistribution{S}
+    μ::T
+    σ::T
+    ρ::D
+    function LocationScale{T,S,D}(μ::T, σ::T, ρ::D; check_args=true) where {T<:Real, S<:ValueSupport, D<:UnivariateDistribution{S}}
+        check_args && @check_args(LocationScale, σ > zero(σ))
+        new{T, S, D}(μ, σ, ρ)
+    end
+end
+
+function LocationScale(μ::T, σ::T, ρ::UnivariateDistribution; check_args=true) where {T<:Real}
+    _T = promote_type(eltype(ρ), T)
+    D = typeof(ρ)
+    S = value_support(D)
+    return LocationScale{_T,S,D}(_T(μ), _T(σ), ρ; check_args=check_args)
+end
+
+LocationScale(μ::Real, σ::Real, ρ::UnivariateDistribution) = LocationScale(promote(μ, σ)..., ρ)
+
+# aliases
+const ContinuousLocationScale{T<:Real,D<:ContinuousUnivariateDistribution} = LocationScale{T,Continuous,D}
+const DiscreteLocationScale{T<:Real,D<:DiscreteUnivariateDistribution} = LocationScale{T,Discrete,D}
+
+Base.eltype(::Type{<:LocationScale{T}}) where T = T
+
+minimum(d::LocationScale) = d.μ + d.σ * minimum(d.ρ)
+maximum(d::LocationScale) = d.μ + d.σ * maximum(d.ρ)
+
+LocationScale(μ::Real, σ::Real, d::LocationScale) = LocationScale(μ + d.μ * σ, σ * d.σ, d.ρ)
+
+#### Conversions
+
+convert(::Type{LocationScale{T}}, μ::Real, σ::Real, ρ::D) where {T<:Real, D<:UnivariateDistribution} = LocationScale(T(μ),T(σ),ρ)
+convert(::Type{LocationScale{T}}, d::LocationScale{S}) where {T<:Real, S<:Real} = LocationScale(T(d.μ),T(d.σ),d.ρ, check_args=false)
+
+#### Parameters
+
+location(d::LocationScale) = d.μ
+scale(d::LocationScale) = d.σ
+params(d::LocationScale) = (d.μ,d.σ,d.ρ)
+partype(::LocationScale{T}) where {T} = T
+
+#### Statistics
+
+mean(d::LocationScale) = d.μ + d.σ * mean(d.ρ)
+median(d::LocationScale) = d.μ + d.σ * median(d.ρ)
+mode(d::LocationScale) = d.μ + d.σ * mode(d.ρ)
+modes(d::LocationScale) = d.μ .+ d.σ .* modes(d.ρ)
+
+var(d::LocationScale) = d.σ^2 * var(d.ρ)
+std(d::LocationScale) = d.σ * std(d.ρ)
+skewness(d::LocationScale) = skewness(d.ρ)
+kurtosis(d::LocationScale) = kurtosis(d.ρ)
+
+isplatykurtic(d::LocationScale) = isplatykurtic(d.ρ)
+isleptokurtic(d::LocationScale) = isleptokurtic(d.ρ)
+ismesokurtic(d::LocationScale) = ismesokurtic(d.ρ)
+
+entropy(d::ContinuousLocationScale) = entropy(d.ρ) + log(d.σ)
+entropy(d::DiscreteLocationScale) = entropy(d.ρ)
+
+mgf(d::LocationScale,t::Real) = exp(d.μ*t) * mgf(d.ρ,d.σ*t)
+
+#### Evaluation & Sampling
+
+pdf(d::ContinuousLocationScale,x::Real) = pdf(d.ρ,(x-d.μ)/d.σ) / d.σ
+pdf(d::DiscreteLocationScale, x::Real) = pdf(d.ρ,(x-d.μ)/d.σ)
+
+logpdf(d::ContinuousLocationScale,x::Real) = logpdf(d.ρ,(x-d.μ)/d.σ) - log(d.σ)
+logpdf(d::DiscreteLocationScale, x::Real) = logpdf(d.ρ,(x-d.μ)/d.σ)
+
+# additional definitions are required to fix ambiguity errors and incorrect defaults
+for f in (:cdf, :ccdf, :logcdf, :logccdf)
+    _f = Symbol(:_, f)
+    @eval begin
+        $f(d::LocationScale, x::Real) = $_f(d, x)
+        $f(d::DiscreteLocationScale, x::Real) = $_f(d, x)
+        $f(d::DiscreteLocationScale, x::Integer) = $_f(d, x)
+        $_f(d::LocationScale, x::Real) = $f(d.ρ, (x - d.μ) / d.σ)
+    end
+end
+
+quantile(d::LocationScale,q::Real) = d.μ + d.σ * quantile(d.ρ,q)
+
+rand(rng::AbstractRNG, d::LocationScale) = d.μ + d.σ * rand(rng, d.ρ)
+cf(d::LocationScale, t::Real) = cf(d.ρ,t*d.σ) * exp(1im*t*d.μ)
+gradlogpdf(d::ContinuousLocationScale, x::Real) = gradlogpdf(d.ρ,(x-d.μ)/d.σ) / d.σ
+
+#### Syntactic sugar for simple transforms of distributions, e.g., d + x, d - x, and so on
+
+Base.:+(d::UnivariateDistribution, x::Real) = LocationScale(x, one(x), d)
+Base.:+(x::Real, d::UnivariateDistribution) = d + x
+Base.:*(x::Real, d::UnivariateDistribution) = LocationScale(zero(x), x, d)
+Base.:*(d::UnivariateDistribution, x::Real) = x * d
+Base.:-(d::UnivariateDistribution, x::Real) = d + -x
+Base.:/(d::UnivariateDistribution, x::Real) = inv(x) * d
+

src/univariate/locationscale.jl

@@ -606,7 +606,6 @@ const continuous_distributions = [
     "ksonesided",
     "laplace",
     "levy",
-    "locationscale",
     "logistic",
     "noncentralbeta",
     "noncentralchisq",
@@ -631,6 +630,8 @@ const continuous_distributions = [
     "weibull"
 ]
 
+include(joinpath("univariate", "locationscale.jl"))
+
 for dname in discrete_distributions
     include(joinpath("univariate", "discrete", "$(dname).jl"))
 end