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Add UniformScaling constructors for normals #182

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Jan 29, 2024
Merged

Add UniformScaling constructors for normals #182

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b288a2e
Add tests for normals with uniformscaling
bartvanerp Jan 11, 2024
d50aeda
Add UniformScaling constructor for normals
bartvanerp Jan 11, 2024
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7 changes: 7 additions & 0 deletions src/distributions/normal_family/mv_normal_mean_covariance.jl
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Expand Up @@ -35,6 +35,13 @@ function MvNormalMeanCovariance(μ::AbstractVector{T}) where {T}
return MvNormalMeanCovariance(μ, convert(AbstractArray{T}, ones(length(μ))))
end

function MvNormalMeanCovariance(μ::AbstractVector{T1}, Σ::UniformScaling{T2}) where {T1, T2}
T = promote_type(T1, T2)
μ_new = convert(AbstractArray{T}, μ)
Σ_new = convert(UniformScaling{T}, Σ)(length(μ))
return MvNormalMeanCovariance(μ_new, Σ_new)
end

Distributions.distrname(::MvNormalMeanCovariance) = "MvNormalMeanCovariance"

function BayesBase.weightedmean(dist::MvNormalMeanCovariance)
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7 changes: 7 additions & 0 deletions src/distributions/normal_family/mv_normal_mean_precision.jl
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Expand Up @@ -35,6 +35,13 @@ function MvNormalMeanPrecision(μ::AbstractVector{T}) where {T}
return MvNormalMeanPrecision(μ, convert(AbstractArray{T}, ones(length(μ))))
end

function MvNormalMeanPrecision(μ::AbstractVector{T1}, Λ::UniformScaling{T2}) where {T1, T2}
T = promote_type(T1, T2)
μ_new = convert(AbstractArray{T}, μ)
Λ_new = convert(UniformScaling{T}, Λ)(length(μ))
return MvNormalMeanPrecision(μ_new, Λ_new)
end

Distributions.distrname(::MvNormalMeanPrecision) = "MvNormalMeanPrecision"

BayesBase.weightedmean(dist::MvNormalMeanPrecision) = precision(dist) * mean(dist)
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7 changes: 7 additions & 0 deletions src/distributions/normal_family/mv_normal_weighted_mean_precision.jl
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Expand Up @@ -34,6 +34,13 @@ function MvNormalWeightedMeanPrecision(xi::AbstractVector{T}) where {T}
return MvNormalWeightedMeanPrecision(xi, convert(AbstractArray{T}, ones(length(xi))))
end

function MvNormalWeightedMeanPrecision(xi::AbstractVector{T1}, Λ::UniformScaling{T2}) where {T1, T2}
T = promote_type(T1, T2)
xi_new = convert(AbstractArray{T}, xi)
Λ_new = convert(UniformScaling{T}, Λ)(length(xi))
return MvNormalWeightedMeanPrecision(xi_new, Λ_new)
end

Distributions.distrname(::MvNormalWeightedMeanPrecision) = "MvNormalWeightedMeanPrecision"

BayesBase.weightedmean(dist::MvNormalWeightedMeanPrecision) = dist.xi
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7 changes: 7 additions & 0 deletions src/distributions/normal_family/normal_mean_precision.jl
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Expand Up @@ -21,6 +21,13 @@ NormalMeanPrecision(μ::Integer, w::Integer) = NormalMeanPrecision(float(μ), fl
NormalMeanPrecision(μ::Real) = NormalMeanPrecision(μ, one(μ))
NormalMeanPrecision() = NormalMeanPrecision(0.0, 1.0)

function NormalMeanPrecision(μ::T1, w::UniformScaling{T2}) where {T1 <: Real, T2}
T = promote_type(T1, T2)
μ_new = convert(T, μ)
w_new = convert(T, w.λ)
return NormalMeanPrecision(μ_new, w_new)
end

Distributions.@distr_support NormalMeanPrecision -Inf Inf

BayesBase.support(dist::NormalMeanPrecision) = Distributions.RealInterval(minimum(dist), maximum(dist))
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7 changes: 7 additions & 0 deletions src/distributions/normal_family/normal_mean_variance.jl
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Expand Up @@ -21,6 +21,13 @@ NormalMeanVariance(μ::Integer, v::Integer) = NormalMeanVariance(float(μ), floa
NormalMeanVariance(μ::T) where {T <: Real} = NormalMeanVariance(μ, one(T))
NormalMeanVariance() = NormalMeanVariance(0.0, 1.0)

function NormalMeanVariance(μ::T1, v::UniformScaling{T2}) where {T1 <: Real, T2}
T = promote_type(T1, T2)
μ_new = convert(T, μ)
v_new = convert(T, v.λ)
return NormalMeanVariance(μ_new, v_new)
end

Distributions.@distr_support NormalMeanVariance -Inf Inf

BayesBase.support(dist::NormalMeanVariance) = Distributions.RealInterval(minimum(dist), maximum(dist))
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7 changes: 7 additions & 0 deletions src/distributions/normal_family/normal_weighted_mean_precision.jl
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Expand Up @@ -21,6 +21,13 @@ NormalWeightedMeanPrecision(xi::Integer, w::Integer) = NormalWeightedMeanPrecisi
NormalWeightedMeanPrecision(xi::Real) = NormalWeightedMeanPrecision(xi, one(xi))
NormalWeightedMeanPrecision() = NormalWeightedMeanPrecision(0.0, 1.0)

function NormalWeightedMeanPrecision(xi::T1, w::UniformScaling{T2}) where {T1 <: Real, T2}
T = promote_type(T1, T2)
xi_new = convert(T, xi)
w_new = convert(T, w.λ)
return NormalWeightedMeanPrecision(xi_new, w_new)
end

Distributions.@distr_support NormalWeightedMeanPrecision -Inf Inf

BayesBase.support(dist::NormalWeightedMeanPrecision) = Distributions.RealInterval(minimum(dist), maximum(dist))
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3 changes: 3 additions & 0 deletions test/distributions/normal_family/mv_normal_mean_covariance_tests.jl
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Expand Up @@ -15,6 +15,9 @@
@test eltype(MvNormalMeanCovariance([1, 1], [1, 1])) === Float64
@test eltype(MvNormalMeanCovariance([1.0f0, 1.0f0])) === Float32
@test eltype(MvNormalMeanCovariance([1.0f0, 1.0f0], [1.0f0, 1.0f0])) === Float32

@test MvNormalMeanCovariance(ones(3), 5I) == MvNormalMeanCovariance(ones(3), Diagonal(5 * ones(3)))
@test MvNormalMeanCovariance([1, 2, 3, 4], 7.0I) == MvNormalMeanCovariance([1.0, 2.0, 3.0, 4.0], Diagonal(7.0 * ones(4)))
end

@testitem "MvNormalMeanCovariance: distrname" begin
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3 changes: 3 additions & 0 deletions test/distributions/normal_family/mv_normal_mean_precision_tests.jl
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Expand Up @@ -15,6 +15,9 @@
@test eltype(MvNormalMeanPrecision([1, 1], [1, 1])) === Float64
@test eltype(MvNormalMeanPrecision([1.0f0, 1.0f0])) === Float32
@test eltype(MvNormalMeanPrecision([1.0f0, 1.0f0], [1.0f0, 1.0f0])) === Float32

@test MvNormalMeanPrecision(ones(3), 5I) == MvNormalMeanPrecision(ones(3), Diagonal(5 * ones(3)))
@test MvNormalMeanPrecision([1, 2, 3, 4], 7.0I) == MvNormalMeanPrecision([1.0, 2.0, 3.0, 4.0], Diagonal(7.0 * ones(4)))
end

@testitem "MvNormalMeanPrecision: distrname" begin
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3 changes: 3 additions & 0 deletions test/distributions/normal_family/mv_normal_weighted_mean_precision_tests.jl
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Expand Up @@ -16,6 +16,9 @@
@test eltype(MvNormalWeightedMeanPrecision([1, 1], [1, 1])) === Float64
@test eltype(MvNormalWeightedMeanPrecision([1.0f0, 1.0f0])) === Float32
@test eltype(MvNormalWeightedMeanPrecision([1.0f0, 1.0f0], [1.0f0, 1.0f0])) === Float32

@test MvNormalWeightedMeanPrecision(ones(3), 5I) == MvNormalWeightedMeanPrecision(ones(3), Diagonal(5 * ones(3)))
@test MvNormalWeightedMeanPrecision([1, 2, 3, 4], 7.0I) == MvNormalWeightedMeanPrecision([1.0, 2.0, 3.0, 4.0], Diagonal(7.0 * ones(4)))
end

@testitem "MvNormalWeightedMeanPrecision: distrname" begin
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3 changes: 3 additions & 0 deletions test/distributions/normal_family/normal_mean_precision_tests.jl
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Expand Up @@ -27,6 +27,9 @@
@test eltype(NormalMeanPrecision(0.0f0)) === Float32
@test eltype(NormalMeanPrecision(0.0f0, 1.0f0)) === Float32
@test eltype(NormalMeanPrecision(0.0f0, 1.0)) === Float64

@test NormalMeanPrecision(3, 5I) == NormalMeanPrecision(3, 5)
@test NormalMeanPrecision(2, 7.0I) == NormalMeanPrecision(2.0, 7.0)
end

@testitem "NormalMeanPrecision: Stats methods" begin
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3 changes: 3 additions & 0 deletions test/distributions/normal_family/normal_mean_variance_tests.jl
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Expand Up @@ -27,6 +27,9 @@
@test eltype(NormalMeanVariance(0.0f0)) === Float32
@test eltype(NormalMeanVariance(0.0f0, 1.0f0)) === Float32
@test eltype(NormalMeanVariance(0.0f0, 1.0)) === Float64

@test NormalMeanVariance(3, 5I) == NormalMeanVariance(3, 5)
@test NormalMeanVariance(2, 7.0I) == NormalMeanVariance(2.0, 7.0)
end

@testitem "NormalMeanVariance: Stats methods" begin
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3 changes: 3 additions & 0 deletions test/distributions/normal_family/normal_weighted_mean_precision_tests.jl
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Expand Up @@ -26,6 +26,9 @@
@test eltype(NormalWeightedMeanPrecision(0.0f0)) === Float32
@test eltype(NormalWeightedMeanPrecision(0.0f0, 1.0f0)) === Float32
@test eltype(NormalWeightedMeanPrecision(0.0f0, 1.0)) === Float64

@test NormalWeightedMeanPrecision(3, 5I) == NormalWeightedMeanPrecision(3, 5)
@test NormalWeightedMeanPrecision(2, 7.0I) == NormalWeightedMeanPrecision(2.0, 7.0)
end

@testitem "NormalWeightedMeanPrecision: Stats methods" begin
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