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Added eltype for multivariate distributions #882

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merged 11 commits into from
Jul 11, 2019
Merged

Added eltype for multivariate distributions #882

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1eba376
Added eltype for multivariate distributions
theogf May 8, 2019
fa266f0
Removed eltype from multinomial distributions
theogf May 8, 2019
746ff48
Remove <:Real for Dirichlet
theogf Jun 11, 2019
014905e
Merge remote-tracking branch 'upstream/master'
theogf Jun 11, 2019
d9dd9b3
Homogeinity of `where {T}` for eltype
theogf Jun 11, 2019
726249a
Additional docs for output type of rand
theogf Jun 21, 2019
32e0b28
Rework of mvt dist for parametrized inputs
theogf Jun 21, 2019
646f70f
Added Tests to verify output type of rand
theogf Jun 21, 2019
91b032d
Added all @matbesancon suggestions
theogf Jun 21, 2019
365802f
Added recommendation to add `eltype` for new Multivariate distributions
theogf Jun 21, 2019
ad4e660
Merge remote-tracking branch 'upstream/master'
theogf Jul 10, 2019
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1 change: 1 addition & 0 deletions src/multivariate/dirichlet.jl
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Expand Up @@ -57,6 +57,7 @@ end

length(d::DirichletCanon) = length(d.alpha)

eltype(::Dirichlet{T}) where {T} = T
#### Conversions
convert(::Type{Dirichlet{Float64}}, cf::DirichletCanon) = Dirichlet(cf.alpha)
convert(::Type{Dirichlet{T}}, alpha::Vector{S}) where {T<:Real, S<:Real} =
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3 changes: 1 addition & 2 deletions src/multivariate/dirichletmultinomial.jl
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Expand Up @@ -22,8 +22,7 @@ ncategories(d::DirichletMultinomial) = length(d.α)
length(d::DirichletMultinomial) = ncategories(d)
ntrials(d::DirichletMultinomial) = d.n
params(d::DirichletMultinomial) = (d.n, d.α)
@inline partype(d::DirichletMultinomial{T}) where {T<:Real} = T

@inline partype(d::DirichletMultinomial{T}) where {T} = T

# Statistics
mean(d::DirichletMultinomial) = d.α .* (d.n / d.α0)
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2 changes: 2 additions & 0 deletions src/multivariate/mvlognormal.jl
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Expand Up @@ -175,6 +175,8 @@ MvLogNormal(Σ::AbstractMatrix) = MvLogNormal(MvNormal(Σ))
MvLogNormal(σ::AbstractVector) = MvLogNormal(MvNormal(σ))
MvLogNormal(d::Int,s::Real) = MvLogNormal(MvNormal(d,s))


eltype(::MvLogNormal{T}) where {T} = T
### Conversion
function convert(::Type{MvLogNormal{T}}, d::MvLogNormal) where T<:Real
MvLogNormal(convert(MvNormal{T}, d.normal))
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4 changes: 3 additions & 1 deletion src/multivariate/mvnormal.jl
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Expand Up @@ -219,6 +219,8 @@ MvNormal(Σ::Matrix{<:Real}) = MvNormal(PDMat(Σ))
MvNormal(σ::Vector{<:Real}) = MvNormal(PDiagMat(abs2.(σ)))
MvNormal(d::Int, σ::Real) = MvNormal(ScalMat(d, abs2(σ)))


eltype(::MvNormal{T}) where {T} = T
### Conversion
function convert(::Type{MvNormal{T}}, d::MvNormal) where T<:Real
MvNormal(convert(AbstractArray{T}, d.μ), convert(AbstractArray{T}, d.Σ))
Expand Down Expand Up @@ -270,7 +272,7 @@ _rand!(rng::AbstractRNG, d::MvNormal, x::VecOrMat) =
# Workaround: randn! only works for Array, but not generally for AbstractArray
function _rand!(rng::AbstractRNG, d::MvNormal, x::AbstractVector)
for i in eachindex(x)
@inbounds x[i] = randn(rng)
@inbounds x[i] = randn(rng,eltype(d))
end
add!(unwhiten!(d.Σ, x), d.μ)
end
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3 changes: 2 additions & 1 deletion src/multivariate/mvnormalcanon.jl
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Expand Up @@ -85,7 +85,7 @@ function MvNormalCanon(μ::AbstractVector{T}, h::AbstractVector{T}, J::AbstractP
if typeof(μ) == typeof(h)
return MvNormalCanon{T,typeof(J),typeof(μ)}(μ, h, J)
else
return MvNormalCanon{T,typeof(J),Vector{T}}(collect(μ), collect(h), J)
return MvNormalCanon{T,typeof(J),Vector{T}}(collect(μ), collect(h), J)
end
end

Expand Down Expand Up @@ -130,6 +130,7 @@ distrname(d::ZeroMeanIsoNormalCanon) = "ZeroMeanIsoNormalCanon"
distrname(d::ZeroMeanDiagNormalCanon) = "ZeroMeanDiagormalCanon"
distrname(d::ZeroMeanFullNormalCanon) = "ZeroMeanFullNormalCanon"

eltype(::MvNormalCanon{T}) where {T} = T
### Conversion
function convert(::Type{MvNormalCanon{T}}, d::MvNormalCanon) where {T<:Real}
MvNormalCanon(convert(AbstractArray{T}, d.μ), convert(AbstractArray{T}, d.h), convert(AbstractArray{T}, d.J))
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1 change: 1 addition & 0 deletions src/multivariate/mvtdist.jl
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Expand Up @@ -34,6 +34,7 @@ GenericMvTDist(df::Real, μ::Vector{S}, Σ::Cov) where {Cov<:AbstractPDMat, S<:R

GenericMvTDist(df::T, Σ::Cov) where {Cov<:AbstractPDMat, T<:Real} = GenericMvTDist(df, zeros(dim(Σ)), Σ, true)

eltype(::GenericMvTDist{T}) where {T} = T
### Conversion
function convert(::Type{GenericMvTDist{T}}, d::GenericMvTDist) where T<:Real
S = convert(AbstractArray{T}, d.Σ)
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