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Handle Real and Any better #96

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Handle Real and Any better #96

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2 changes: 1 addition & 1 deletion src/multivariate.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -254,7 +254,7 @@ end
MvNormal(d::Int, σ::TrackedReal{<:Real}) = TuringMvNormal(d, σ)

TuringMvNormal(d::Int, σ::Real) = TuringMvNormal(zeros(d), σ)
TuringMvNormal(m::AbstractVector{<:Real}, σ::Real) = TuringScalMvNormal(m, σ)
TuringMvNormal(m::AbstractVector, σ::Real) = TuringScalMvNormal(m, σ)
TuringMvNormal(σ::AbstractVector) = TuringMvNormal(zeros(length(σ)), σ)
TuringMvNormal(A::AbstractMatrix) = TuringMvNormal(zeros(size(A, 1)), A)
function TuringMvNormal(m::AbstractVector{<:Real}, σ::AbstractVector{<:Real})
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4 changes: 2 additions & 2 deletions src/mvcategorical.jl
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Expand Up @@ -77,7 +77,7 @@ function Base.rand(rng::AbstractRNG, d::MvDiscreteNonParametric{T,P}) where {T,P
p = probs(d)
n, k = size(p)
map(1:k) do j
draw = rand(rng, P)
draw = rand(rng, (P === Real ? Float64 : P))
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That seems like a quite crude heuristic. Ideally we would want P to be

mapreduce(promote_type, 1:n) do i
    typeof(p[i, j])
end

assuming that all provided probabilities can be promoted to a common non-abstract type in each column (but possibly not the same type for all columns) - I guess that should usually be satisfied?

BTW copying the logic from Distributions.DiscreteNonParametric here unfortunately introduces also the bugs present in the implementation in Distributions (see e.g. JuliaStats/Distributions.jl#1128), which might not be fixed when the upstream bugs are fixed if people don't keep track of Distributions closely.

cp = zero(P)
i = 0
while cp < draw && i < n
Expand All @@ -87,7 +87,7 @@ function Base.rand(rng::AbstractRNG, d::MvDiscreteNonParametric{T,P}) where {T,P
end
end

Base.rand(d::MvDiscreteNonParametric) = rand(GLOBAL_RNG, d)
Base.rand(d::MvDiscreteNonParametric) = rand(Random.GLOBAL_RNG, d)

# Override the method in testutils.jl since it assumes
# an evenly-spaced integer support
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