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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,76 @@ | ||
| # Univariate | ||
|
|
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| const VectorOfUnivariate = Distributions.Product | ||
|
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| function arraydist(dists::AbstractVector{<:Normal{T}}) where {T} | ||
| means = mean.(dists) | ||
| vars = var.(dists) | ||
| return MvNormal(means, vars) | ||
| end | ||
| function arraydist(dists::AbstractVector{<:Normal{<:TrackedReal}}) | ||
| means = vcatmapreduce(mean, dists) | ||
| vars = vcatmapreduce(var, dists) | ||
| return MvNormal(means, vars) | ||
| end | ||
| function arraydist(dists::AbstractVector{<:UnivariateDistribution}) | ||
| return product_distribution(dists) | ||
| end | ||
| function Distributions.logpdf(dist::VectorOfUnivariate, x::AbstractVector{<:Real}) | ||
| return sum(vcatmapreduce(logpdf, dist.v, x)) | ||
| end | ||
| function Distributions.logpdf(dist::VectorOfUnivariate, x::AbstractMatrix{<:Real}) | ||
| # eachcol breaks Zygote, so we need an adjoint | ||
| return vcatmapreduce((dist, c) -> logpdf.(dist, c), dist.v, eachcol(x)) | ||
| end | ||
| @adjoint function Distributions.logpdf(dist::VectorOfUnivariate, x::AbstractMatrix{<:Real}) | ||
| # Any other more efficient implementation breaks Zygote | ||
| f(dist, x) = [sum(logpdf.(dist.v, view(x, :, i))) for i in 1:size(x, 2)] | ||
| return pullback(f, dist, x) | ||
| end | ||
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| struct MatrixOfUnivariate{ | ||
| S <: ValueSupport, | ||
| Tdist <: UnivariateDistribution{S}, | ||
| Tdists <: AbstractMatrix{Tdist}, | ||
| } <: MatrixDistribution{S} | ||
| dists::Tdists | ||
| end | ||
| Base.size(dist::MatrixOfUnivariate) = size(dist.dists) | ||
| function arraydist(dists::AbstractMatrix{<:UnivariateDistribution}) | ||
| return MatrixOfUnivariate(dists) | ||
| end | ||
| function Distributions.logpdf(dist::MatrixOfUnivariate, x::AbstractMatrix{<:Real}) | ||
| # Broadcasting here breaks Tracker for some reason | ||
| # A Zygote adjoint is defined for vcatmapreduce to use broadcasting | ||
| return sum(vcatmapreduce(logpdf, dist.dists, x)) | ||
| end | ||
| function Distributions.rand(rng::Random.AbstractRNG, dist::MatrixOfUnivariate) | ||
| return rand.(Ref(rng), dist.dists) | ||
| end | ||
|
|
||
| # Multivariate | ||
|
|
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| struct VectorOfMultivariate{ | ||
| S <: ValueSupport, | ||
| Tdist <: MultivariateDistribution{S}, | ||
| Tdists <: AbstractVector{Tdist}, | ||
| } <: MatrixDistribution{S} | ||
| dists::Tdists | ||
| end | ||
| Base.size(dist::VectorOfMultivariate) = (length(dist.dists[1]), length(dist)) | ||
| Base.length(dist::VectorOfMultivariate) = length(dist.dists) | ||
| function arraydist(dists::AbstractVector{<:MultivariateDistribution}) | ||
| return VectorOfMultivariate(dists) | ||
| end | ||
| function Distributions.logpdf(dist::VectorOfMultivariate, x::AbstractMatrix{<:Real}) | ||
| # eachcol breaks Zygote, so we define an adjoint | ||
| return sum(vcatmapreduce(logpdf, dist.dists, eachcol(x))) | ||
| end | ||
| @adjoint function Distributions.logpdf(dist::VectorOfMultivariate, x::AbstractMatrix{<:Real}) | ||
| f(dist, x) = sum(vcatmapreduce(i -> logpdf(dist.dists[i], view(x, :, i)), 1:size(x, 2))) | ||
| return pullback(f, dist, x) | ||
| end | ||
| function Distributions.rand(rng::Random.AbstractRNG, dist::VectorOfMultivariate) | ||
| init = reshape(rand(rng, dist.dists[1]), :, 1) | ||
| return mapreduce(i -> rand(rng, dist.dists[i]), hcat, 2:length(dist); init = init) | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,111 @@ | ||
| # Univariate | ||
|
|
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| const FillVectorOfUnivariate{ | ||
| S <: ValueSupport, | ||
| T <: UnivariateDistribution{S}, | ||
| Tdists <: Fill{T, 1}, | ||
| } = VectorOfUnivariate{S, T, Tdists} | ||
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| function filldist(dist::UnivariateDistribution, N::Int) | ||
| return product_distribution(Fill(dist, N)) | ||
| end | ||
| filldist(d::Normal, N::Int) = MvNormal(fill(d.μ, N), d.σ) | ||
|
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| function Distributions.logpdf( | ||
| dist::FillVectorOfUnivariate, | ||
| x::AbstractVector{<:Real}, | ||
| ) | ||
| return _logpdf(dist, x) | ||
| end | ||
| function Distributions.logpdf( | ||
| dist::FillVectorOfUnivariate, | ||
| x::AbstractMatrix{<:Real}, | ||
| ) | ||
| return _logpdf(dist, x) | ||
| end | ||
| @adjoint function Distributions.logpdf( | ||
| dist::FillVectorOfUnivariate, | ||
| x::AbstractMatrix{<:Real}, | ||
| ) | ||
| return pullback(_logpdf, dist, x) | ||
| end | ||
|
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| function _logpdf( | ||
| dist::FillVectorOfUnivariate, | ||
| x::AbstractVector{<:Real}, | ||
| ) | ||
| return _flat_logpdf(dist.v.value, x) | ||
| end | ||
| function _logpdf( | ||
| dist::FillVectorOfUnivariate, | ||
| x::AbstractMatrix{<:Real}, | ||
| ) | ||
| return _flat_logpdf_mat(dist.v.value, x) | ||
| end | ||
|
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| function _flat_logpdf(dist, x) | ||
| if toflatten(dist) | ||
| f, args = flatten(dist) | ||
| return sum(f.(args..., x)) | ||
| else | ||
| return sum(vcatmapreduce(x -> logpdf(dist, x), x)) | ||
| end | ||
| end | ||
| function _flat_logpdf_mat(dist, x) | ||
| if toflatten(dist) | ||
| f, args = flatten(dist) | ||
| return vec(sum(f.(args..., x), dims = 1)) | ||
| else | ||
| temp = vcatmapreduce(x -> logpdf(dist, x), x) | ||
| return vec(sum(reshape(temp, size(x)), dims = 1)) | ||
| end | ||
| end | ||
|
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| const FillMatrixOfUnivariate{ | ||
| S <: ValueSupport, | ||
| T <: UnivariateDistribution{S}, | ||
| Tdists <: Fill{T, 2}, | ||
| } = MatrixOfUnivariate{S, T, Tdists} | ||
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| function filldist(dist::UnivariateDistribution, N1::Integer, N2::Integer) | ||
| return MatrixOfUnivariate(Fill(dist, N1, N2)) | ||
| end | ||
| function Distributions.logpdf(dist::FillMatrixOfUnivariate, x::AbstractMatrix{<:Real}) | ||
| return _flat_logpdf(dist.dists.value, x) | ||
| end | ||
| function Distributions.rand(rng::Random.AbstractRNG, dist::FillMatrixOfUnivariate) | ||
| return rand(rng, dist.dists.value, length.(dist.dists.axes)) | ||
| end | ||
|
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| # Multivariate | ||
|
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| const FillVectorOfMultivariate{ | ||
| S <: ValueSupport, | ||
| T <: MultivariateDistribution{S}, | ||
| Tdists <: Fill{T, 1}, | ||
| } = VectorOfMultivariate{S, T, Tdists} | ||
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| function filldist(dist::MultivariateDistribution, N::Int) | ||
| return VectorOfMultivariate(Fill(dist, N)) | ||
| end | ||
| function Distributions.logpdf( | ||
| dist::FillVectorOfMultivariate, | ||
| x::AbstractMatrix{<:Real}, | ||
| ) | ||
| return _logpdf(dist, x) | ||
| end | ||
| @adjoint function Distributions.logpdf( | ||
| dist::FillVectorOfMultivariate, | ||
| x::AbstractMatrix{<:Real}, | ||
| ) | ||
| return pullback(_logpdf, dist, x) | ||
| end | ||
| function _logpdf( | ||
| dist::FillVectorOfMultivariate, | ||
| x::AbstractMatrix{<:Real}, | ||
| ) | ||
| return sum(logpdf(dist.dists.value, x)) | ||
| end | ||
| function Distributions.rand(rng::Random.AbstractRNG, dist::FillVectorOfMultivariate) | ||
| return rand(rng, dist.dists.value, length.(dist.dists.axes)) | ||
| end |
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