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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| # Univariate | ||
|
|
||
| const VectorOfUnivariate{ | ||
| S <: ValueSupport, | ||
| Tdist <: UnivariateDistribution{S}, | ||
| Tdists <: AbstractVector{Tdist}, | ||
| } = Distributions.Product{S, Tdist, Tdists} | ||
|
|
||
| function ArrayDist(dists::AbstractVector{<:Normal{T}}) where {T} | ||
| if T <: TrackedReal | ||
| init_m = vcat(dists[1].μ) | ||
| means = mapreduce(vcat, drop(dists, 1); init = init_m) do d | ||
| d.μ | ||
| end | ||
| init_v = vcat(dists[1].σ^2) | ||
| vars = mapreduce(vcat, drop(dists, 1); init = init_v) do d | ||
| d.σ^2 | ||
| end | ||
| else | ||
| means = [d.μ for d in dists] | ||
| vars = [d.σ^2 for d in dists] | ||
| end | ||
|
|
||
| return MvNormal(means, vars) | ||
| end | ||
| function ArrayDist(dists::AbstractVector{<:UnivariateDistribution}) | ||
| return Distributions.Product(dists) | ||
| end | ||
| function Distributions.logpdf(dist::VectorOfUnivariate, x::AbstractVector{<:Real}) | ||
| return sum(logpdf.(dist.v, x)) | ||
| end | ||
| function Distributions.logpdf(dist::VectorOfUnivariate, x::AbstractMatrix{<:Real}) | ||
| # Any other more efficient implementation breaks Zygote | ||
| return [logpdf(dist, x[:,i]) for i in 1:size(x, 2)] | ||
| end | ||
| function Distributions.logpdf( | ||
| dist::VectorOfUnivariate, | ||
| x::AbstractVector{<:AbstractMatrix{<:Real}}, | ||
| ) | ||
| return logpdf.(Ref(dist), x) | ||
| end | ||
|
|
||
| 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 | ||
| return sum(zip(dist.dists, x)) do (dist, x) | ||
| logpdf(dist, x) | ||
| end | ||
| end | ||
| function Distributions.rand(rng::Random.AbstractRNG, dist::MatrixOfUnivariate) | ||
| return rand.(Ref(rng), dist.dists) | ||
| end | ||
|
|
||
| # Multivariate | ||
|
|
||
| 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}) | ||
| return sum(logpdf(dist.dists[i], x[:,i]) for i in 1:length(dist)) | ||
| end | ||
| function Distributions.logpdf( | ||
| dist::VectorOfMultivariate, | ||
| x::AbstractVector{<:AbstractVector{<:Real}}, | ||
| ) | ||
| return sum(logpdf(dist.dists[i], x[i]) for i in 1:length(dist)) | ||
| 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 +1,76 @@ | ||
| macro register(dist) | ||
| return quote | ||
| DistributionsAD.eval(getexpr($(esc(dist)))) | ||
| DistributionsAD.toflatten(::$(esc(dist))) = true | ||
| end | ||
| end | ||
| function getexpr(Tdist) | ||
| x = gensym() | ||
| fnames = fieldnames(Tdist) | ||
| flattened_args = Expr(:tuple, [:(dist.$f) for f in fnames]...) | ||
| func = Expr(:->, | ||
| Expr(:tuple, fnames..., x), | ||
| Expr(:block, | ||
| Expr(:call, :logpdf, | ||
| Expr(:call, :($Tdist), fnames...), | ||
| x, | ||
| ) | ||
| ) | ||
| ) | ||
| return :(flatten(dist::$Tdist) = ($func, $flattened_args)) | ||
| end | ||
| const flattened_dists = [ Bernoulli, | ||
| BetaBinomial, | ||
| Binomial, | ||
| Geometric, | ||
| NegativeBinomial, | ||
| Poisson, | ||
| Skellam, | ||
| PoissonBinomial, | ||
| Arcsine, | ||
| Beta, | ||
| BetaPrime, | ||
| Biweight, | ||
| Cauchy, | ||
| Chernoff, | ||
| Chi, | ||
| Chisq, | ||
| Cosine, | ||
| Epanechnikov, | ||
| Erlang, | ||
| Exponential, | ||
| FDist, | ||
| Frechet, | ||
| Gamma, | ||
| GeneralizedExtremeValue, | ||
| GeneralizedPareto, | ||
| Gumbel, | ||
| InverseGamma, | ||
| InverseGaussian, | ||
| Kolmogorov, | ||
| Laplace, | ||
| Levy, | ||
| LocationScale, | ||
| Logistic, | ||
| LogitNormal, | ||
| LogNormal, | ||
| Normal, | ||
| NormalCanon, | ||
| NormalInverseGaussian, | ||
| Pareto, | ||
| PGeneralizedGaussian, | ||
| Rayleigh, | ||
| SymTriangularDist, | ||
| TDist, | ||
| TriangularDist, | ||
| Triweight, | ||
| Categorical, | ||
| Truncated, | ||
| ] | ||
| for T in flattened_dists | ||
| @eval toflatten(::$T) = true | ||
| end | ||
| toflatten(::Distribution) = false | ||
| for T in flattened_dists | ||
| eval(getexpr(T)) | ||
| end |
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