Merge pull request #14 from TuringLang/tor/turing-diag-normal-extras

added length, params, and entropy for TuringDiagNormal

Project.toml

@@ -10,6 +10,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

src/multivariate.jl

@@ -30,11 +30,10 @@ struct TuringDiagMvNormal{Tm<:AbstractVector, Tσ<:AbstractVector} <: Continuous
     σ::Tσ
 end
 
+Distributions.params(d::TuringDiagMvNormal) = (d.m, d.σ)
+Distributions.dim(d::TuringDiagMvNormal) = length(d.m)
 Base.length(d::TuringDiagMvNormal) = length(d.m)
-Base.size(d::TuringDiagMvNormal) = (length(d), length(d))
-function Distributions.rand(rng::Random.AbstractRNG, d::TuringDiagMvNormal)
-    return d.m .+ d.σ .* randn(rng, length(d))
-end
+Base.size(d::TuringDiagMvNormal) = (length(d), )
 function Distributions.rand(rng::Random.AbstractRNG, d::TuringDiagMvNormal, n::Int)
     return d.m .+ d.σ .* randn(rng, length(d), n)
 end
@@ -79,6 +78,12 @@ function _logpdf(d::TuringDenseMvNormal, x::AbstractMatrix)
     return -((size(x, 1) * log(2π) + logdet(d.C)) .+ vec(sum(abs2.(zygote_ldiv(d.C.U', x .- d.m)), dims=1))) ./ 2
 end
 
+import StatsBase: entropy
+function entropy(d::TuringDiagMvNormal)
+    T = eltype(d.σ)
+    return (length(d) * (T(log2π) + one(T)) / 2 + sum(log.(d.σ)))
+end
+
 # zero mean, dense covariance
 MvNormal(A::TrackedMatrix) = TuringMvNormal(A)
 

test/others.jl

@@ -1,3 +1,5 @@
+using StatsBase: entropy
+
 @testset "Others" begin
     @test fill(param(1.0), 3) isa TrackedArray
     x = rand(3)
@@ -11,3 +13,11 @@
     B = copy(A)
     @test DistributionsAD.zygote_ldiv(A, B) == A \ B
 end
+
+@testset "Extras from StatsBase.jl" begin
+    sigmas = exp.(randn(10))
+    d1 = TuringDiagMvNormal(zeros(10), sigmas)
+    d2 = MvNormal(zeros(10), sigmas)
+
+    @test entropy(d1) == entropy(d2)
+end