fix MvNormal bug (#189)

* fix MvNormal bug

* bump version

Project.toml

@@ -1,7 +1,7 @@
 name = "MeasureTheory"
 uuid = "eadaa1a4-d27c-401d-8699-e962e1bbc33b"
 authors = ["Chad Scherrer <[email protected]> and contributors"]
-version = "0.15.0"
+version = "0.15.1"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

src/combinators/affine.jl

@@ -228,8 +228,14 @@ end
 # #     lmul!(d.ω, z)
 # #     logdensity_def(d.parent, z)
 # # end
-
-@inline basemeasure(d::Affine{N,M,Tuple{Vararg{T,K}}}) where {K,N,M,T<:AbstractArray} = affine(getfield(d, :f), rootmeasure(d.parent))
+
+@inline function basemeasure(d::Affine{N,M,Tuple{A}}) where {N,M,A<:AbstractArray}
+    weightedmeasure(-logjac(d), OrthoLebesgue(params(d)))
+end
+
+@inline function basemeasure(d::Affine{N,M,Tuple{A1,A2}}) where {N,M,A1<:AbstractArray, A2<:AbstractArray}
+    weightedmeasure(-logjac(d), OrthoLebesgue(params(d)))
+end
 
 @inline basemeasure(d::Affine) = affine(getfield(d, :f), basemeasure(d.parent))
 
@@ -238,11 +244,6 @@ end
 @inline basemeasure(d::Affine{N,L}) where {N,L<:Lebesgue} = weightedmeasure(-logjac(d), d.parent)
 @inline basemeasure(d::Affine{N,L}) where {N,L<:LebesgueMeasure} = weightedmeasure(-logjac(d), d.parent)
 
-
-@inline function basemeasure(d::Affine{N,P, Tuple{Vararg{T, K}}}) where {K,N,L<:Union{<:Lebesgue, <:LebesgueMeasure},P<:PowerMeasure{L}, T<:AbstractArray} 
-    weightedmeasure(-logjac(d), OrthoLebesgue(params(d)))
-end
-
 logjac(d::Affine) = logjac(getfield(d, :f))
 
 function Random.rand!(

test/runtests.jl

@@ -351,6 +351,7 @@ end
         d = MvNormal(σ=σ)
         x = rand(d)
         @test logdensityof(d, x) ≈ logdensityof(Dists.MvNormal(Σ), x)
+        @test logdensityof(MvNormal(zeros(3), σ), x) ≈ logdensityof(d, x)
     end
 
     @testset "NegativeBinomial" begin