fix tests

ReactiveBayes · Oct 3, 2023 · 230d456 · 230d456
1 parent bb32410
commit 230d456
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Showing 5 changed files with 14 additions and 14 deletions.
diff --git a/src/distributions/wishart.jl b/src/distributions/wishart.jl
@@ -84,7 +84,7 @@ function Distributions.mean(::typeof(cholinv), distribution::Wishart)
     return mean(InverseWishart(ν, cholinv(S)))
 end
 
-vague(::Type{<:Wishart}, dims::Int) = Wishart(dims, huge .* Eye(dims))
+vague(::Type{<:Wishart}, dims::Int) = Wishart(dims, huge .* Array(Eye(dims)))
 
 Base.ndims(dist::Wishart) = size(dist, 1)
 

diff --git a/src/distributions/wishart_inverse.jl b/src/distributions/wishart_inverse.jl
@@ -192,7 +192,7 @@ function Distributions._logpdf(d::InverseWishartFast, X::AbstractMatrix{<:Real})
     return Distributions.logkernel(dist, X) + dist.logc0
 end
 
-vague(::Type{<:InverseWishart}, dims::Integer) = InverseWishart(dims + 2, tiny .* Eye(dims))
+vague(::Type{<:InverseWishart}, dims::Integer) = InverseWishart(dims + 2, tiny .* Array(Eye(dims)))
 
 Base.ndims(dist::InverseWishart) = size(dist, 1)
 

diff --git a/test/distributions/test_mv_normal_wishart.jl b/test/distributions/test_mv_normal_wishart.jl
@@ -26,7 +26,7 @@ include("../testutils.jl")
     end
 
     @testset "ExponentialFamilyDistribution{MvNormalWishart}" begin
-        @testset for dim in (3), invS in rand(Wishart(10, Eye(dim)), 4)
+        @testset for dim in (3), invS in rand(Wishart(10, Array(Eye(dim))), 4)
             ν = dim + 2
             @testset let (d = MvNormalWishart(rand(dim), invS, rand(), ν))
                 ef = test_exponentialfamily_interface(
@@ -54,7 +54,7 @@ include("../testutils.jl")
     end
 
     @testset "prod with ExponentialFamilyDistribution{MvNormalWishart}" begin
-        for Sleft in rand(Wishart(10, Eye(2)), 2), Sright in rand(Wishart(10, Eye(2)), 2), νright in (6, 7), νleft in (4, 5)
+        for Sleft in rand(Wishart(10, Array(Eye(2))), 2), Sright in rand(Wishart(10, Array(Eye(2))), 2), νright in (6, 7), νleft in (4, 5)
             let left = MvNormalWishart(rand(2), Sleft, rand(), νleft), right = MvNormalWishart(rand(2), Sright, rand(), νright)
                 @test test_generic_simple_exponentialfamily_product(
                     left,

diff --git a/test/distributions/test_wishart.jl b/test/distributions/test_wishart.jl
@@ -77,16 +77,16 @@ import StatsFuns: logmvgamma
     end
 
     @testset "ExponentialFamilyDistribution{WishartFast}" begin
-        @testset for dim in (3), invS in rand(Wishart(10, Eye(dim)), 2)
+        @testset for dim in (3), invS in rand(Wishart(10, Array(Eye(dim))), 2)
             ν = dim + 2
             @testset let (d = WishartFast(ν, invS))
                 ef = test_exponentialfamily_interface(d; option_assume_no_allocations = false, test_fisherinformation_against_hessian = false)
                 (η1, η2) = unpack_parameters(WishartFast, getnaturalparameters(ef))
 
-                for x in Eye(dim)
+                for x in (Eye(dim), Diagonal(ones(dim)), Array(Eye(dim)))
                     @test @inferred(isbasemeasureconstant(ef)) === ConstantBaseMeasure()
                     @test @inferred(basemeasure(ef, x)) === 1.0
-                    @test @inferred(sufficientstatistics(ef, x)) === (logdet(x), x)
+                    @test all(@inferred(sufficientstatistics(ef, x)) .≈ (logdet(x), x))
                     @test @inferred(logpartition(ef)) ≈ -(η1 + (dim + 1) / 2) * logdet(-η2) + logmvgamma(dim, η1 + (dim + 1) / 2)
                 end
             end
@@ -113,7 +113,7 @@ import StatsFuns: logmvgamma
     end
 
     @testset "prod with ExponentialFamilyDistribution{Wishart}" begin
-        for Sleft in rand(Wishart(10, Eye(2)), 2), Sright in rand(Wishart(10, Eye(2)), 2), νright in (6, 7), νleft in (4, 5)
+        for Sleft in rand(Wishart(10, Array(Eye(2))), 2), Sright in rand(Wishart(10, Array(Eye(2))), 2), νright in (6, 7), νleft in (4, 5)
             let left = WishartFast(νleft, Sleft), right = WishartFast(νright, Sright)
                 @test test_generic_simple_exponentialfamily_product(
                     left,

diff --git a/test/distributions/test_wishart_inverse.jl b/test/distributions/test_wishart_inverse.jl
@@ -27,16 +27,16 @@ include("../testutils.jl")
     end
 
     @testset "ExponentialFamilyDistribution{InverseWishartFast}" begin
-        @testset for dim in (3), S in rand(InverseWishart(10, Eye(dim)), 2)
+        @testset for dim in (3), S in rand(InverseWishart(10, Array(Eye(dim))), 2)
             ν = dim + 4
             @testset let (d = InverseWishartFast(ν, S))
                 ef = test_exponentialfamily_interface(d; option_assume_no_allocations = false, test_fisherinformation_against_hessian = false)
                 (η1, η2) = unpack_parameters(InverseWishartFast, getnaturalparameters(ef))
 
-                for x in Eye(dim)
+                for x in (Eye(dim), Diagonal(ones(dim)), Array(Eye(dim)))
                     @test @inferred(isbasemeasureconstant(ef)) === ConstantBaseMeasure()
                     @test @inferred(basemeasure(ef, x)) === 1.0
-                    @test @inferred(sufficientstatistics(ef, x)) === (logdet(x), inv(x))
+                    @test all(@inferred(sufficientstatistics(ef, x)) .≈ (logdet(x), inv(x)))
                     @test @inferred(logpartition(ef)) ≈ (η1 + (dim + 1) / 2) * logdet(-η2) + logmvgamma(dim, -(η1 + (dim + 1) / 2))
                 end
             end
@@ -116,7 +116,7 @@ include("../testutils.jl")
         samples = rand(rng, InverseWishart(ν, S), Int(1e6))
         @test isapprox(mean(logdet, InverseWishartFast(ν, S)), mean(logdet.(samples)), atol = 1e-2)
 
-        ν, S = 4.0, Eye(3)
+        ν, S = 4.0, Array(Eye(3))
         samples = rand(rng, InverseWishart(ν, S), Int(1e6))
         @test isapprox(mean(logdet, InverseWishartFast(ν, S)), mean(logdet.(samples)), atol = 1e-2)
     end
@@ -127,7 +127,7 @@ include("../testutils.jl")
         samples = rand(rng, InverseWishart(ν, S), Int(1e6))
         @test isapprox(mean(inv, InverseWishartFast(ν, S)), mean(inv.(samples)), atol = 1e-2)
 
-        ν, S = 4.0, Eye(3)
+        ν, S = 4.0, Array(Eye(3))
         samples = rand(rng, InverseWishart(ν, S), Int(1e6))
         @test isapprox(mean(inv, InverseWishartFast(ν, S)), mean(inv.(samples)), atol = 1e-2)
     end
@@ -185,7 +185,7 @@ include("../testutils.jl")
     end
 
     @testset "prod with ExponentialFamilyDistribution{InverseWishartFast}" begin
-        for Sleft in rand(InverseWishart(10, Eye(2)), 2), Sright in rand(InverseWishart(10, Eye(2)), 2), νright in (6, 7), νleft in (4, 5)
+        for Sleft in rand(InverseWishart(10, Array(Eye(2))), 2), Sright in rand(InverseWishart(10, Array(Eye(2))), 2), νright in (6, 7), νleft in (4, 5)
             let left = InverseWishartFast(νleft, Sleft), right = InverseWishartFast(νright, Sright)
                 @test test_generic_simple_exponentialfamily_product(
                     left,