Merge pull request #496 from ReactiveBayes/revert-486-dont-check-for-proper-in-division-of

Revert "Dont check for proper in division of to accomodate inference"

Author: bvdmitri

ext/ReactiveMPProjectionExt/ReactiveMPProjectionExt.jl

@@ -5,13 +5,6 @@ using ReactiveMP, ExponentialFamily, Distributions, ExponentialFamilyProjection,
 struct DivisionOf{A, B}
     numerator::A
     denumerator::B
-    function DivisionOf(numerator::A, denumerator::B) where {A, B}
-        if variate_form(A) == variate_form(B)
-            return new{A, B}(numerator, denumerator)
-        else
-            error("DivisionOf does not support arguments of different variate forms: $(variate_form(A)) and $(variate_form(B))")
-        end
-    end
 end
 
 (divisionof::DivisionOf)(x) = logpdf(divisionof, x)
@@ -51,8 +44,6 @@ include("layout/cvi_projection.jl")
 include("rules/in.jl")
 include("rules/out.jl")
 include("rules/marginals.jl")
-include("divisionof/univariate_gaussian.jl")
-include("divisionof/multivariate_gaussian.jl")
 
 # This will enable the extension and make `CVIProjection` compatible with delta nodes 
 # Otherwise it should throw an error suggesting users to install `ExponentialFamilyProjection`

ext/ReactiveMPProjectionExt/divisionof/multivariate_gaussian.jl

@@ -15,105 +15,6 @@
     @test ext.DivisionOf(d1, d2) == prod(GenericProd(), missing, ext.DivisionOf(d1, d2))
 end
 
-@testitem "Check warning when DivisionOf is not proper" begin
-    using ExponentialFamily, ExponentialFamilyProjection, BayesBase
-    ext = Base.get_extension(ReactiveMP, :ReactiveMPProjectionExt)
-    @test !isnothing(ext)
-    using .ext
-
-    @testset "Check warning when DivisionOf is not proper" begin
-        d1 = NormalMeanVariance(0, 1)
-        d2 = NormalMeanVariance(0, 0.01)
-        d3 = NormalMeanVariance(0, 0.5)
-        @test_logs (:warn, "The product of $(d3) and $(d1) divided by $(d2) is not proper") prod(GenericProd(), ext.DivisionOf(d1, d2), d3)
-    end
-
-    @testset "Check warning when DivisionOf is not proper" begin
-        d1 = MvNormalMeanCovariance([0.0, 0.0], [1.0 0.0; 0.0 1.0])
-        d2 = MvNormalMeanCovariance([1.0, 1.0], [0.01 0.0; 0.0 0.01])
-        d3 = MvNormalMeanCovariance([2.0, 2.0], [3.0 0.0; 0.0 3.0])
-        @test_logs (:warn, "The product of $(d3) and $(d1) divided by $(d2) is not proper") prod(GenericProd(), ext.DivisionOf(d1, d2), d3)
-    end
-
-    @testset "Check no warning when DivisionOf is proper" begin
-        d1 = NormalMeanVariance(0, 1)
-        d2 = NormalMeanVariance(0, 2)
-        d3 = NormalMeanVariance(0, 1)
-        @test_logs prod(GenericProd(), ext.DivisionOf(d1, d2), d3)
-    end
-end
-
-@testitem "DivisionOf(Gaussian, Gaussian) x Gaussian" begin
-    using ExponentialFamily, ExponentialFamilyProjection, BayesBase
-
-    # `DivisionOf` is internal to the extension
-    ext = Base.get_extension(ReactiveMP, :ReactiveMPProjectionExt)
-    @test !isnothing(ext)
-    using .ext
-
-    d1 = NormalMeanVariance(0, 1)
-    d2 = NormalMeanVariance(1, 2)
-    d3 = NormalMeanVariance(2, 3)
-    result = NormalMeanVariance(0.2, 1.2)
-
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ prod(GenericProd(), d3, ext.DivisionOf(d1, d2))
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ result
-
-    d1 = NormalMeanVariance(0, 1)
-    d2 = convert(NormalWeightedMeanPrecision, d2)
-    d3 = convert(NormalMeanPrecision, d3)
-
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ prod(GenericProd(), d3, ext.DivisionOf(d1, d2))
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ result
-end
-
-@testitem "DivisionOf(MvGaussian, MvGaussian) x MvGaussian" begin
-    using ExponentialFamily, ExponentialFamilyProjection, BayesBase
-
-    # `DivisionOf` is internal to the extension
-    ext = Base.get_extension(ReactiveMP, :ReactiveMPProjectionExt)
-    @test !isnothing(ext)
-    using .ext
-
-    d1 = MvNormalMeanCovariance([0.0, 0.0], [1.0 0.0; 0.0 1.0])
-    d2 = MvNormalMeanCovariance([1.0, 1.0], [2.0 0.0; 0.0 2.0])
-    d3 = MvNormalMeanCovariance([2.0, 2.0], [3.0 0.0; 0.0 3.0])
-    result = MvNormalMeanCovariance([0.2, 0.2], [1.2 0.0; 0.0 1.2])
-
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ prod(GenericProd(), d3, ext.DivisionOf(d1, d2))
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ result
-
-    d1 = MvNormalMeanCovariance([0.0, 0.0], [1.0 0.0; 0.0 1.0])
-    d2 = convert(MvNormalWeightedMeanPrecision, d2)
-    d3 = convert(MvNormalMeanPrecision, d3)
-
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ prod(GenericProd(), d3, ext.DivisionOf(d1, d2))
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ result
-
-    d1 = MvNormalMeanCovariance([0.0, 0.0], [1.0 0.0; 0.0 1.0])
-    d2 = MvNormalMeanScalePrecision([1.0, 1.0], 1 / 2)
-    d3 = convert(MvNormalMeanPrecision, d3)
-
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ prod(GenericProd(), d3, ext.DivisionOf(d1, d2))
-    @test prod(GenericProd(), ext.DivisionOf(d1, d2), d3) ≈ result
-end
-
-@testitem "Raise error when DivisionOf of Univarive and Multivariate" begin
-    using ExponentialFamily, ExponentialFamilyProjection, BayesBase
-
-    # `DivisionOf` is internal to the extension
-    ext = Base.get_extension(ReactiveMP, :ReactiveMPProjectionExt)
-    @test !isnothing(ext)
-    using .ext
-
-    d1 = NormalMeanVariance(0, 1)
-    d2 = MvNormalMeanCovariance([0.0, 0.0], [1.0 0.0; 0.0 1.0])
-    d3 = NormalMeanVariance(0, 1)
-
-    @test_throws "DivisionOf does not support arguments of different variate forms" prod(GenericProd(), ext.DivisionOf(d1, d2), d2)
-    @test_throws "DivisionOf does not support arguments of different variate forms" prod(GenericProd(), d2, ext.DivisionOf(d1, d2))
-end
-
 @testitem "create_project_to_ins type stability" begin
     using ExponentialFamily, ExponentialFamilyProjection, BayesBase, Test
     using ReactiveMP: CVIProjection