Testvalues

Project.toml

@@ -1,7 +1,7 @@
 name = "MeasureTheory"
 uuid = "eadaa1a4-d27c-401d-8699-e962e1bbc33b"
 authors = ["Chad Scherrer <[email protected]> and contributors"]
-version = "0.10.1"
+version = "0.10.2"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

src/combinators/chain.jl

@@ -179,3 +179,9 @@ function Base.collect(r::Realized)
     end
     return a
 end 
+
+function testvalue(mc::Chain)
+    μ = mc.μ
+    κ = mc.κ
+    rand(Chain(Dirac ∘ testvalue ∘ κ, (Dirac ∘ testvalue)(μ)))
+end

src/parameterized/dirichlet.jl

@@ -26,3 +26,8 @@ end
 Base.rand(rng::AbstractRNG, T::Type, μ::Dirichlet) = rand(rng, Dists.Dirichlet(μ.α))
 
 distproxy(d::Dirichlet{(:α,)}) = Dists.Dirichlet(d.α)
+
+function testvalue(d::Dirichlet{(:α,)})
+    n = length(d.α)
+    Fill(1/n, n)
+end

src/parameterized/lkj-cholesky.jl

@@ -102,6 +102,11 @@ function Base.rand(rng::AbstractRNG, ::Type, d::LKJCholesky{(:k, :η,)})
     return cholesky(Positive, rand(rng, Dists.LKJ(d.k, d.η)))
 end;
 
+function testvalue(d::LKJCholesky)
+    t = CorrCholesky(d.k)
+    return transform(t, zeros(dimension(t)))
+end
+
 function Base.rand(rng::AbstractRNG, ::Type, d::LKJCholesky{(:k, :logη)})
     return cholesky(Positive, rand(rng, Dists.LKJ(d.k, exp(d.logη))))
 end;

src/parameterized/multinomial.jl

@@ -36,3 +36,12 @@ function logmultinomial(k)
     end
     result
 end
+
+function testvalue(d::Multinomial{(:n,:p)})
+    n = d.n
+    l = length(d.p)
+    q,r = divrem(n, l)
+    x = fill(q, l)
+    x[1] += r
+    return x
+end

src/primitives/dirac.jl

@@ -32,3 +32,5 @@ function logdensity(μ::Dirac{M}, ν::Dirac{M}, x) where {M}
         return -Inf
     end
 end
+
+testvalue(d::Dirac) = d.x

src/utils.jl

@@ -23,6 +23,8 @@ end
 
 constructor(::T) where {T} = constructor(T)
 
+constructor(::Type{T}) where {T} = constructorof(T)
+
 macro trysupport(ex)
     ex = esc(ex)
     quote

test/runtests.jl

@@ -21,6 +21,53 @@ function draw2(μ)
     return (x,y)
 end
 
+measures = [
+    # Chain(x -> Normal(μ=x), Normal(μ=0.0))
+    For(3) do j Normal(σ=j) end
+    For(2,3) do i,j Normal(i,j) end
+    # Likelihood
+    # Pointwise
+    Normal() ^ 3
+    Normal() ^ (2,3)
+    # SpikeMixture(Normal(), 2)
+    # Dirac(0.0) + Normal()
+    # transforms
+    3 * Normal()
+    Bernoulli(0.2)
+    Beta(2,3)
+    Binomial(10,0.3)
+    Cauchy()
+    Dirichlet(ones(3))
+    Exponential()
+    Gumbel()
+    # InverseGamma(2)
+    Laplace()
+    LKJCholesky(3,2.0)
+    Multinomial(n=10,p=[0.2,0.3,0.5])
+    # MvNormal
+    NegativeBinomial(5,0.2)
+    Normal(2,3)
+    Poisson(3.1)
+    StudentT(ν=2.1)    
+    Uniform()
+    # CountingMeasure(Float64)
+    Dirac(π)
+    Lebesgue(ℝ)
+    # TrivialMeasure()
+]
+
+@testset "testvalue" begin
+    for μ in measures
+        @test logdensity(μ, testvalue(μ)) isa Float64
+    end
+
+    @testset "testvalue(::Chain)" begin
+        mc =  Chain(x -> Normal(μ=x), Normal(μ=0.0))
+        r = testvalue(mc)
+        @test logdensity(mc, Iterators.take(r, 10)) isa Float64
+    end
+end
+
 @testset "Parameterized Measures" begin
     @testset "Binomial" begin
         D = Binomial{(:n, :p)}