predict for Disitributions (#295)

* `predict` for Disitributions

* add tests

* bump version

* small update to test

Author: cscherrer

Project.toml

@@ -1,7 +1,7 @@
 name = "Soss"
 uuid = "8ce77f84-9b61-11e8-39ff-d17a774bf41c"
 author = ["Chad Scherrer <[email protected]>"]
-version = "0.20.2"
+version = "0.20.3"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"

src/transforms/predict.jl

@@ -4,6 +4,7 @@ using TupleVectors
 predict(m::AbstractModel, args...) = predict(Random.GLOBAL_RNG, m, args...)
 predict(d::AbstractMeasure, x) = x
 
+predict(d::Dists.Distribution, x) = x
 
 @inline function predict(rng::AbstractRNG, m::AbstractModel, nt::NamedTuple{N}) where {N}
     pred = predictive(Model(m), N...)

test/runtests.jl

@@ -132,5 +132,22 @@ include("examples-list.jl")
 
         @test transform(xform(post), randn(6)) isa NamedTuple
 
+        @testset "logdensity" begin
+            dat = randn(100)
+            m = Soss.@model n begin
+                μ ~ Dists.Normal()
+                σ ~ Dists.Exponential()
+                data ~ Dists.Normal(μ, σ) |> iid(n)
+                return (; data)
+            end
+            mod = m( (; n = length(dat) ) )
+            post = mod | (data = dat,)
+
+            @test logdensity( mod( (μ = 1., σ = 2., data = dat) ) ) == logdensity( post( (μ = 1., σ = 2.) ) )
+        end
+
+
     end
+
+
 end