Real tests

Author: ErikQQY

src/abstractmcmc.jl

@@ -290,14 +290,15 @@ function AbstractMCMC.step(
     logdensity_and_gradient = Base.Fix1(
         LogDensityProblems.logdensity_and_gradient, model.logdensity
     )
-    θ = t_old.z.θ
+    θ = copy(t_old.z.θ)
     grad = last(logdensity_and_gradient(θ))
 
     stepsize = spl.stepsize(i)
     θ .+= (stepsize / 2) .* grad .+ sqrt(stepsize) .* randn(rng, eltype(θ), length(θ))
 
     # Make new transition.
-    t = transition(rng, h, κ, t_old.z)
+    z = phasepoint(h, θ, t_old.z.r)
+    t = transition(rng, h, κ, z)
 
     # Adapt h and spl.
     tstat = stat(t)

src/constructors.jl

@@ -198,3 +198,5 @@ end
 function SGLD(stepsize, n_leapfrog; integrator=:leapfrog, metric=:diagonal)
     return SGLD(stepsize, n_leapfrog, integrator, metric)
 end
+
+sampler_eltype(sampler::SGLD) = eltype(sampler.stepsize)

src/utilities.jl

@@ -115,6 +115,7 @@ struct PolynomialStepsize{T<:Real}
         return new{T}(a, b, γ)
     end
 end
+Base.eltype(p::PolynomialStepsize{T}) where {T} = T
 
 """
     PolynomialStepsize(a[, b=0, γ=0.55])

test/abstractmcmc.jl

@@ -115,7 +115,7 @@ using Statistics: mean
     samples_sgld = AbstractMCMC.sample(
         rng,
         model,
-        hmc,
+        sgld,
         n_adapts + n_samples;
         n_adapts=n_adapts,
         initial_params=θ_init,
@@ -129,11 +129,11 @@ using Statistics: mean
     for t in samples_sgld
         t.z.θ .= invlink_gdemo(t.z.θ)
     end
-    m_est_hmc = mean(samples_sgld) do t
+    m_est_sgld = mean(samples_sgld) do t
         t.z.θ
     end
 
-    @test m_est_hmc ≈ [49 / 24, 7 / 6] atol = RNDATOL
+    @test m_est_sgld ≈ [49 / 24, 7 / 6] atol = RNDATOL
 
     samples_custom = AbstractMCMC.sample(
         rng,