Fix tests

ReactiveBayes · Sep 18, 2024 · 98de244 · 98de244
1 parent a631308
commit 98de244
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Showing 4 changed files with 14 additions and 13 deletions.
diff --git a/src/model/graphppl.jl b/src/model/graphppl.jl
@@ -225,14 +225,14 @@ function GraphPPL.default_parametrization(backend::ReactiveMPGraphPPLBackend, no
 end
 
 function GraphPPL.instantiate(::Type{ReactiveMPGraphPPLBackend})
-    return ReactiveMPGraphPPLBackend(Static.False)
+    return ReactiveMPGraphPPLBackend(Static.False())
 end
 
 function GraphPPL.instantiate(::Type{ReactiveMPGraphPPLBackend{Static.True}})
-    return ReactiveMPGraphPPLBackend(Static.True)
+    return ReactiveMPGraphPPLBackend(Static.True())
 end
 function GraphPPL.instantiate(::Type{ReactiveMPGraphPPLBackend{Static.False}})
-    return ReactiveMPGraphPPLBackend(Static.False)
+    return ReactiveMPGraphPPLBackend(Static.False())
 end
 
 # Node specific aliases

diff --git a/test/inference/inference_tests.jl b/test/inference/inference_tests.jl
@@ -227,7 +227,7 @@ end
     import RxInfer: ReactiveMPGraphPPLBackend
     import Static
 
-    n = 5  # Number of test cases
+    n = 6  # Number of test cases
 
     distribution = NormalMeanVariance(0.0, 1.0)
     dataset      = rand(distribution, n)
@@ -332,13 +332,13 @@ end
         q(ω_2) = vague(NormalMeanVariance)
         q(κ_2) = vague(NormalMeanVariance)
         q(x_1) = vague(NormalMeanVariance)
-        q(x_2) = vague(NormalMeanVariance)
+        q(x_2[1:2:n]) = vague(NormalMeanVariance)
         q(x_3) = vague(NormalMeanVariance)
     end
 
     result_2 = infer(model = hgf_2(), data = (y = dataset,), initialization = hgf_2_initialization(), constraints = MeanField(), allow_node_contraction = true)
 
-    @test result_2.posteriors[:x_1] isa Vector{<:NormalMeanVariance}
+    @test result_2.posteriors[:x_1] isa Vector{<:NormalDistributionsFamily}
 end
 
 @testitem "Test warn argument in `infer()`" begin

diff --git a/test/model/graphppl_tests.jl b/test/model/graphppl_tests.jl
@@ -92,12 +92,13 @@ end
     import RxInfer: ReactiveMPGraphPPLBackend
     import ReactiveMP: @node
     import GraphPPL
+    using Static
 
     struct CustomStochasticNode end
 
     @node CustomStochasticNode Stochastic [out, (x, aliases = [xx]), (y, aliases = [yy]), z]
 
-    backend = ReactiveMPGraphPPLBackend()
+    backend = ReactiveMPGraphPPLBackend(Static.False())
 
     @test GraphPPL.NodeBehaviour(backend, CustomStochasticNode) === GraphPPL.Stochastic()
     @test GraphPPL.NodeType(backend, CustomStochasticNode) === GraphPPL.Atomic()
@@ -135,7 +136,7 @@ end
 
     @node CustomStochasticNode Stochastic [out, (x, aliases = [xx]), (y, aliases = [yy]), z]
 
-    backend = ReactiveMPGraphPPLBackend(true)
+    backend = ReactiveMPGraphPPLBackend(Static.True())
 
     @test GraphPPL.NodeBehaviour(backend, CustomStochasticNode) === GraphPPL.Stochastic()
     @test GraphPPL.NodeType(backend, CustomStochasticNode) === GraphPPL.Atomic()

diff --git a/test/model/initialization_plugin_tests.jl b/test/model/initialization_plugin_tests.jl
@@ -95,15 +95,15 @@ end
 
     @model function gcv(κ, ω, z, x, y)
         log_σ := κ * z + ω
-        y ~ Normal(x, exp(log_σ))
+        y ~ NormalMeanVariance(x, exp(log_σ))
     end
 
     @model function gcv_collection()
-        κ ~ Normal(0, 1)
-        ω ~ Normal(0, 1)
-        z ~ Normal(0, 1)
+        κ ~ NormalMeanVariance(0, 1)
+        ω ~ NormalMeanVariance(0, 1)
+        z ~ NormalMeanVariance(0, 1)
         for i in 1:10
-            x[i] ~ Normal(0, 1)
+            x[i] ~ NormalMeanVariance(0, 1)
             y[i] ~ gcv(κ = κ, ω = ω, z = z, x = x[i])
         end
     end