test: add nsamples for gradlogpartition test

ReactiveBayes · Jan 13, 2025 · 4af1f19 · 4af1f19
1 parent 66601a4
commit 4af1f19
Showing 1 changed file with 4 additions and 3 deletions.
diff --git a/test/distributions/distributions_setuptests.jl b/test/distributions/distributions_setuptests.jl
@@ -68,7 +68,8 @@ function test_exponentialfamily_interface(distribution;
     test_fisherinformation_against_hessian = true,
     test_fisherinformation_against_jacobian = true,
     test_plogpdf_interface = true,
-    option_assume_no_allocations = false
+    option_assume_no_allocations = false,
+    nsamples_for_gradlogpartition_properties = 6000
 )
     T = ExponentialFamily.exponential_family_typetag(distribution)
 
@@ -84,7 +85,7 @@ function test_exponentialfamily_interface(distribution;
     test_packing_unpacking && run_test_packing_unpacking(distribution)
     test_isproper && run_test_isproper(distribution; assume_no_allocations = option_assume_no_allocations)
     test_basic_functions && run_test_basic_functions(distribution; assume_no_allocations = option_assume_no_allocations)
-    test_gradlogpartition_properties && run_test_gradlogpartition_properties(distribution)
+    test_gradlogpartition_properties && run_test_gradlogpartition_properties(distribution, nsamples = nsamples_for_gradlogpartition_properties)
     test_fisherinformation_properties && run_test_fisherinformation_properties(distribution)
     test_fisherinformation_against_hessian && run_test_fisherinformation_against_hessian(distribution; assume_no_allocations = option_assume_no_allocations)
     test_fisherinformation_against_jacobian && run_test_fisherinformation_against_jacobian(distribution; assume_no_allocations = option_assume_no_allocations)
@@ -96,7 +97,7 @@ function run_test_plogpdf_interface(distribution)
     ef = convert(ExponentialFamily.ExponentialFamilyDistribution, distribution)
     η = getnaturalparameters(ef)
     samples = rand(StableRNG(42), distribution, 10)
-    _, _samples = ExponentialFamily.check_logpdf(variate_form(typeof(ef)), typeof(samples), eltype(samples), ef, samples)
+    _, _samples = ExponentialFamily.check_logpdf(ef, samples)
     ss_vectors = map(s -> ExponentialFamily.pack_parameters(ExponentialFamily.sufficientstatistics(ef, s)), _samples)
     unnormalized_logpdfs = map(v -> dot(v, η), ss_vectors)
     @test all(unnormalized_logpdfs ≈ map(x -> ExponentialFamily._plogpdf(ef, x, 0, 0), _samples))