style: 🎨

test/ext/ReactiveMPProjectionExt/rules/marginals_tests.jl

@@ -120,14 +120,14 @@ end
         rng = MersenneTwister(123)
         method = CVIProjection(rng = rng, marginalsamples = 2000)
         meta = DeltaMeta(method = method, inverse = nothing)
-        
+
         f(x, y) = x * y
-        
+
         # Define distributions
         m_out = NormalMeanVariance(2.0, 0.1)
         m_in1 = NormalMeanVariance(0.0, 2.0)
         m_in2 = NormalMeanVariance(0.0, 2.0)
-        
+
         # Function to compute unnormalized log posterior for a sample
         function log_posterior(x, y)
             return logpdf(m_in1, x) + logpdf(m_in2, y) + logpdf(m_out, f(x, y))
@@ -137,24 +137,20 @@ end
         function estimate_kl_divergence(q_result)
             n_samples = 10000
             samples_q = [(rand(rng, q_result[1]), rand(rng, q_result[2])) for _ in 1:n_samples]
-            
+
             # Compute E_q[log q(x,y) - log p(x,y)]
             log_q_terms = [logpdf(q_result[1], x) + logpdf(q_result[2], y) for (x, y) in samples_q]
             log_p_terms = [log_posterior(x, y) for (x, y) in samples_q]
-            
+
             return mean(log_q_terms .- log_p_terms)
         end
 
         # Run multiple iterations and collect KL divergences
         n_iterations = 10
         kl_divergences = Vector{Float64}(undef, n_iterations)
-        
+
         for i in 1:n_iterations
-            result = @call_marginalrule DeltaFn{f}(:ins) (
-                m_out = m_out, 
-                m_ins = ManyOf(m_in1, m_in2), 
-                meta = meta
-            )
+            result = @call_marginalrule DeltaFn{f}(:ins) (m_out = m_out, m_ins = ManyOf(m_in1, m_in2), meta = meta)
             kl_divergences[i] = estimate_kl_divergence(result)
         end
 
@@ -180,7 +176,7 @@ end
     using Test
     using BenchmarkTools
     using BayesBase, ExponentialFamily, ExponentialFamilyProjection
-    
+
     f(x, y) = [x, y]
 
     function run_marginal_test(strategy)
@@ -189,11 +185,7 @@ end
         m_in1 = NormalMeanVariance(0.0, 2.0)
         m_in2 = NormalMeanVariance(0.0, 2.0)
         return @belapsed begin
-            @call_marginalrule DeltaFn{f}(:ins) (
-                m_out = $m_out, 
-                m_ins = ManyOf($m_in1, $m_in2), 
-                meta = $meta
-            )
+            @call_marginalrule DeltaFn{f}(:ins) (m_out = $m_out, m_ins = ManyOf($m_in1, $m_in2), meta = $meta)
         end samples = 2
     end
 
@@ -204,5 +196,5 @@ end
     @test mean_time < full_time
 
     # Optional: Print the actual times for verification
-    @info "Sampling strategy performance" full_time mean_time ratio=(full_time/mean_time)
+    @info "Sampling strategy performance" full_time mean_time ratio = (full_time / mean_time)
 end