Add TensorDirichlet benchmarks

benchmark/benchmarks.jl

@@ -4,4 +4,5 @@ using ExponentialFamily.BayesBase
 
 const SUITE = BenchmarkGroup()
 
-include("benchmarks/bernoulli.jl")
+include("benchmarks/bernoulli.jl")
+include("benchmarks/tensordirichlet.jl")

benchmark/benchmarks/tensordirichlet.jl

@@ -0,0 +1,36 @@
+
+SUITE["tensordirichlet"] = BenchmarkGroup(
+    ["tensordirichlet", "distribution"],
+    "prod" => BenchmarkGroup(["prod", "multiplication"]),
+    "convert" => BenchmarkGroup(["convert"])
+)
+
+# `prod` BenchmarkGroup ========================
+SUITE["tensordirichlet"]["prod"]["Closed"] = @benchmarkable prod(ClosedProd(), left, right) setup = begin
+    left, right = TensorDirichlet(rand(5, 5, 5)), TensorDirichlet(rand(5, 5, 5))
+end
+# ==============================================
+
+# # `convert` BenchmarkGroup =====================
+# SUITE["tensordirichlet"]["convert"]["Convert from D to EF"] = @benchmarkable convert(ExponentialFamilyDistribution, dist) setup = begin
+#     dist = TensorDirichlet(rand(5, 5, 5))
+# end
+
+# SUITE["tensordirichlet"]["convert"]["Convert from EF to D"] = @benchmarkable convert(Distribution, efdist) setup = begin
+#     efdist = convert(ExponentialFamilyDistribution, TensorDirichlet(rand(5, 5, 5)))
+# end
+# ==============================================
+
+for rank in (3,)
+    for d in (2,)
+        distribution = TensorDirichlet(rand([d for _ in 1:rank]...))
+        sample = rand(d)
+        SUITE["tensordirichlet"]["mean"]["rank=$rank, d=$d"] = @benchmarkable mean($distribution)
+        SUITE["tensordirichlet"]["rand"]["rank=$rank, d=$d"] = @benchmarkable rand($distribution)
+        # SUITE["tensordirichlet"]["logpdf"]["rank=$rank, d=$d"] = @benchmarkable logpdf($distribution, $sample)
+        SUITE["tensordirichlet"]["var"]["rank=$rank, d=$d"] = @benchmarkable var($distribution)
+        SUITE["tensordirichlet"]["cov"]["rank=$rank, d=$d"] = @benchmarkable cov($distribution)
+    end
+end
+
+# ==============================================