updating test; test was incorrect. It passed in a non-cholesky factor…

…. New code properly catches the error.
stan-dev · Feb 23, 2024 · 44920e1 · 44920e1
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diff --git a/test/unit/math/mix/prob/inv_wishart_cholesky_test.cpp b/test/unit/math/mix/prob/inv_wishart_cholesky_test.cpp
@@ -1,37 +1,44 @@
 #include <test/unit/math/test_ad.hpp>
 
 TEST(ProbDistributionsInvWishartCholesky, matvar) {
-  auto f = [](const auto& L_Y, const auto& dof, const auto& L_S) {
-    return stan::math::inv_wishart_cholesky_lpdf(L_Y, dof, L_S);
+  auto f = [](const auto& Y, const auto& dof, const auto& Sigma) {
+    auto symmetric_Y = ((Y + Y.transpose()) * 0.5).eval();
+    auto symmetric_Sigma = ((Sigma + Sigma.transpose()) * 0.5).eval();
+
+    auto L_Y = stan::math::cholesky_decompose(symmetric_Y);
+    stan::plain_type_t<decltype(L_Y)> L_Y_plain = L_Y;
+
+    auto L_S = stan::math::cholesky_decompose(symmetric_Sigma);
+    stan::plain_type_t<decltype(L_S)> L_S_plain = L_S;
+
+    return stan::math::inv_wishart_cholesky_lpdf(L_Y, dof, L_S_plain);
   };
 
   double dof = 3.4;
-  Eigen::MatrixXd L_Y11(1, 1);
-  L_Y11 << 1;
-  Eigen::MatrixXd L_S11(1, 1);
-  L_S11 << 1;
-  stan::test::expect_ad(f, L_Y11, dof, L_S11);
-  stan::test::expect_ad_matvar(f, L_Y11, dof, L_S11);
-
-  Eigen::MatrixXd L_Y00(0, 0);
-  Eigen::MatrixXd L_S00(0, 0);
-  stan::test::expect_ad(f, L_Y00, dof, L_S00);
-  stan::test::expect_ad_matvar(f, L_Y00, dof, L_S00);
-
-  Eigen::MatrixXd y22(2, 2);
-  y22 << 1.0, 0.1, 0.1, 1.5;
-  Eigen::MatrixXd L_Y22 = y22.llt().matrixL();
+  Eigen::MatrixXd Y11(1, 1);
+  Y11 << 1;
+  Eigen::MatrixXd Sigma11(1, 1);
+  Sigma11 << 1;
+  stan::test::expect_ad(f, Y11, dof, Sigma11);
+  stan::test::expect_ad_matvar(f, Y11, dof, Sigma11);
+
+  Eigen::MatrixXd Y00(0, 0);
+  Eigen::MatrixXd Sigma00(0, 0);
+  stan::test::expect_ad(f, Y00, dof, Sigma00);
+  stan::test::expect_ad_matvar(f, Y00, dof, Sigma00);
+
+  Eigen::MatrixXd Y22(2, 2);
+  Y22 << 1.0, 0.1, 0.1, 1.5;
   Eigen::MatrixXd Sigma22(2, 2);
   Sigma22 << 1.5, 0.5, 0.5, 1.1;
-  Eigen::MatrixXd L_S22 = Sigma22.llt().matrixL();
-  stan::test::expect_ad(f, L_Y22, dof, L_S22);
-  stan::test::expect_ad_matvar(f, L_Y22, dof, L_S22);
+  stan::test::expect_ad(f, Y22, dof, Sigma22);
+  stan::test::expect_ad_matvar(f, Y22, dof, Sigma22);
 
   // Error sizes
-  stan::test::expect_ad(f, L_Y00, dof, L_S11);
-  stan::test::expect_ad(f, L_Y11, dof, L_S00);
-  stan::test::expect_ad_matvar(f, L_Y00, dof, L_S11);
-  stan::test::expect_ad_matvar(f, L_Y11, dof, L_S00);
+  stan::test::expect_ad(f, Y00, dof, Sigma11);
+  stan::test::expect_ad(f, Y11, dof, Sigma00);
+  stan::test::expect_ad_matvar(f, Y00, dof, Sigma11);
+  stan::test::expect_ad_matvar(f, Y11, dof, Sigma00);
 }
 
 TEST(ProbDistributionsInvWishartCholesky, fvar_var) {

diff --git a/test/unit/math/mix/prob/wishart_cholesky_test.cpp b/test/unit/math/mix/prob/wishart_cholesky_test.cpp
@@ -1,37 +1,44 @@
 #include <test/unit/math/test_ad.hpp>
 
 TEST(ProbDistributionsWishartCholesky, matvar) {
-  auto f = [](const auto& L_Y, const auto& dof, const auto& L_S) {
-    return stan::math::wishart_cholesky_lpdf(L_Y, dof, L_S);
+  auto f = [](const auto& Y, const auto& dof, const auto& Sigma) {
+    auto symmetric_Y = ((Y + Y.transpose()) * 0.5).eval();
+    auto symmetric_Sigma = ((Sigma + Sigma.transpose()) * 0.5).eval();
+
+    auto L_Y = stan::math::cholesky_decompose(symmetric_Y);
+    stan::plain_type_t<decltype(L_Y)> L_Y_plain = L_Y;
+
+    auto L_S = stan::math::cholesky_decompose(symmetric_Sigma);
+    stan::plain_type_t<decltype(L_S)> L_S_plain = L_S;
+
+    return stan::math::wishart_cholesky_lpdf(L_Y, dof, L_S_plain);
   };
 
   double dof = 3.4;
-  Eigen::MatrixXd L_Y11(1, 1);
-  L_Y11 << 1;
-  Eigen::MatrixXd L_S11(1, 1);
-  L_S11 << 1;
-  stan::test::expect_ad(f, L_Y11, dof, L_S11);
-  stan::test::expect_ad_matvar(f, L_Y11, dof, L_S11);
-
-  Eigen::MatrixXd L_Y00(0, 0);
-  Eigen::MatrixXd L_S00(0, 0);
-  stan::test::expect_ad(f, L_Y00, dof, L_S00);
-  stan::test::expect_ad_matvar(f, L_Y00, dof, L_S00);
-
-  Eigen::MatrixXd y22(2, 2);
-  y22 << 1.0, 0.1, 0.1, 1.5;
-  Eigen::MatrixXd L_Y22 = y22.llt().matrixL();
+  Eigen::MatrixXd Y11(1, 1);
+  Y11 << 1;
+  Eigen::MatrixXd Sigma11(1, 1);
+  Sigma11 << 1;
+  stan::test::expect_ad(f, Y11, dof, Sigma11);
+  stan::test::expect_ad_matvar(f, Y11, dof, Sigma11);
+
+  Eigen::MatrixXd Y00(0, 0);
+  Eigen::MatrixXd Sigma00(0, 0);
+  stan::test::expect_ad(f, Y00, dof, Sigma00);
+  stan::test::expect_ad_matvar(f, Y00, dof, Sigma00);
+
+  Eigen::MatrixXd Y22(2, 2);
+  Y22 << 1.0, 0.1, 0.1, 1.5;
   Eigen::MatrixXd Sigma22(2, 2);
   Sigma22 << 1.5, 0.5, 0.5, 1.1;
-  Eigen::MatrixXd L_S22 = Sigma22.llt().matrixL();
-  stan::test::expect_ad(f, L_Y22, dof, L_S22);
-  stan::test::expect_ad_matvar(f, L_Y22, dof, L_S22);
+  stan::test::expect_ad(f, Y22, dof, Sigma22);
+  stan::test::expect_ad_matvar(f, Y22, dof, Sigma22);
 
   // Error sizes
-  stan::test::expect_ad(f, L_Y00, dof, L_S11);
-  stan::test::expect_ad(f, L_Y11, dof, L_S00);
-  stan::test::expect_ad_matvar(f, L_Y00, dof, L_S11);
-  stan::test::expect_ad_matvar(f, L_Y11, dof, L_S00);
+  stan::test::expect_ad(f, Y00, dof, Sigma11);
+  stan::test::expect_ad(f, Y11, dof, Sigma00);
+  stan::test::expect_ad_matvar(f, Y00, dof, Sigma11);
+  stan::test::expect_ad_matvar(f, Y11, dof, Sigma00);
 }
 
 TEST(ProbDistributionsWishartCholesky, fvar_var) {

diff --git a/test/unit/math/opencl/rev/multi_normal_cholesky_lpdf_test.cpp b/test/unit/math/opencl/rev/multi_normal_cholesky_lpdf_test.cpp
@@ -105,7 +105,7 @@ TEST(ProbDistributionsMultiNormalCholesky, opencl_matches_cpu_small) {
   std::vector<Eigen::VectorXd> mu3{mu1, mu2};
   std::vector<Eigen::RowVectorXd> mu4{mu2, mu1};
   Eigen::MatrixXd L(N, N);
-  L << 1, 0, 0, 2, 3, 0, -4, 5, -6;
+  L << 4, 0, 0, -1, 1, 0, -4, -2, 3;
 
   stan::math::test::compare_cpu_opencl_prim_rev(
       multi_normal_cholesky_lpdf_functor, y1, mu1, L);