format fixups

swift-nav · Aug 5, 2024 · 9b9cb6a · 9b9cb6a
1 parent bf9fd3b
commit 9b9cb6a
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Showing 4 changed files with 12 additions and 11 deletions.
diff --git a/include/albatross/src/cereal/gp.hpp b/include/albatross/src/cereal/gp.hpp
@@ -18,8 +18,8 @@ using albatross::GaussianProcessBase;
 using albatross::GPFit;
 using albatross::LinearCombination;
 
-using albatross::SparseGPFit;
 using albatross::PICGPFit;
+using albatross::SparseGPFit;
 
 #ifndef GP_SERIALIZATION_VERSION
 #define GP_SERIALIZATION_VERSION 2

diff --git a/include/albatross/src/models/pic_gp.hpp b/include/albatross/src/models/pic_gp.hpp
@@ -681,7 +681,8 @@ class PICGaussianProcessRegression
         (xi_lambda.transpose() * xi_lambda - xi_u.transpose() * xi_u)
             .diagonal()};
 
-    const Eigen::VectorXd U_diag = (K_up.transpose() * sparse_gp_fit.W * Vp).diagonal();
+    const Eigen::VectorXd U_diag =
+        (K_up.transpose() * sparse_gp_fit.W * Vp).diagonal();
 
     Eigen::VectorXd marginal_variance(cast::to_index(features.size()));
     for (Eigen::Index i = 0; i < marginal_variance.size(); ++i) {
@@ -764,7 +765,6 @@ class PICGaussianProcessRegression
     }
     Vp.makeCompressed();
 
-
     Eigen::MatrixXd xi_lambda = sparse_gp_fit.A_ldlt.sqrt_solve(Vp);
     Eigen::MatrixXd xi_u = sparse_gp_fit.Z * Vp;
     Eigen::MatrixXd VSV{xi_lambda.transpose() * xi_lambda -

diff --git a/include/albatross/src/models/sparse_common.hpp b/include/albatross/src/models/sparse_common.hpp
@@ -86,7 +86,6 @@ struct DenseQRImplementation {
   }
 };
 
-
 } // namespace albatross
 
-#endif  // INCLUDE_ALBATROSS_MODELS_SPARSE_COMMON_H_
+#endif // INCLUDE_ALBATROSS_MODELS_SPARSE_COMMON_H_
diff --git a/tests/test_pic_gp.cc b/tests/test_pic_gp.cc
@@ -59,7 +59,6 @@ TEST(TestPicGP, TestPredictionExists) {
   EXPECT_GT(pic_pred.mean.size(), 0);
 }
 
-
 TEST(TestPicGP, ScalarEquivalence) {
   static constexpr std::size_t kNumTrainPoints = 3;
   static constexpr std::size_t kNumTestPoints = 1;
@@ -337,7 +336,8 @@ TEST(TestPicGP, PITCEquivalenceOutOfTraining) {
 
   auto test_features = linspace(10.1, 19.9, kNumTestPoints);
   auto pic_pred = pic_fit.predict_with_measurement_noise(test_features).joint();
-  auto pitc_pred = pitc_fit.predict_with_measurement_noise(test_features).joint();
+  auto pitc_pred =
+      pitc_fit.predict_with_measurement_noise(test_features).joint();
 
   EXPECT_LT(distance::wasserstein_2(pic_pred, pitc_pred), 1e-12);
 }
@@ -358,7 +358,8 @@ TEST(TestPicGP, PredictMeanEquivalent) {
 
   auto test_features = linspace(0.1, 9.9, kNumTestPoints);
   auto pic_pred = pic_fit.predict_with_measurement_noise(test_features).mean();
-  auto pic_joint_pred = pic_fit.predict_with_measurement_noise(test_features).joint();
+  auto pic_joint_pred =
+      pic_fit.predict_with_measurement_noise(test_features).joint();
 
   const double pic_mean_error = (pic_pred - pic_joint_pred.mean).norm();
   EXPECT_LT(pic_mean_error, 1e-12);
@@ -379,8 +380,10 @@ TEST(TestPicGP, PredictMarginalEquivalent) {
   auto pic_fit = pic.fit(dataset);
 
   auto test_features = linspace(0.1, 9.9, kNumTestPoints);
-  auto pic_pred = pic_fit.predict_with_measurement_noise(test_features).marginal();
-  auto pic_joint_pred = pic_fit.predict_with_measurement_noise(test_features).joint();
+  auto pic_pred =
+      pic_fit.predict_with_measurement_noise(test_features).marginal();
+  auto pic_joint_pred =
+      pic_fit.predict_with_measurement_noise(test_features).joint();
 
   const double pic_marginal_error =
       (pic_pred.mean - pic_joint_pred.mean).norm();
@@ -400,5 +403,4 @@ TEST(TestPicGP, PredictMarginalEquivalent) {
              .format(Eigen::FullPrecision);
 }
 
-
 } // namespace albatross