[Jenkins] auto-formatting by clang-format version 6.0.0-1ubuntu2~16.04.1 (tags/RELEASE_600/final)

Author: stan-buildbot

stan/math/prim/prob/multi_normal_cholesky_lpdf.hpp

@@ -23,7 +23,7 @@ namespace math {
  * The log of the multivariate normal density for the given y, mu, and
  * a Cholesky factor L of the variance matrix.
  * Sigma = LL', a square, semi-positive definite matrix.
- * 
+ *
  * This version of the function is vectorized on y and mu.
  *
  * Analytic expressions taken from
@@ -122,9 +122,9 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
     logp += NEG_LOG_SQRT_TWO_PI * size_y * size_vec;
   }
 
-  if(include_summand<propto, T_y, T_loc, T_covar_elem>::value) {
+  if (include_summand<propto, T_y, T_loc, T_covar_elem>::value) {
     Eigen::Matrix<T_partials_return, Eigen::Dynamic, Eigen::Dynamic>
-    y_val_minus_mu_val(size_y, size_vec);
+        y_val_minus_mu_val(size_y, size_vec);
 
     for (size_t i = 0; i < size_vec; i++) {
       decltype(auto) y_val = as_value_column_vector_or_scalar(y_vec[i]);
@@ -134,12 +134,13 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
 
     matrix_partials_t half, scaled_diff;
 
-    // If the covariance is not autodiff, we can avoid computing a matrix inverse
-    if(is_constant<T_covar_elem>::value) {
+    // If the covariance is not autodiff, we can avoid computing a matrix
+    // inverse
+    if (is_constant<T_covar_elem>::value) {
       matrix_partials_t L_val = value_of(L_ref);
 
       half = mdivide_left_tri<Eigen::Lower>(L_val, y_val_minus_mu_val)
-                .transpose();
+                 .transpose();
 
       scaled_diff = mdivide_right_tri<Eigen::Lower>(half, L_val).transpose();
 
@@ -148,20 +149,24 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
       }
     } else {
       matrix_partials_t inv_L_val
-        = mdivide_left_tri<Eigen::Lower>(value_of(L_ref));
+          = mdivide_left_tri<Eigen::Lower>(value_of(L_ref));
 
-      half = (inv_L_val.template triangularView<Eigen::Lower>() * y_val_minus_mu_val).transpose();
+      half = (inv_L_val.template triangularView<Eigen::Lower>()
+              * y_val_minus_mu_val)
+                 .transpose();
 
-      scaled_diff = (half * inv_L_val.template triangularView<Eigen::Lower>()).transpose();
+      scaled_diff = (half * inv_L_val.template triangularView<Eigen::Lower>())
+                        .transpose();
 
       logp += sum(log(inv_L_val.diagonal())) * size_vec;
       ops_partials.edge3_.partials_ -= size_vec * inv_L_val.transpose();
 
       for (size_t i = 0; i < size_vec; i++) {
-        ops_partials.edge3_.partials_vec_[i] += scaled_diff.col(i) * half.row(i);
+        ops_partials.edge3_.partials_vec_[i]
+            += scaled_diff.col(i) * half.row(i);
       }
     }
-    
+
     logp -= 0.5 * sum(columns_dot_self(half));
 
     for (size_t i = 0; i < size_vec; i++) {
@@ -251,30 +256,32 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
     row_vector_partials_t half;
     vector_partials_t scaled_diff;
 
-    // If the covariance is not autodiff, we can avoid computing a matrix inverse
-    if(is_constant<T_covar_elem>::value) {
+    // If the covariance is not autodiff, we can avoid computing a matrix
+    // inverse
+    if (is_constant<T_covar_elem>::value) {
       matrix_partials_t L_val = value_of(L_ref);
-      
+
       half = mdivide_left_tri<Eigen::Lower>(L_val, y_val - mu_val).transpose();
 
-      scaled_diff
-          = mdivide_right_tri<Eigen::Lower>(half, L_val).transpose();
+      scaled_diff = mdivide_right_tri<Eigen::Lower>(half, L_val).transpose();
 
       if (include_summand<propto>::value) {
         logp -= sum(log(L_val.diagonal()));
       }
     } else {
       matrix_partials_t inv_L_val
-        = mdivide_left_tri<Eigen::Lower>(value_of(L_ref));
+          = mdivide_left_tri<Eigen::Lower>(value_of(L_ref));
 
       half = (inv_L_val.template triangularView<Eigen::Lower>()
-             * (y_val - mu_val).template cast<T_partials_return>())
-                .transpose();
+              * (y_val - mu_val).template cast<T_partials_return>())
+                 .transpose();
 
-      scaled_diff = (half * inv_L_val.template triangularView<Eigen::Lower>()).transpose();
+      scaled_diff = (half * inv_L_val.template triangularView<Eigen::Lower>())
+                        .transpose();
 
       logp += sum(log(inv_L_val.diagonal()));
-      ops_partials.edge3_.partials_ += scaled_diff * half - inv_L_val.transpose();
+      ops_partials.edge3_.partials_
+          += scaled_diff * half - inv_L_val.transpose();
     }
 
     logp -= 0.5 * sum(dot_self(half));