[Jenkins] auto-formatting by clang-format version 10.0.0-4ubuntu1

Author: stan-buildbot

stan/math/rev/core/var.hpp

@@ -1022,7 +1022,8 @@ class var_value<T, internal::require_matrix_var_value<T>> {
    * @param other the value to assign
    * @return this
    */
-  template <typename S, typename T_ = T, require_assignable_t<value_type, S>* = nullptr,
+  template <typename S, typename T_ = T,
+            require_assignable_t<value_type, S>* = nullptr,
             require_all_plain_type_t<T_, S>* = nullptr,
             require_not_same_t<plain_type_t<T_>, plain_type_t<S>>* = nullptr>
   inline var_value<T>& operator=(const var_value<S>& other) {
@@ -1049,7 +1050,7 @@ class var_value<T, internal::require_matrix_var_value<T>> {
     if (!(this->vi_)) {
       *this = var_value<T>(other);
       return *this;
-    } 
+    }
     arena_t<plain_type_t<T>> prev_val(vi_->val_.rows(), vi_->val_.cols());
     prev_val.hard_copy(vi_->val_);
     vi_->val_.hard_copy(other.val());
@@ -1086,11 +1087,11 @@ class var_value<T, internal::require_matrix_var_value<T>> {
     if (!(this->vi_)) {
       []() STAN_COLD_PATH {
         throw std::domain_error(
-          "var_value<matrix>::operator=(var_value<expression>):"
-          " Internal Bug! Please report this with an example"
-          " of your model to the Stan math github repository.");
+            "var_value<matrix>::operator=(var_value<expression>):"
+            " Internal Bug! Please report this with an example"
+            " of your model to the Stan math github repository.");
       }();
-    } 
+    }
     arena_t<plain_type_t<T>> prev_val = vi_->val_;
     vi_->val_ = other.val();
     // no need to change any adjoints - these are just zeros before the reverse
@@ -1111,7 +1112,6 @@ class var_value<T, internal::require_matrix_var_value<T>> {
     return *this;
   }
 
-
   /**
    * No-op to match with Eigen methods which call eval
    */

test/unit/math/rev/core/var_test.cpp

@@ -912,47 +912,48 @@ TEST_F(AgradRev, matrix_compile_time_conversions) {
 }
 
 TEST_F(AgradRev, assign_nan) {
-    using stan::math::var_value;
-    using var_vector = var_value<Eigen::Matrix<double,-1,1>>;
-    using stan::math::var;
-    Eigen::VectorXd x_val(10);
-    for (int i = 0; i < 10; ++i) {
-        x_val(i) = i + 0.1;
-    }
-    var_vector x(x_val);
-    var_vector y = var_vector(Eigen::Matrix<double,-1,1>::Constant(10, std::numeric_limits<double>::quiet_NaN()));
-    y = stan::math::head(x, 10);
-    var sigma = 1.0;
-    var lp = stan::math::normal_lpdf<false>(y, 0, sigma);
-    lp.grad();
-    Eigen::VectorXd x_ans_adj(10);
-    for (int i = 0; i < 10; ++i) {
-        x_ans_adj(i) = -(i + 0.1);
-    }
-    EXPECT_MATRIX_EQ(x.adj(), x_ans_adj);
-    Eigen::VectorXd y_ans_adj = Eigen::VectorXd::Zero(10);
-    EXPECT_MATRIX_EQ(y_ans_adj, y.adj());
+  using stan::math::var_value;
+  using var_vector = var_value<Eigen::Matrix<double, -1, 1>>;
+  using stan::math::var;
+  Eigen::VectorXd x_val(10);
+  for (int i = 0; i < 10; ++i) {
+    x_val(i) = i + 0.1;
+  }
+  var_vector x(x_val);
+  var_vector y = var_vector(Eigen::Matrix<double, -1, 1>::Constant(
+      10, std::numeric_limits<double>::quiet_NaN()));
+  y = stan::math::head(x, 10);
+  var sigma = 1.0;
+  var lp = stan::math::normal_lpdf<false>(y, 0, sigma);
+  lp.grad();
+  Eigen::VectorXd x_ans_adj(10);
+  for (int i = 0; i < 10; ++i) {
+    x_ans_adj(i) = -(i + 0.1);
+  }
+  EXPECT_MATRIX_EQ(x.adj(), x_ans_adj);
+  Eigen::VectorXd y_ans_adj = Eigen::VectorXd::Zero(10);
+  EXPECT_MATRIX_EQ(y_ans_adj, y.adj());
 }
 
 TEST_F(AgradRev, assign_nullptr_vari) {
-    using stan::math::var_value;
-    using var_vector = var_value<Eigen::Matrix<double,-1,1>>;
-    using stan::math::var;
-    Eigen::VectorXd x_val(10);
-    for (int i = 0; i < 10; ++i) {
-        x_val(i) = i + 0.1;
-    }
-    var_vector x(x_val);
-    var_vector y;
-    y = stan::math::head(x, 10);
-    var sigma = 1.0;
-    var lp = stan::math::normal_lpdf<false>(y, 0, sigma);
-    lp.grad();
-    Eigen::VectorXd x_ans_adj(10);
-    for (int i = 0; i < 10; ++i) {
-        x_ans_adj(i) = -(i + 0.1);
-    }
-    EXPECT_MATRIX_EQ(x.adj(), x_ans_adj);
-    Eigen::VectorXd y_ans_adj = Eigen::VectorXd::Zero(10);
-    EXPECT_MATRIX_EQ(y_ans_adj, y.adj());
+  using stan::math::var_value;
+  using var_vector = var_value<Eigen::Matrix<double, -1, 1>>;
+  using stan::math::var;
+  Eigen::VectorXd x_val(10);
+  for (int i = 0; i < 10; ++i) {
+    x_val(i) = i + 0.1;
+  }
+  var_vector x(x_val);
+  var_vector y;
+  y = stan::math::head(x, 10);
+  var sigma = 1.0;
+  var lp = stan::math::normal_lpdf<false>(y, 0, sigma);
+  lp.grad();
+  Eigen::VectorXd x_ans_adj(10);
+  for (int i = 0; i < 10; ++i) {
+    x_ans_adj(i) = -(i + 0.1);
+  }
+  EXPECT_MATRIX_EQ(x.adj(), x_ans_adj);
+  Eigen::VectorXd y_ans_adj = Eigen::VectorXd::Zero(10);
+  EXPECT_MATRIX_EQ(y_ans_adj, y.adj());
 }