Some styling changes

src/mlpack/core/cv/metrics/r2_score.hpp

@@ -42,6 +42,9 @@ namespace cv {
  * where @f$ \bar{y} = frac{1}{y}\sum_{i=1}^{n} y_i @f$.
  * For example, a model having R2Score = 0.85, explains 85 \% variability of
  * the response data around its mean.
+ * 
+ * @tparam AdjustedR2 If true, then the Adjusted R2 score will be used.
+ * 	Otherwise, the regular R2 score is used.
  */
 
 template<bool AdjustedR2>
@@ -49,7 +52,7 @@ class R2Score
 {
  public:
   /**
-   * Run prediction and calculate the R squared or Adjusted R sauared error.
+   * Run prediction and calculate the R squared or Adjusted R squared error.
    *
    * @param model A regression model.
    * @param data Column-major data containing test items.

src/mlpack/core/cv/metrics/r2_score_impl.hpp

@@ -18,8 +18,8 @@ namespace cv {
 template<bool AdjustedR2>
 template<typename MLAlgorithm, typename DataType, typename ResponsesType>
 double R2Score<AdjustedR2>::Evaluate(MLAlgorithm& model,
-                         const DataType& data,
-                         const ResponsesType& responses)
+                                     const DataType& data,
+                                     const ResponsesType& responses)
 {
   if (data.n_cols != responses.n_cols)
   {
@@ -50,7 +50,8 @@ double R2Score<AdjustedR2>::Evaluate(MLAlgorithm& model,
       return totalSumSquared ? 1.0 : DBL_MIN;
     // Returning adjusted R-squared.
     double rsq = 1 - (residualSumSquared / totalSumSquared);
-    return (1 - ((1 - rsq) * ((data.n_cols - 1) / (data.n_cols - data.n_rows - 1))));
+    return (1 - ((1 - rsq) * ((data.n_cols - 1) /
+        (data.n_cols - data.n_rows - 1))));
   }
   else
   {

src/mlpack/tests/cv_test.cpp

@@ -212,7 +212,6 @@ TEST_CASE("AdjR2ScoreTest", "[CVTest]")
           <= 1e-7);
 }
 
-
 /**
  * Test the mean squared error with matrix responses.
  */