Fix style check warnings

src/mlpack/methods/decision_tree/decision_tree_regressor.hpp

@@ -409,7 +409,10 @@ class DecisionTreeRegressor :
   size_t NumLeaves() const;
 
   //! Get the child of the given index.
-  const DecisionTreeRegressor& Child(const size_t i) const { return *children[i]; }
+  const DecisionTreeRegressor& Child(const size_t i) const
+  {
+    return *children[i];
+  }
   //! Modify the child of the given index (be careful!).
   DecisionTreeRegressor& Child(const size_t i) { return *children[i]; }
 

src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp

@@ -677,7 +677,8 @@ double DecisionTreeRegressor<FitnessFunction,
     // Get the number of children we will have.
     size_t numChildren = 0;
     if (datasetInfo.Type(bestDim) == data::Datatype::categorical)
-      numChildren = CategoricalSplit::NumChildren(splitPointOrPrediction, *this);
+      numChildren = CategoricalSplit::NumChildren(splitPointOrPrediction,
+          *this);
     else
       numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
 
@@ -837,7 +838,8 @@ double DecisionTreeRegressor<FitnessFunction,
   if (bestDim != data.n_rows)
   {
     // We know that the split is numeric.
-    size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
+    size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction,
+        *this);
     splitDimension = bestDim;
     dimensionType = (size_t) data::Datatype::numeric;
 

src/mlpack/methods/decision_tree/mad_gain.hpp

@@ -98,7 +98,6 @@ class MADGain
 
     return Evaluate<UseWeights>(values, weights, 0, values.n_elem);
   }
-
 };
 
 } // namespace tree