Modernize typedefs in docs

doc/developer/elemtype.md

@@ -28,7 +28,7 @@ types cannot be used.
 easily defined as below:
 
 ```c++
-typedef typename MatType::elem_type ElemType;
+using ElemType = typename MatType::elem_type;
 ```
 
 and otherwise a template parameter with the name `ElemType` can be used.  It is

doc/developer/trees.md

@@ -202,7 +202,7 @@ class ExampleTree
  public:
   // This is the element type held by the matrix.
   // It will generally either be `double`, or `float`.
-  typedef typename MatType::elem_type ElemType;
+  using ElemType = typename MatType::elem_type;
 
   //////////////////////
   //// Constructors ////

doc/tutorials/fastmks.md

@@ -386,8 +386,9 @@ IPMetric<PolynomialKernel> metric(pk);
 // the custom base of 1.5 (default is 1.3).  We have to be sure to use the right
 // type here -- FastMKS needs the FastMKSStat object as the tree's
 // StatisticType.
-typedef CoverTree<IPMetric<PolynomialKernel>, FirstPointIsRoot, FastMKSStat>
-    TreeType; // Convenience typedef.
+// Convenience typedef.
+using TreeType =
+    CoverTree<IPMetric<PolynomialKernel>, FirstPointIsRoot, FastMKSStat>;
 TreeType* tree = new TreeType(data, metric, 1.5);
 
 // Now initialize FastMKS with that statistic.  We don't need to specify the
@@ -455,7 +456,7 @@ extern arma::mat data;
 
 // The custom tree type.  We'll assume that the first template parameter is the
 // statistic type.
-typedef CustomTree<FastMKSStat> TreeType;
+using TreeType = CustomTree<FastMKSStat>;
 
 // The FastMKS constructor will create the tree.
 FastMKS<LinearKernel, arma::mat, TreeType> f(data);

doc/tutorials/neighbor_search.md

@@ -213,7 +213,7 @@ The `KNN` class is, specifically, a typedef of the more extensible
 distance.
 
 ```c++
-typedef NeighborSearch<NearestNeighborSort, EuclideanDistance> KNN;
+using KNN = NeighborSearch<NearestNeighborSort, EuclideanDistance>;
 ```
 
 Using the `KNN` class is particularly simple; first, the object must be

doc/user/core/trees/ball_tree.md

@@ -553,9 +553,9 @@ find the number of leaf nodes with fewer than 10 children.
 // above).
 
 // This convenient typedef saves us a long type name!
-typedef mlpack::BallTree<mlpack::EuclideanDistance,
-                         mlpack::EmptyStatistic,
-                         arma::fmat> TreeType;
+using TreeType = mlpack::BallTree<mlpack::EuclideanDistance,
+                                  mlpack::EmptyStatistic,
+                                  arma::fmat>;
 
 TreeType tree;
 mlpack::data::Load("tree.bin", "tree", tree);

doc/user/core/trees/binary_space_tree.md

@@ -1850,11 +1850,11 @@ arma::mat dataset;
 mlpack::data::Load("corel-histogram.csv", dataset, true);
 
 // Convenience typedef for the tree type.
-typedef mlpack::BinarySpaceTree<mlpack::EuclideanDistance,
-                                mlpack::EmptyStatistic,
-                                arma::mat,
-                                mlpack::HRectBound,
-                                mlpack::MidpointSplit> TreeType;
+using TreeType = mlpack::BinarySpaceTree<mlpack::EuclideanDistance,
+                                         mlpack::EmptyStatistic,
+                                         arma::mat,
+                                         mlpack::HRectBound,
+                                         mlpack::MidpointSplit>;
 
 // Build trees on the first half and the second half of points.
 TreeType tree1(dataset.cols(0, dataset.n_cols / 2));
@@ -1947,11 +1947,11 @@ manually and find the number of leaf nodes with less than 10 children.
 // above).
 
 // This convenient typedef saves us a long type name!
-typedef mlpack::BinarySpaceTree<mlpack::EuclideanDistance,
-                                mlpack::EmptyStatistic,
-                                arma::fmat,
-                                mlpack::HRectBound,
-                                mlpack::MidpointSplit> TreeType;
+using TreeType = mlpack::BinarySpaceTree<mlpack::EuclideanDistance,
+                                         mlpack::EmptyStatistic,
+                                         arma::fmat,
+                                         mlpack::HRectBound,
+                                         mlpack::MidpointSplit>;
 
 TreeType tree;
 mlpack::data::Load("tree.bin", "tree", tree);

doc/user/core/trees/kdtree.md

@@ -547,9 +547,9 @@ find the number of leaf nodes with fewer than 10 children.
 // above).
 
 // This convenient typedef saves us a long type name!
-typedef mlpack::KDTree<mlpack::EuclideanDistance,
-                       mlpack::EmptyStatistic,
-                       arma::fmat> TreeType;
+using TreeType = mlpack::KDTree<mlpack::EuclideanDistance,
+                                mlpack::EmptyStatistic,
+                                arma::fmat>;
 
 TreeType tree;
 mlpack::data::Load("tree.bin", "tree", tree);

doc/user/core/trees/mean_split_ball_tree.md

@@ -551,9 +551,9 @@ find the number of leaf nodes with fewer than 10 children.
 // above).
 
 // This convenient typedef saves us a long type name!
-typedef mlpack::MeanSplitBallTree<mlpack::EuclideanDistance,
-                                  mlpack::EmptyStatistic,
-                                  arma::fmat> TreeType;
+using TreeType = mlpack::MeanSplitBallTree<mlpack::EuclideanDistance,
+                                           mlpack::EmptyStatistic,
+                                           arma::fmat>;
 
 TreeType tree;
 mlpack::data::Load("tree.bin", "tree", tree);

doc/user/core/trees/mean_split_kdtree.md

@@ -560,9 +560,9 @@ manually and find the number of leaf nodes with fewer than 10 children.
 // above).
 
 // This convenient typedef saves us a long type name!
-typedef mlpack::MeanSplitKDTree<mlpack::EuclideanDistance,
-                                mlpack::EmptyStatistic,
-                                arma::fmat> TreeType;
+using TreeType = mlpack::MeanSplitKDTree<mlpack::EuclideanDistance,
+                                         mlpack::EmptyStatistic,
+                                         arma::fmat>;
 
 TreeType tree;
 mlpack::data::Load("tree.bin", "tree", tree);

doc/user/core/trees/vptree.md

@@ -558,9 +558,9 @@ find the number of leaf nodes with less than 10 children.
 // above).
 
 // This convenient typedef saves us a long type name!
-typedef mlpack::VPTree<mlpack::EuclideanDistance,
-                       mlpack::EmptyStatistic,
-                       arma::fmat> TreeType;
+using TreeType = mlpack::VPTree<mlpack::EuclideanDistance,
+                                mlpack::EmptyStatistic,
+                                arma::fmat>;
 
 TreeType tree;
 mlpack::data::Load("tree.bin", "tree", tree);

doc/user/matrices.md

@@ -256,10 +256,10 @@ arma::Row<size_t> labels =
 
 // Train in the constructor, using floating-point data.
 // The weak learner type is now a floating-point Perceptron.
-typedef mlpack::Perceptron<
+using PerceptronType = mlpack::Perceptron<
     mlpack::SimpleWeightUpdate,
     mlpack::ZeroInitialization,
-    arma::fmat> PerceptronType;
+    arma::fmat>;
 mlpack::AdaBoost<PerceptronType, arma::fmat> ab(dataset, labels, 5);
 
 // Create test data (500 points).

doc/user/methods/adaboost.md

@@ -418,9 +418,9 @@ arma::Row<size_t> labels =
 
 // Train in the constructor, using floating-point data.
 // The weak learner type is now a floating-point Perceptron.
-typedef mlpack::Perceptron<mlpack::SimpleWeightUpdate,
-                           mlpack::ZeroInitialization,
-                           arma::fmat> PerceptronType;
+using PerceptronType = mlpack::Perceptron<mlpack::SimpleWeightUpdate,
+                                          mlpack::ZeroInitialization,
+                                          arma::fmat>;
 mlpack::AdaBoost<PerceptronType, arma::fmat> ab(dataset, labels, 5);
 
 // Create test data (500 points).

doc/user/methods/amf.md

@@ -659,8 +659,9 @@ mlpack::RandomAcolInitialization<5> initW;
 mlpack::RandomAMFInitialization initH;
 
 // Combine the two initializations so we can pass it to the AMF class.
-typedef mlpack::MergeInitialization<mlpack::RandomAcolInitialization<5>,
-                                    mlpack::RandomAMFInitialization> InitType;
+using InitType =
+    mlpack::MergeInitialization<mlpack::RandomAcolInitialization<5>,
+                                mlpack::RandomAMFInitialization>;
 InitType init(initW, initH);
 
 // Create an AMF object with the custom initialization.