spelling, wording, and doc fixes in c++ code

I was reading through the code and fixing some things in the comments.
Only a few trivial actual code changes were made to make things more
readable.

src/tree/model.h

@@ -321,9 +321,9 @@ class TreeModel {
    */
   inline void SaveModel(utils::IStream &fo) const { // NOLINT(*)
     utils::Assert(param.num_nodes == static_cast<int>(nodes.size()),
-                  "Tree::SaveModel");
+                  "TreeModel::SaveModel");
     utils::Assert(param.num_nodes == static_cast<int>(stats.size()),
-                  "Tree::SaveModel");
+                  "TreeModel::SaveModel");
     fo.Write(&param, sizeof(Param));
     utils::Assert(param.num_nodes != 0, "invalid model");
     fo.Write(BeginPtr(nodes), sizeof(Node) * nodes.size());
@@ -462,7 +462,7 @@ class TreeModel {
 
 /*! \brief node statistics used in regression tree */
 struct RTreeNodeStat {
-  /*! \brief loss chg caused by current split */
+  /*! \brief loss change caused by current split */
   float loss_chg;
   /*! \brief sum of hessian values, used to measure coverage of data */
   float sum_hess;
@@ -485,7 +485,7 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
  public:
   /*!
    * \brief dense feature vector that can be taken by RegTree
-   * to do tranverse efficiently
+   * to do traverse efficiently
    * and can be construct from sparse feature vector
    */
   struct FVec {
@@ -498,7 +498,7 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
       int flag;
     };
     std::vector<Entry> data;
-    /*! \brief intialize the vector with size vector */
+    /*! \brief initialize the vector with size vector */
     inline void Init(size_t size) {
       Entry e; e.flag = -1;
       data.resize(size);
@@ -529,14 +529,14 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
   };
   /*!
    * \brief get the leaf index
-   * \param feats dense feature vector, if the feature is missing the field is set to NaN
-   * \param root_gid starting root index of the instance
+   * \param feat dense feature vector, if the feature is missing the field is set to NaN
+   * \param root_id starting root index of the instance
    * \return the leaf index of the given feature
    */
-  inline int GetLeafIndex(const FVec&feat, unsigned root_id = 0) const {
+  inline int GetLeafIndex(const FVec &feat, unsigned root_id = 0) const {
     // start from groups that belongs to current data
     int pid = static_cast<int>(root_id);
-    // tranverse tree
+    // traverse tree
     while (!(*this)[ pid ].is_leaf()) {
       unsigned split_index = (*this)[pid].split_index();
       pid = this->GetNext(pid, feat.fvalue(split_index), feat.is_missing(split_index));
@@ -546,7 +546,7 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
   /*!
    * \brief get the prediction of regression tree, only accepts dense feature vector
    * \param feats dense feature vector, if the feature is missing the field is set to NaN
-   * \param root_gid starting root index of the instance
+   * \param root_id starting root index of the instance
    * \return the leaf index of the given feature
    */
   inline float Predict(const FVec &feat, unsigned root_id = 0) const {

src/tree/param.h

@@ -32,7 +32,7 @@ struct TrainParam{
   // default direction choice
   int default_direction;
   // maximum delta update we can add in weight estimation
-  // this parameter can be used to stablize update
+  // this parameter can be used to stabilize update
   // default=0 means no constraint on weight delta
   float max_delta_step;
   // whether we want to do subsample
@@ -51,7 +51,7 @@ struct TrainParam{
   int size_leaf_vector;
   // option for parallelization
   int parallel_option;
-  // option to open cacheline optimizaton
+  // option to open cacheline optimization
   int cache_opt;
   // number of threads to be used for tree construction,
   // if OpenMP is enabled, if equals 0, use system default
@@ -132,7 +132,7 @@ struct TrainParam{
       }
     }
   }
-  // calculate cost of loss function with four stati
+  // calculate cost of loss function with four statistics
   inline double CalcGain(double sum_grad, double sum_hess,
                          double test_grad, double test_hess) const {
     double w = CalcWeight(sum_grad, sum_hess);
@@ -167,7 +167,7 @@ struct TrainParam{
   inline bool need_backward_search(float col_density, bool indicator) const {
     return this->default_direction != 2;
   }
-  /*! \brief given the loss change, whether we need to invode prunning */
+  /*! \brief given the loss change, whether we need to invoke pruning */
   inline bool need_prune(double loss_chg, int depth) const {
     return loss_chg < this->min_split_loss;
   }
@@ -235,7 +235,7 @@ struct GradStats {
     const bst_gpair &b = gpair[ridx];
     this->Add(b.grad, b.hess);
   }
-  /*! \brief caculate leaf weight */
+  /*! \brief calculate leaf weight */
   inline double CalcWeight(const TrainParam &param) const {
     return param.CalcWeight(sum_grad, sum_hess);
   }
@@ -362,10 +362,10 @@ struct SplitEntry{
   /*! \brief constructor */
   SplitEntry(void) : loss_chg(0.0f), sindex(0), split_value(0.0f) {}
   /*!
-   * \brief decides whether a we can replace current entry with the statistics given
-   *   This function gives better priority to lower index when loss_chg equals
-   *    not the best way, but helps to give consistent result during multi-thread execution
-   * \param loss_chg the loss reduction get through the split
+   * \brief decides whether we can replace current entry with the given statistics
+   *   This function gives better priority to lower index when loss_chg == new_loss_chg.
+   *   Not the best way, but helps to give consistent result during multi-thread execution.
+   * \param new_loss_chg the loss reduction get through the split
    * \param split_index the feature index where the split is on
    */
   inline bool NeedReplace(bst_float new_loss_chg, unsigned split_index) const {
@@ -392,9 +392,9 @@ struct SplitEntry{
   }
   /*!
    * \brief update the split entry, replace it if e is better
-   * \param loss_chg loss reduction of new candidate
+   * \param new_loss_chg loss reduction of new candidate
    * \param split_index feature index to split on
-   * \param split_value the split point
+   * \param new_split_value the split point
    * \param default_left whether the missing value goes to left
    * \return whether the proposed split is better and can replace current split
    */

src/tree/updater.h

@@ -26,11 +26,11 @@ class IUpdater {
    */
   virtual void SetParam(const char *name, const char *val) = 0;
   /*!
-   * \brief peform update to the tree models
+   * \brief perform update to the tree models
    * \param gpair the gradient pair statistics of the data
    * \param p_fmat feature matrix that provide access to features
    * \param info extra side information that may be need, such as root index
-   * \param trees pointer to the trees to be updated, upater will change the content of the tree
+   * \param trees references the trees to be updated, updater will change the content of trees
    *   note: all the trees in the vector are updated, with the same statistics,
    *         but maybe different random seeds, usually one tree is passed in at a time,
    *         there can be multiple trees when we train random forest style model
@@ -53,7 +53,7 @@ class IUpdater {
   virtual ~IUpdater(void) {}
 };
 /*!
- * \brief create a updater based on name
+ * \brief create an updater based on name
  * \param name name of updater
  * \return return the updater instance
  */

src/tree/updater_colmaker-inl.hpp

@@ -17,7 +17,7 @@
 
 namespace xgboost {
 namespace tree {
-/*! \brief colunwise update to construct a tree */
+/*! \brief column-wise update to construct a tree */
 template<typename TStats>
 class ColMaker: public IUpdater {
  public:

src/tree/updater_prune-inl.hpp

@@ -14,7 +14,7 @@
 
 namespace xgboost {
 namespace tree {
-/*! \brief pruner that prunes a tree after growing finishs */
+/*! \brief pruner that prunes a tree after growing finishes */
 class TreePruner: public IUpdater {
  public:
   virtual ~TreePruner(void) {}
@@ -56,7 +56,7 @@ class TreePruner: public IUpdater {
       return npruned;
     }
   }
-  /*! \brief do prunning of a tree */
+  /*! \brief do pruning of a tree */
   inline void DoPrune(RegTree &tree) { // NOLINT(*)
     int npruned = 0;
     // initialize auxiliary statistics
@@ -69,7 +69,7 @@ class TreePruner: public IUpdater {
       }
     }
     if (silent == 0) {
-      utils::Printf("tree prunning end, %d roots, %d extra nodes, %d pruned nodes ,max_depth=%d\n",
+      utils::Printf("tree pruning end, %d roots, %d extra nodes, %d pruned nodes ,max_depth=%d\n",
                     tree.param.num_roots, tree.num_extra_nodes(), npruned, tree.MaxDepth());
     }
   }