fix typos (#361)

src/boosting/boosting.cpp

@@ -48,11 +48,11 @@ Boosting* Boosting::CreateBoosting(const std::string& type, const char* filename
       } else if (type == std::string("goss")) {
         ret.reset(new GOSS());
       } else {
-        Log::Fatal("unknow boosting type %s", type.c_str());
+        Log::Fatal("unknown boosting type %s", type.c_str());
       }
       LoadFileToBoosting(ret.get(), filename);
     } else {
-      Log::Fatal("unknow submodel type in model file %s", filename);
+      Log::Fatal("unknown submodel type in model file %s", filename);
     }
     return ret.release();
   }
@@ -64,7 +64,7 @@ Boosting* Boosting::CreateBoosting(const char* filename) {
   if (type == std::string("tree")) {
     ret.reset(new GBDT());
   } else {
-    Log::Fatal("unknow submodel type in model file %s", filename);
+    Log::Fatal("unknown submodel type in model file %s", filename);
   }
   LoadFileToBoosting(ret.get(), filename);
   return ret.release();

src/boosting/dart.hpp

@@ -84,7 +84,7 @@ private:
   void DroppingTrees() {
     drop_index_.clear();
     bool is_skip = random_for_drop_.NextFloat() < gbdt_config_->skip_drop;
-    // select dropping tree indexes based on drop_rate and tree weights
+    // select dropping tree indices based on drop_rate and tree weights
     if (!is_skip) {
       double drop_rate = gbdt_config_->drop_rate;
       if (!gbdt_config_->uniform_drop) {
@@ -180,7 +180,7 @@ private:
   std::vector<double> tree_weight_;
   /*! \brief sum weights of all trees */
   double sum_weight_;
-  /*! \brief The indexes of dropping trees */
+  /*! \brief The indices of dropping trees */
   std::vector<int> drop_index_;
   /*! \brief Random generator, used to select dropping trees */
   Random random_for_drop_;

src/boosting/gbdt.cpp

@@ -370,7 +370,7 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
   #ifdef TIMETAG
     start_time = std::chrono::steady_clock::now();
   #endif
-    std::unique_ptr<Tree> new_tree(new Tree(2));
+    std::unique_ptr<Tree> new_tree;
     // train a new tree
     new_tree.reset(tree_learner_->Train(gradient + curr_class * num_data_, hessian + curr_class * num_data_));
   #ifdef TIMETAG

src/boosting/gbdt.h

@@ -26,11 +26,10 @@ public:
   ~GBDT();
   /*!
   * \brief Initialization logic
-  * \param config Config for boosting
+  * \param gbdt_config Config for boosting
   * \param train_data Training data
   * \param object_function Training objective function
   * \param training_metrics Training metrics
-  * \param output_model_filename Filename of output model
   */
   void Init(const BoostingConfig* gbdt_config, const Dataset* train_data, const ObjectiveFunction* object_function,
                              const std::vector<const Metric*>& training_metrics)
@@ -267,7 +266,6 @@ protected:
   std::string OutputMetric(int iter);
   /*!
   * \brief Calculate feature importances
-  * \param last_iter Last tree use to calculate
   */
   std::vector<std::pair<size_t, std::string>> FeatureImportance() const;
   /*! \brief current iteration */