update for typo.

src/metric/regression_metric.hpp

@@ -65,7 +65,7 @@ public:
   }
 
 private:
-  /*! \brief Output frequently */
+  /*! \brief Output frequency */
   int output_freq_;
   /*! \brief Number of data */
   data_size_t num_data_;

src/network/network.cpp

@@ -9,7 +9,7 @@
 
 namespace LightGBM {
 
-// static member defination
+// static member definition
 int Network::num_machines_;
 int Network::rank_;
 Linkers* Network::linkers_;
@@ -141,7 +141,7 @@ void Network::ReduceScatter(char* input, int input_size, int* block_start, int*
       // send local data to neighbor first
       linkers_->Send(recursive_halving_map_.neighbor, input, input_size);
     } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) {
-      // recieve neighbor data first
+      // receive neighbor data first
       int need_recv_cnt = input_size;
       linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt);
       // reduce

src/objective/rank_objective.hpp

@@ -50,7 +50,7 @@ public:
       Log::Stderr("For NDCG metric, should have query information");
     }
     num_queries_ = metadata.num_queries();
-    // cache inverse max DCG, avoid compution many times
+    // cache inverse max DCG, avoid computation many times
     inverse_max_dcgs_ = new score_t[num_queries_];
     for (data_size_t i = 0; i < num_queries_; ++i) {
       inverse_max_dcgs_[i] = static_cast<score_t>(

src/treelearner/feature_histogram.hpp

@@ -40,7 +40,7 @@ public:
   * \brief Construct a histogram
   * \param num_data number of data in current leaf
   * \param sum_gradients sum of gradients of current leaf
-  * \param sum_hessians sum of hissians of current leaf
+  * \param sum_hessians sum of hessians of current leaf
   * \param ordered_gradients Orederd gradients
   * \param ordered_hessians  Ordered hessians
   * \param data_indices data indices of current leaf
@@ -59,7 +59,7 @@ public:
   * \param leaf current leaf
   * \param num_data number of data in current leaf
   * \param sum_gradients sum of gradients of current leaf
-  * \param sum_hessians sum of hissians of current leaf
+  * \param sum_hessians sum of hessians of current leaf
   * \param gradients
   * \param hessian
   */
@@ -76,7 +76,7 @@ public:
   * \brief Set sumup information for current histogram
   * \param num_data number of data in current leaf
   * \param sum_gradients sum of gradients of current leaf
-  * \param sum_hessians sum of hissians of current leaf
+  * \param sum_hessians sum of hessians of current leaf
   */
   void SetSumup(data_size_t num_data, score_t sum_gradients, score_t sum_hessians) {
     num_data_ = num_data;

src/treelearner/leaf_splits.hpp

@@ -26,7 +26,7 @@ public:
   }
 
   /*!
-  * \brief Init splits on current leaf, don't need to travesal all data
+  * \brief Init splits on current leaf, don't need to traverse all data
   * \param leaf Index of current leaf
   * \param data_partition current data partition
   * \param sum_gradients
@@ -43,7 +43,7 @@ public:
   }
 
   /*!
-  * \brief Init splits on current leaf, need to travesal all data to sum up
+  * \brief Init splits on current leaf, need to traverse all data to sum up
   * \param gradients
   * \param hessians
   */

src/treelearner/parallel_tree_learner.h

@@ -77,9 +77,9 @@ private:
   int* block_start_;
   /*! \brief Block size for reduce scatter */
   int* block_len_;
-  /*! \brief Write positions for feature histgrams */
+  /*! \brief Write positions for feature histograms */
   int* buffer_write_start_pos_;
-  /*! \brief Read positions for local feature histgrams */
+  /*! \brief Read positions for local feature histograms */
   int* buffer_read_start_pos_;
   /*! \brief Size for reduce scatter */
   int reduce_scatter_size_;

src/treelearner/serial_tree_learner.cpp

@@ -239,16 +239,16 @@ bool SerialTreeLearner::BeforeFindBestSplit(int left_leaf, int right_leaf) {
     larger_leaf_histogram_array_ = nullptr;
   } else if (num_data_in_left_child < num_data_in_right_child) {
     smaller_leaf = left_leaf;
-    // put parent(left) leaf's histograms into larger leaf's histgrams
+    // put parent(left) leaf's histograms into larger leaf's histograms
     larger_leaf_histogram_array_ = historical_histogram_array_[left_leaf];
     smaller_leaf_histogram_array_ = historical_histogram_array_[right_leaf];
 
     // We will construc histograms for smaller leaf, and smaller_leaf=left_leaf = parent.
-    // if we don't swap the cache, we will overwrite the parent's hisogram cache.
+    // if we don't swap the cache, we will overwrite the parent's histogram cache.
     std::swap(historical_histogram_array_[left_leaf], historical_histogram_array_[right_leaf]);
   } else {
     smaller_leaf = right_leaf;
-    // put parent(left) leaf's histograms to larger leaf's histgrams
+    // put parent(left) leaf's histograms to larger leaf's histograms
     larger_leaf_histogram_array_ = historical_histogram_array_[left_leaf];
     smaller_leaf_histogram_array_ = historical_histogram_array_[right_leaf];
   }

src/treelearner/serial_tree_learner.h

@@ -120,7 +120,7 @@ protected:
   DataPartition* data_partition_;
   /*! \brief used for generate used features */
   Random random_;
-  /*! \brief used for sub feature training, is_feature_used_[i] = falase means don't used feature i */
+  /*! \brief used for sub feature training, is_feature_used_[i] = false means don't used feature i */
   bool* is_feature_used_;
   /*! \brief cache historical histogram to speed up */
   FeatureHistogram** historical_histogram_array_;