update for typo.

src/boosting/gbdt.h

@@ -107,7 +107,7 @@ private:
   */
   void UpdateScore(const Tree* tree);
   /*!
-  * \brief Print Metric result of current iteration
+  * \brief Print metric result of current iteration
   * \param iter Current interation
   */
   void OutputMetric(int iter);
@@ -116,11 +116,11 @@ private:
   const Dataset* train_data_;
   /*! \brief Config of gbdt */
   const GBDTConfig* gbdt_config_;
-  /*! \brief Tree learner, will use tihs class to learn trees */
+  /*! \brief Tree learner, will use this class to learn trees */
   TreeLearner* tree_learner_;
   /*! \brief Objective function */
   const ObjectiveFunction* object_function_;
-  /*! \brief Store and update traning data's score */
+  /*! \brief Store and update training data's score */
   ScoreUpdater* train_score_updater_;
   /*! \brief Metrics for training data */
   std::vector<const Metric*> training_metrics_;

src/boosting/score_updater.hpp

@@ -57,8 +57,8 @@ public:
   * \brief Like AddScore(const Tree* tree), but only for part of data
   * Used for prediction of training out-of-bad data
   * \param tree Trained tree model
-  * \param data_indices Indices of data that want proccess to
-  * \param data_cnt Number of data that want proccess to
+  * \param data_indices Indices of data that will be proccessed
+  * \param data_cnt Number of data that will be proccessed
   */
   inline void AddScore(const Tree* tree, const data_size_t* data_indices,
                                                   data_size_t data_cnt) {

src/io/dataset.cpp

@@ -31,12 +31,12 @@ Dataset::Dataset(const char* data_filename, const char* init_score_filename,
     // create text parser
     parser_ = Parser::CreateParser(data_filename_, 0, nullptr);
     if (parser_ == nullptr) {
-      Log::Stderr("cannot recognise input data format, filename: %s", data_filename_);
+      Log::Stderr("cannot recognize input data format, filename: %s", data_filename_);
     }
     // create text reader
     text_reader_ = new TextReader<data_size_t>(data_filename);
   } else {
-    // only need to load initilize score, other meta data will load from bin flie
+    // only need to load initilize score, other meta data will be loaded from bin flie
     metadata_.Init(init_score_filename);
     Log::Stdout("will load data set from binary file");
     parser_ = nullptr;
@@ -613,7 +613,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
 
   size_t size_of_metadata = *(reinterpret_cast<size_t*>(buffer));
 
-  // re-allocmate space if not enough
+  // re-allocate space if not enough
   if (size_of_metadata > buffer_size) {
     delete[] buffer;
     buffer_size = size_of_metadata;
@@ -673,7 +673,7 @@ void Dataset::LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partit
       Log::Stderr("binary file format error at feature %d's size", i);
     }
     size_t size_of_feature = *(reinterpret_cast<size_t*>(buffer));
-    // re-allocmate space if not enough
+    // re-allocate space if not enough
     if (size_of_feature > buffer_size) {
       delete[] buffer;
       buffer_size = size_of_feature;

src/io/dense_bin.hpp

@@ -10,7 +10,7 @@
 namespace LightGBM {
 
 /*!
-* \brief Used to Store bins for dense feature
+* \brief Used to store bins for dense feature
 * Use template to reduce memory cost
 */
 template <typename VAL_T>

src/io/ordered_sparse_bin.hpp

@@ -13,7 +13,7 @@
 namespace LightGBM {
 
 /*!
-* \brief Ordered bin for sparse feature . efficient for construct histogram, especally for sparse bin
+* \brief Ordered bin for sparse feature . Efficient for construct histogram, especally for sparse bin
 * There are 2 advantages for using ordered bin.
 * 1. group the data by leaf, improve the cache hit.
 * 2. only store the non-zero bin, which can speed up the histogram cconsturction for sparse feature.

src/metric/binary_metric.hpp

@@ -225,7 +225,7 @@ public:
   }
 
 private:
-  /*! \brief Output frequently */
+  /*! \brief Output frequency */
   int output_freq_;
   /*! \brief Number of data */
   data_size_t num_data_;

src/treelearner/serial_tree_learner.cpp

@@ -91,7 +91,7 @@ void SerialTreeLearner::Init(const Dataset* train_data) {
   // initialize ordered gradients and hessians
   ordered_gradients_ = new score_t[num_data_];
   ordered_hessians_ = new score_t[num_data_];
-  // if has ordered bin, need allocata a buffer to fast split
+  // if has ordered bin, need allocate a buffer to fast split
   if (has_ordered_bin_) {
     is_data_in_leaf_ = new char[num_data_];
   }
@@ -331,7 +331,7 @@ void SerialTreeLearner::FindBestThresholds() {
     // only has root leaf
     if (larger_leaf_splits_ == nullptr || larger_leaf_splits_->LeafIndex() < 0) continue;
 
-    // construct histgroms for large leaf, we initialize larger leaf as the parent,
+    // construct histograms for large leaf, we initialize larger leaf as the parent,
     // so we can just subtract the smaller leaf's histograms
     larger_leaf_histogram_array_[feature_index].Subtract(smaller_leaf_histogram_array_[feature_index]);