Merge pull request #16 from xuehui1991/update_for_typo

update for typo from header file.

include/LightGBM/bin.h

@@ -119,7 +119,7 @@ private:
 };
 
 /*!
-* \brief Interface for ordered bin data. It very efficient for construct histogram, especially for sparse bin
+* \brief Interface for ordered bin data. It's very efficient for constructing histogram, especially 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 consturction for sparse feature.
@@ -253,7 +253,7 @@ public:
   virtual OrderedBin* CreateOrderedBin() const = 0;
 
   /*!
-  * \brief After pushed all feature data, should call this to have better refactor for bin data
+  * \brief After pushed all feature data, call this could have better refactor for bin data
   */
   virtual void FinishLoad() = 0;
 
@@ -261,7 +261,7 @@ public:
   * \brief Create object for bin data of one feature, will call CreateDenseBin or CreateSparseBin according to "is_sparse"
   * \param num_data Total number of data
   * \param num_bin Number of bin
-  * \param is_sparse True if this feature is saprese
+  * \param is_sparse True if this feature is sparse
   * \param sparse_rate Sparse rate of this bins( num_bin0/num_data )
   * \param is_enable_sparse True if enable sparse feature
   * \param is_sparse Will set to true if this bin is sparse

include/LightGBM/boosting.h

@@ -47,14 +47,14 @@ public:
   virtual void Train() = 0;
 
   /*!
-  * \brief Predtion for one record, not sigmoid transform
+  * \brief Prediction for one record, not sigmoid transform
   * \param feature_values Feature value on this record
   * \return Prediction result for this record
   */
   virtual double PredictRaw(const double * feature_values) const = 0;
 
   /*!
-  * \brief Predtion for one record, will use sigmoid transform if needed
+  * \brief Prediction for one record, will use sigmoid transform if needed
   * \param feature_values Feature value on this record
   * \return Prediction result for this record
   */

include/LightGBM/config.h

@@ -20,7 +20,7 @@ public:
   virtual ~ConfigBase() {}
 
   /*!
-  * \brief SetLabelAt current config object by params
+  * \brief Set current config object by params
   * \param params Store the key and value for params
   */
   virtual void Set(
@@ -30,7 +30,7 @@ public:
   * \brief Get string value by specific name of key
   * \param params Store the key and value for params
   * \param name Name of key
-  * \param out Value will asign to out if key exists
+  * \param out Value will assign to out if key exists
   * \return True if key exists
   */
   inline bool GetString(
@@ -41,7 +41,7 @@ public:
   * \brief Get int value by specific name of key
   * \param params Store the key and value for params
   * \param name Name of key
-  * \param out Value will asign to out if key exists
+  * \param out Value will assign to out if key exists
   * \return True if key exists
   */
   inline bool GetInt(
@@ -52,7 +52,7 @@ public:
   * \brief Get double value by specific name of key
   * \param params Store the key and value for params
   * \param name Name of key
-  * \param out Value will asign to out if key exists
+  * \param out Value will assign to out if key exists
   * \return True if key exists
   */
   inline bool GetDouble(
@@ -63,7 +63,7 @@ public:
   * \brief Get bool value by specific name of key
   * \param params Store the key and value for params
   * \param name Name of key
-  * \param out Value will asign to out if key exists
+  * \param out Value will assign to out if key exists
   * \return True if key exists
   */
   inline bool GetBool(

include/LightGBM/dataset.h

@@ -17,7 +17,7 @@ namespace LightGBM {
 class Feature;
 
 /*!
-* \brief This class is used to store some meta(non-feature) data for tranining data,
+* \brief This class is used to store some meta(non-feature) data for training data,
 *        e.g. labels, weights, initial scores, qurey level informations.
 *
 * Some details:
@@ -110,14 +110,14 @@ public:
   }
 
   /*!
-  * \brief Get weights, if not exists, will return nullput
+  * \brief Get weights, if not exists, will return nullptr
   * \return Pointer of weights
   */
   inline const float* weights()
             const { return weights_; }
 
   /*!
-  * \brief Get data boundaries on queries, if not exists, will return nullput
+  * \brief Get data boundaries on queries, if not exists, will return nullptr
   *        we assume data will order by query, 
   *        the interval of [query_boundaris[i], query_boundaris[i+1])
   *        is the data indices for query i.
@@ -133,13 +133,13 @@ public:
   inline const data_size_t num_queries() const { return num_queries_; }
 
   /*!
-  * \brief Get weights for queries, if not exists, will return nullput
+  * \brief Get weights for queries, if not exists, will return nullptr
   * \return Pointer of weights for queries
   */
   inline const float* query_weights() const { return query_weights_; }
 
   /*!
-  * \brief Get initial scores, if not exists, will return nullput
+  * \brief Get initial scores, if not exists, will return nullptr
   * \return Pointer of initial scores
   */
   inline const score_t* init_score() const { return init_score_; }
@@ -231,7 +231,7 @@ public:
   * \param max_bin The maximal number of bin that feature values will bucket in
   * \param random_seed The seed for random generator
   * \param is_enable_sparse True for sparse feature
-  * \param predict_fun Used for initial model, will give a prediction score based on this function, thenn set as initial score
+  * \param predict_fun Used for initial model, will give a prediction score based on this function, then set as initial score
   */
   Dataset(const char* data_filename, const char* init_score_filename,
     int max_bin, int random_seed, bool is_enable_sparse, const PredictFunction& predict_fun);
@@ -243,7 +243,7 @@ public:
   * \param max_bin The maximal number of bin that feature values will bucket in
   * \param random_seed The seed for random generator
   * \param is_enable_sparse True for sparse feature
-  * \param predict_fun Used for initial model, will give a prediction score based on this function, thenn set as initial score
+  * \param predict_fun Used for initial model, will give a prediction score based on this function, then set as initial score
   */
   Dataset(const char* data_filename,
     int max_bin, int random_seed, bool is_enable_sparse,

include/LightGBM/network.h

@@ -37,7 +37,7 @@ public:
 
 /*!
 * \brief node type on recursive halving algorithm
-* When number of machines is not power of 2, need group maiches into power of 2 group.
+* When number of machines is not power of 2, need group machines into power of 2 group.
 * And we can let each group has at most 2 machines.
 * if the group only has 1 machine. this machine is the normal node
 * if the grou has 2 machines, this group will have two type of nodes, one is the leader.

src/boosting/gbdt.cpp

@@ -248,7 +248,6 @@ std::string GBDT::ModelsToString() const {
 
 void GBDT::ModelsFromString(const std::string& model_str, int num_used_model) {
   // use serialized string to restore this object
-  // deseialize string to object????
   models_.clear();
   std::vector<std::string> lines = Common::Split(model_str.c_str(), '\n');
   size_t i = 0;