diff --git a/src/learner/dmatrix.h b/src/learner/dmatrix.h
index b58f7b2bb..3fbc579de 100644
--- a/src/learner/dmatrix.h
+++ b/src/learner/dmatrix.h
@@ -1,11 +1,13 @@
-#ifndef XGBOOST_LEARNER_DMATRIX_H_
-#define XGBOOST_LEARNER_DMATRIX_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file dmatrix.h
- * \brief meta data and template data structure 
+ * \brief meta data and template data structure
  *        used for regression/classification/ranking
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_LEARNER_DMATRIX_H_
+#define XGBOOST_LEARNER_DMATRIX_H_
+
 #include <vector>
 #include <cstring>
 #include "../data.h"
@@ -16,8 +18,8 @@ namespace learner {
  * \brief meta information needed in training, including label, weight
  */
 struct MetaInfo {
-  /*! 
-   * \brief information needed by booster 
+  /*!
+   * \brief information needed by booster
    * BoosterInfo does not implement save and load,
    * all serialization is done in MetaInfo
    */
@@ -31,7 +33,7 @@ struct MetaInfo {
   std::vector<bst_uint> group_ptr;
   /*! \brief weights of each instance, optional */
   std::vector<float> weights;
-  /*! 
+  /*!
    * \brief initialized margins,
    * if specified, xgboost will start from this init margin
    * can be used to specify initial prediction to boost from
@@ -66,7 +68,7 @@ struct MetaInfo {
       return 1.0f;
     }
   }
-  inline void SaveBinary(utils::IStream &fo) const {
+  inline void SaveBinary(utils::IStream &fo) const { // NOLINT(*)
     int version = kVersion;
     fo.Write(&version, sizeof(version));
     fo.Write(&info.num_row, sizeof(info.num_row));
@@ -77,7 +79,7 @@ struct MetaInfo {
     fo.Write(info.root_index);
     fo.Write(base_margin);
   }
-  inline void LoadBinary(utils::IStream &fi) {
+  inline void LoadBinary(utils::IStream &fi) { // NOLINT(*)
     int version;
     utils::Check(fi.Read(&version, sizeof(version)) != 0, "MetaInfo: invalid format");
     utils::Check(fi.Read(&info.num_row, sizeof(info.num_row)) != 0, "MetaInfo: invalid format");
@@ -114,7 +116,7 @@ struct MetaInfo {
     return labels;
   }
   inline const std::vector<float>& GetFloatInfo(const char *field) const {
-    return ((MetaInfo*)this)->GetFloatInfo(field);
+    return ((MetaInfo*)this)->GetFloatInfo(field); // NOLINT(*)
   }
   inline std::vector<unsigned> &GetUIntInfo(const char *field) {
     using namespace std;
@@ -124,7 +126,7 @@ struct MetaInfo {
     return info.root_index;
   }
   inline const std::vector<unsigned> &GetUIntInfo(const char *field) const {
-    return ((MetaInfo*)this)->GetUIntInfo(field);
+    return ((MetaInfo*)this)->GetUIntInfo(field);  // NOLINT(*)
   }
   // try to load weight information from file, if exists
   inline bool TryLoadFloatInfo(const char *field, const char* fname, bool silent = false) {
@@ -149,14 +151,14 @@ struct MetaInfo {
  * \tparam FMatrix type of feature data source
  */
 struct DMatrix {
-  /*! 
-   * \brief magic number associated with this object 
+  /*!
+   * \brief magic number associated with this object
    *    used to check if it is specific instance
    */
   const int magic;
   /*! \brief meta information about the dataset */
   MetaInfo info;
-  /*! 
+  /*!
    * \brief cache pointer to verify if the data structure is cached in some learner
    *  used to verify if DMatrix is cached
    */
diff --git a/src/learner/evaluation-inl.hpp b/src/learner/evaluation-inl.hpp
index 433b5a00b..2b69a43a8 100644
--- a/src/learner/evaluation-inl.hpp
+++ b/src/learner/evaluation-inl.hpp
@@ -1,10 +1,12 @@
+/*!
+ * Copyright 2014 by Contributors
+ * \file xgboost_evaluation-inl.hpp
+ * \brief evaluation metrics for regression and classification and rank
+ * \author Kailong Chen, Tianqi Chen
+ */
 #ifndef XGBOOST_LEARNER_EVALUATION_INL_HPP_
 #define XGBOOST_LEARNER_EVALUATION_INL_HPP_
-/*!
-* \file xgboost_evaluation-inl.hpp
-* \brief evaluation metrics for regression and classification and rank
-* \author Kailong Chen, Tianqi Chen
-*/
+
 #include <vector>
 #include <utility>
 #include <string>
@@ -18,8 +20,8 @@
 
 namespace xgboost {
 namespace learner {
-/*! 
- * \brief base class of elementwise evaluation 
+/*!
+ * \brief base class of elementwise evaluation
  * \tparam Derived the name of subclass
  */
 template<typename Derived>
@@ -47,15 +49,15 @@ struct EvalEWiseBase : public IEvaluator {
     }
     return Derived::GetFinal(dat[0], dat[1]);
   }
-  /*! 
-   * \brief to be implemented by subclass, 
-   *   get evaluation result from one row 
+  /*!
+   * \brief to be implemented by subclass,
+   *   get evaluation result from one row
    * \param label label of current instance
    * \param pred prediction value of current instance
    */
   inline static float EvalRow(float label, float pred);
-  /*! 
-   * \brief to be overide by subclas, final trasnformation 
+  /*!
+   * \brief to be overide by subclas, final trasnformation
    * \param esum the sum statistics returned by EvalRow
    * \param wsum sum of weight
    */
@@ -87,9 +89,9 @@ struct EvalLogLoss : public EvalEWiseBase<EvalLogLoss> {
     const float eps = 1e-16f;
     const float pneg = 1.0f - py;
     if (py < eps) {
-      return -y * std::log(eps) - (1.0f - y)  * std::log(1.0f - eps); 
+      return -y * std::log(eps) - (1.0f - y)  * std::log(1.0f - eps);
     } else if (pneg < eps) {
-      return -y * std::log(1.0f - eps) - (1.0f - y)  * std::log(eps); 
+      return -y * std::log(1.0f - eps) - (1.0f - y)  * std::log(eps);
     } else {
       return -y * std::log(py) - (1.0f - y) * std::log(pneg);
     }
@@ -119,7 +121,7 @@ struct EvalPoissionNegLogLik : public EvalEWiseBase<EvalPoissionNegLogLik> {
   }
 };
 
-/*! 
+/*!
  * \brief base class of multi-class evaluation
  * \tparam Derived the name of subclass
  */
@@ -139,7 +141,7 @@ struct EvalMClassBase : public IEvaluator {
     float sum = 0.0, wsum = 0.0;
     int label_error = 0;
     #pragma omp parallel for reduction(+: sum, wsum) schedule(static)
-    for (bst_omp_uint i = 0; i < ndata; ++i) {      
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       const float wt = info.GetWeight(i);
       int label =  static_cast<int>(info.labels[i]);
       if (label >= 0 && label < static_cast<int>(nclass)) {
@@ -161,18 +163,18 @@ struct EvalMClassBase : public IEvaluator {
     }
     return Derived::GetFinal(dat[0], dat[1]);
   }
-  /*! 
-   * \brief to be implemented by subclass, 
-   *   get evaluation result from one row 
+  /*!
+   * \brief to be implemented by subclass,
+   *   get evaluation result from one row
    * \param label label of current instance
-   * \param pred prediction value of current instance 
+   * \param pred prediction value of current instance
    * \param nclass number of class in the prediction
    */
   inline static float EvalRow(int label,
                               const float *pred,
                               size_t nclass);
-  /*! 
-   * \brief to be overide by subclas, final trasnformation 
+  /*!
+   * \brief to be overide by subclas, final trasnformation
    * \param esum the sum statistics returned by EvalRow
    * \param wsum sum of weight
    */
@@ -208,7 +210,7 @@ struct EvalMultiLogLoss : public EvalMClassBase<EvalMultiLogLoss> {
     } else {
       return -std::log(eps);
     }
-  } 
+  }
 };
 
 /*! \brief ctest */
@@ -240,7 +242,7 @@ struct EvalCTest: public IEvaluator {
           tpred.push_back(preds[i + (k + 1) * ndata]);
           tinfo.labels.push_back(info.labels[i]);
           tinfo.weights.push_back(info.GetWeight(i));
-        }        
+        }
       }
       wsum += base_->Eval(tpred, tinfo);
     }
@@ -328,7 +330,7 @@ struct EvalPrecisionRatio : public IEvaluator{
                      const MetaInfo &info,
                      bool distributed) const {
     utils::Check(!distributed, "metric %s do not support distributed evaluation", Name());
-    utils::Check(info.labels.size() != 0, "label set cannot be empty");    
+    utils::Check(info.labels.size() != 0, "label set cannot be empty");
     utils::Assert(preds.size() % info.labels.size() == 0,
                   "label size predict size not match");
     std::vector< std::pair<float, unsigned> > rec;
@@ -344,7 +346,8 @@ struct EvalPrecisionRatio : public IEvaluator{
   }
 
  protected:
-  inline double CalcPRatio(const std::vector< std::pair<float, unsigned> >& rec, const MetaInfo &info) const {
+  inline double CalcPRatio(const std::vector< std::pair<float, unsigned> >& rec,
+                           const MetaInfo &info) const {
     size_t cutoff = static_cast<size_t>(ratio_ * rec.size());
     double wt_hit = 0.0, wsum = 0.0, wt_sum = 0.0;
     for (size_t j = 0; j < cutoff; ++j) {
@@ -372,7 +375,7 @@ struct EvalAuc : public IEvaluator {
     utils::Check(info.labels.size() != 0, "label set cannot be empty");
     utils::Check(preds.size() % info.labels.size() == 0,
                  "label size predict size not match");
-    std::vector<unsigned> tgptr(2, 0); 
+    std::vector<unsigned> tgptr(2, 0);
     tgptr[1] = static_cast<unsigned>(info.labels.size());
 
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
@@ -417,8 +420,8 @@ struct EvalAuc : public IEvaluator {
     }
     if (distributed) {
       float dat[2];
-	  dat[0] = static_cast<float>(sum_auc);
-	  dat[1] = static_cast<float>(ngroup);      
+      dat[0] = static_cast<float>(sum_auc);
+      dat[1] = static_cast<float>(ngroup);
       // approximately estimate auc using mean
       rabit::Allreduce<rabit::op::Sum>(dat, 2);
       return dat[0] / dat[1];
@@ -463,8 +466,8 @@ struct EvalRankList : public IEvaluator {
     }
     if (distributed) {
       float dat[2];
-	  dat[0] = static_cast<float>(sum_metric);
-	  dat[1] = static_cast<float>(ngroup);      
+      dat[0] = static_cast<float>(sum_metric);
+      dat[1] = static_cast<float>(ngroup);
       // approximately estimate auc using mean
       rabit::Allreduce<rabit::op::Sum>(dat, 2);
       return dat[0] / dat[1];
@@ -489,7 +492,7 @@ struct EvalRankList : public IEvaluator {
     }
   }
   /*! \return evaluation metric, given the pair_sort record, (pred,label) */
-  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0;
+  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0; // NOLINT(*)
 
  protected:
   unsigned topn_;
@@ -524,13 +527,13 @@ struct EvalNDCG : public EvalRankList{
     double sumdcg = 0.0;
     for (size_t i = 0; i < rec.size() && i < this->topn_; ++i) {
       const unsigned rel = rec[i].second;
-      if (rel != 0) { 
+      if (rel != 0) {
         sumdcg += ((1 << rel) - 1) / std::log(i + 2.0);
       }
     }
     return static_cast<float>(sumdcg);
   }
-  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const {
+  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const { // NOLINT(*)
     std::stable_sort(rec.begin(), rec.end(), CmpFirst);
     float dcg = this->CalcDCG(rec);
     std::stable_sort(rec.begin(), rec.end(), CmpSecond);
diff --git a/src/learner/evaluation.h b/src/learner/evaluation.h
index 85358e72e..a98c47495 100644
--- a/src/learner/evaluation.h
+++ b/src/learner/evaluation.h
@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_EVALUATION_H_
-#define XGBOOST_LEARNER_EVALUATION_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file evaluation.h
  * \brief interface of evaluation function supported in xgboost
  * \author Tianqi Chen, Kailong Chen
  */
+#ifndef XGBOOST_LEARNER_EVALUATION_H_
+#define XGBOOST_LEARNER_EVALUATION_H_
+
 #include <string>
 #include <vector>
 #include <cstdio>
@@ -19,7 +21,7 @@ struct IEvaluator{
    * \brief evaluate a specific metric
    * \param preds prediction
    * \param info information, including label etc.
-   * \param distributed whether a call to Allreduce is needed to gather 
+   * \param distributed whether a call to Allreduce is needed to gather
    *        the average statistics across all the node,
    *        this is only supported by some metrics
    */
diff --git a/src/learner/helper_utils.h b/src/learner/helper_utils.h
index d318cf8bd..7ca7ba59c 100644
--- a/src/learner/helper_utils.h
+++ b/src/learner/helper_utils.h
@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
-#define XGBOOST_LEARNER_HELPER_UTILS_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file helper_utils.h
  * \brief useful helper functions
  * \author Tianqi Chen, Kailong Chen
  */
+#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
+#define XGBOOST_LEARNER_HELPER_UTILS_H_
+
 #include <utility>
 #include <vector>
 #include <cmath>
@@ -61,7 +63,7 @@ inline float LogSum(const float *rec, size_t size) {
   for (size_t i = 0; i < size; ++i) {
     sum += std::exp(rec[i] - mx);
   }
-  return mx + std::log(sum);  
+  return mx + std::log(sum);
 }
 
 inline static bool CmpFirst(const std::pair<float, unsigned> &a,
diff --git a/src/learner/learner-inl.hpp b/src/learner/learner-inl.hpp
index 45e312aa7..f051992d3 100644
--- a/src/learner/learner-inl.hpp
+++ b/src/learner/learner-inl.hpp
@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
-#define XGBOOST_LEARNER_LEARNER_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file learner-inl.hpp
- * \brief learning algorithm 
+ * \brief learning algorithm
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
+#define XGBOOST_LEARNER_LEARNER_INL_HPP_
+
 #include <algorithm>
 #include <vector>
 #include <utility>
@@ -19,7 +21,7 @@
 namespace xgboost {
 /*! \brief namespace for learning algorithm */
 namespace learner {
-/*! 
+/*!
  * \brief learner that takes do gradient boosting on specific objective functions
  *  and do training and prediction
  */
@@ -30,7 +32,7 @@ class BoostLearner : public rabit::Serializable {
     gbm_ = NULL;
     name_obj_ = "reg:linear";
     name_gbm_ = "gbtree";
-    silent= 0;
+    silent = 0;
     prob_buffer_row = 1.0f;
     distributed_mode = 0;
     updater_mode = 0;
@@ -47,10 +49,10 @@ class BoostLearner : public rabit::Serializable {
    * \brief add internal cache space for mat, this can speedup prediction for matrix,
    *        please cache prediction for training and eval data
    *    warning: if the model is loaded from file from some previous training history
-   *             set cache data must be called with exactly SAME 
+   *             set cache data must be called with exactly SAME
    *             data matrices to continue training otherwise it will cause error
    * \param mats array of pointers to matrix whose prediction result need to be cached
-   */          
+   */
   inline void SetCacheData(const std::vector<DMatrix*>& mats) {
     utils::Assert(cache_.size() == 0, "can only call cache data once");
     // assign buffer index
@@ -67,10 +69,10 @@ class BoostLearner : public rabit::Serializable {
       buffer_size += mats[i]->info.num_row();
     }
     char str_temp[25];
-    utils::SPrintf(str_temp, sizeof(str_temp), "%lu", 
-                   static_cast<unsigned long>(buffer_size));
+    utils::SPrintf(str_temp, sizeof(str_temp), "%lu",
+                   static_cast<unsigned long>(buffer_size)); // NOLINT(*)
     this->SetParam("num_pbuffer", str_temp);
-    this->pred_buffer_size = buffer_size;    
+    this->pred_buffer_size = buffer_size;
   }
   /*!
    * \brief set parameters from outside
@@ -79,7 +81,7 @@ class BoostLearner : public rabit::Serializable {
    */
   inline void SetParam(const char *name, const char *val) {
     using namespace std;
-    // in this version, bst: prefix is no longer required 
+    // in this version, bst: prefix is no longer required
     if (strncmp(name, "bst:", 4) != 0) {
       std::string n = "bst:"; n += name;
       this->SetParam(n.c_str(), val);
@@ -119,7 +121,7 @@ class BoostLearner : public rabit::Serializable {
       if (!strcmp(name, "objective")) name_obj_ = val;
       if (!strcmp(name, "booster")) name_gbm_ = val;
       mparam.SetParam(name, val);
-    }    
+    }
     if (gbm_ != NULL) gbm_->SetParam(name, val);
     if (obj_ != NULL) obj_->SetParam(name, val);
     if (gbm_ == NULL || obj_ == NULL) {
@@ -133,16 +135,16 @@ class BoostLearner : public rabit::Serializable {
       // estimate feature bound
       unsigned num_feature = 0;
       for (size_t i = 0; i < cache_.size(); ++i) {
-        num_feature = std::max(num_feature, 
+        num_feature = std::max(num_feature,
                                static_cast<unsigned>(cache_[i].mat_->info.num_col()));
       }
       // run allreduce on num_feature to find the maximum value
       rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
       if (num_feature > mparam.num_feature) mparam.num_feature = num_feature;
-    } 
+    }
     char str_temp[25];
     utils::SPrintf(str_temp, sizeof(str_temp), "%d", mparam.num_feature);
-    this->SetParam("bst:num_feature", str_temp);   
+    this->SetParam("bst:num_feature", str_temp);
   }
   /*!
    * \brief initialize the model
@@ -161,13 +163,13 @@ class BoostLearner : public rabit::Serializable {
    * \param fi input stream
    * \param calc_num_feature whether call InitTrainer with calc_num_feature
    */
-  inline void LoadModel(utils::IStream &fi,
+  inline void LoadModel(utils::IStream &fi,  // NOLINT(*)
                         bool calc_num_feature = true) {
     utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
                  "BoostLearner: wrong model format");
     {
       // backward compatibility code for compatible with old model type
-      // for new model, Read(&name_obj_) is suffice      
+      // for new model, Read(&name_obj_) is suffice
       uint64_t len;
       utils::Check(fi.Read(&len, sizeof(len)) != 0, "BoostLearner: wrong model format");
       if (len >= std::numeric_limits<unsigned>::max()) {
@@ -226,9 +228,9 @@ class BoostLearner : public rabit::Serializable {
       fi = utils::IStream::Create(fname, "r");
       this->LoadModel(*fi, true);
     }
-    delete fi;   
+    delete fi;
   }
-  inline void SaveModel(utils::IStream &fo, bool with_pbuffer) const {
+  inline void SaveModel(utils::IStream &fo, bool with_pbuffer) const { // NOLINT(*)
     ModelParam p = mparam;
     p.saved_with_pbuffer = static_cast<int>(with_pbuffer);
     fo.Write(&p, sizeof(ModelParam));
@@ -247,7 +249,7 @@ class BoostLearner : public rabit::Serializable {
       fo->Write("bs64\t", 5);
       utils::Base64OutStream bout(fo);
       this->SaveModel(bout, with_pbuffer);
-      bout.Finish('\n');    
+      bout.Finish('\n');
     } else {
       fo->Write("binf", 4);
       this->SaveModel(*fo, with_pbuffer);
@@ -260,7 +262,7 @@ class BoostLearner : public rabit::Serializable {
    * \param p_train pointer to the matrix used by training
    */
   inline void CheckInit(DMatrix *p_train) {
-    int ncol = static_cast<int>(p_train->info.info.num_col);    
+    int ncol = static_cast<int>(p_train->info.info.num_col);
     std::vector<bool> enabled(ncol, true);
     // set max row per batch to limited value
     // in distributed mode, use safe choice otherwise
@@ -345,10 +347,9 @@ class BoostLearner : public rabit::Serializable {
                       bool output_margin,
                       std::vector<float> *out_preds,
                       unsigned ntree_limit = 0,
-                      bool pred_leaf = false
-                      ) const {
+                      bool pred_leaf = false) const {
     if (pred_leaf) {
-      gbm_->PredictLeaf(data.fmat(), data.info.info, out_preds, ntree_limit);      
+      gbm_->PredictLeaf(data.fmat(), data.info.info, out_preds, ntree_limit);
     } else {
       this->PredictRaw(data, out_preds, ntree_limit);
       if (!output_margin) {
@@ -361,7 +362,7 @@ class BoostLearner : public rabit::Serializable {
    *  NOTE: use the batch prediction interface if possible, batch prediction is usually
    *        more efficient than online prediction
    *        This function is NOT threadsafe, make sure you only call from one thread
-   *    
+   *
    * \param inst the instance you want to predict
    * \param output_margin whether to only predict margin value instead of transformed prediction
    * \param out_preds output vector to hold the predictions
@@ -387,8 +388,8 @@ class BoostLearner : public rabit::Serializable {
   }
 
  protected:
-  /*! 
-   * \brief initialize the objective function and GBM, 
+  /*!
+   * \brief initialize the objective function and GBM,
    * if not yet done
    */
   inline void InitObjGBM(void) {
@@ -401,12 +402,12 @@ class BoostLearner : public rabit::Serializable {
     for (size_t i = 0; i < cfg_.size(); ++i) {
       obj_->SetParam(cfg_[i].first.c_str(), cfg_[i].second.c_str());
       gbm_->SetParam(cfg_[i].first.c_str(), cfg_[i].second.c_str());
-    }   
+    }
     if (evaluator_.Size() == 0) {
       evaluator_.AddEval(obj_->DefaultEvalMetric());
     }
   }
-  /*! 
+  /*!
    * \brief additional default value for specific objs
    */
   inline void InitAdditionDefaultParam(void) {
@@ -415,12 +416,12 @@ class BoostLearner : public rabit::Serializable {
       gbm_->SetParam("max_delta_step", "0.7");
     }
   }
-  /*! 
+  /*!
    * \brief get un-transformed prediction
    * \param data training data matrix
    * \param out_preds output vector that stores the prediction
    * \param ntree_limit limit number of trees used for boosted tree
-   *   predictor, when it equals 0, this means we are using all the trees   
+   *   predictor, when it equals 0, this means we are using all the trees
    */
   inline void PredictRaw(const DMatrix &data,
                          std::vector<float> *out_preds,
@@ -517,7 +518,7 @@ class BoostLearner : public rabit::Serializable {
 
  protected:
   // magic number to transform random seed
-  const static int kRandSeedMagic = 127;
+  static const int kRandSeedMagic = 127;
   // cache entry object that helps handle feature caching
   struct CacheEntry {
     const DMatrix *mat_;
diff --git a/src/learner/objective-inl.hpp b/src/learner/objective-inl.hpp
index d0ecf7a27..b6d388e3c 100644
--- a/src/learner/objective-inl.hpp
+++ b/src/learner/objective-inl.hpp
@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
-#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file objective-inl.hpp
  * \brief objective function implementations
  * \author Tianqi Chen, Kailong Chen
  */
+#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
+#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
+
 #include <vector>
 #include <algorithm>
 #include <utility>
@@ -176,14 +178,14 @@ class RegLossObj : public IObjFunction {
 // poisson regression for count
 class PoissonRegression : public IObjFunction {
  public:
-  explicit PoissonRegression(void) {
+  PoissonRegression(void) {
     max_delta_step = 0.0f;
   }
   virtual ~PoissonRegression(void) {}
-  
+
   virtual void SetParam(const char *name, const char *val) {
     using namespace std;
-    if (!strcmp( "max_delta_step", name )) {
+    if (!strcmp("max_delta_step", name)) {
       max_delta_step = static_cast<float>(atof(val));
     }
   }
@@ -201,9 +203,9 @@ class PoissonRegression : public IObjFunction {
     // check if label in range
     bool label_correct = true;
     // start calculating gradient
-    const long ndata = static_cast<bst_omp_uint>(preds.size());
+    const long ndata = static_cast<bst_omp_uint>(preds.size()); // NOLINT(*)
     #pragma omp parallel for schedule(static)
-    for (long i = 0; i < ndata; ++i) {
+    for (long i = 0; i < ndata; ++i) { // NOLINT(*)
       float p = preds[i];
       float w = info.GetWeight(i);
       float y = info.labels[i];
@@ -219,9 +221,9 @@ class PoissonRegression : public IObjFunction {
   }
   virtual void PredTransform(std::vector<float> *io_preds) {
     std::vector<float> &preds = *io_preds;
-    const long ndata = static_cast<long>(preds.size());
+    const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
     #pragma omp parallel for schedule(static)
-    for (long j = 0; j < ndata; ++j) {
+    for (long j = 0; j < ndata; ++j) {  // NOLINT(*)
       preds[j] = std::exp(preds[j]);
     }
   }
@@ -234,7 +236,7 @@ class PoissonRegression : public IObjFunction {
   virtual const char* DefaultEvalMetric(void) const {
     return "poisson-nloglik";
   }
-  
+
  private:
   float max_delta_step;
 };
@@ -467,7 +469,7 @@ class LambdaRankObj : public IObjFunction {
         : pos_index(pos_index), neg_index(neg_index), weight(1.0f) {}
   };
   /*!
-   * \brief get lambda weight for existing pairs 
+   * \brief get lambda weight for existing pairs
    * \param list a list that is sorted by pred score
    * \param io_pairs record of pairs, containing the pairs to fill in weights
    */
@@ -555,10 +557,10 @@ class LambdaRankObjMAP : public LambdaRankObj {
     float ap_acc;
     /*!
      * \brief the accumulated precision,
-     *   assuming a positive instance is missing 
+     *   assuming a positive instance is missing
      */
     float ap_acc_miss;
-    /*! 
+    /*!
      * \brief the accumulated precision,
      * assuming that one more positive instance is inserted ahead
      */
diff --git a/src/learner/objective.h b/src/learner/objective.h
index c0a525a43..08b57f528 100644
--- a/src/learner/objective.h
+++ b/src/learner/objective.h
@@ -1,11 +1,14 @@
-#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
-#define XGBOOST_LEARNER_OBJECTIVE_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file objective.h
  * \brief interface of objective function used for gradient boosting
  * \author Tianqi Chen, Kailong Chen
  */
-#include "dmatrix.h"
+#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
+#define XGBOOST_LEARNER_OBJECTIVE_H_
+
+#include <vector>
+#include "./dmatrix.h"
 
 namespace xgboost {
 namespace learner {
@@ -13,13 +16,13 @@ namespace learner {
 class IObjFunction{
  public:
   /*! \brief virtual destructor */
-  virtual ~IObjFunction(void){}
+  virtual ~IObjFunction(void) {}
   /*!
    * \brief set parameters from outside
    * \param name name of the parameter
    * \param val value of the parameter
    */
-  virtual void SetParam(const char *name, const char *val) = 0;  
+  virtual void SetParam(const char *name, const char *val) = 0;
   /*!
    * \brief get gradient over each of predictions, given existing information
    * \param preds prediction of current round
@@ -38,9 +41,9 @@ class IObjFunction{
    * \brief transform prediction values, this is only called when Prediction is called
    * \param io_preds prediction values, saves to this vector as well
    */
-  virtual void PredTransform(std::vector<float> *io_preds){}
+  virtual void PredTransform(std::vector<float> *io_preds) {}
   /*!
-   * \brief transform prediction values, this is only called when Eval is called, 
+   * \brief transform prediction values, this is only called when Eval is called,
    *  usually it redirect to PredTransform
    * \param io_preds prediction values, saves to this vector as well
    */
@@ -49,7 +52,7 @@ class IObjFunction{
   }
   /*!
    * \brief transform probability value back to margin
-   * this is used to transform user-set base_score back to margin 
+   * this is used to transform user-set base_score back to margin
    * used by gradient boosting
    * \return transformed value
    */
@@ -77,7 +80,7 @@ inline IObjFunction* CreateObjFunction(const char *name) {
   if (!strcmp("multi:softprob", name)) return new SoftmaxMultiClassObj(1);
   if (!strcmp("rank:pairwise", name )) return new PairwiseRankObj();
   if (!strcmp("rank:ndcg", name)) return new LambdaRankObjNDCG();
-  if (!strcmp("rank:map", name)) return new LambdaRankObjMAP();  
+  if (!strcmp("rank:map", name)) return new LambdaRankObjMAP();
   utils::Error("unknown objective function type: %s", name);
   return NULL;
 }
diff --git a/src/sync/sync.h b/src/sync/sync.h
index 3a371b03c..b9bdf89fe 100644
--- a/src/sync/sync.h
+++ b/src/sync/sync.h
@@ -1,13 +1,13 @@
-#ifndef XGBOOST_SYNC_H_
-#define XGBOOST_SYNC_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file sync.h
  * \brief the synchronization module of rabit
  *        redirects to subtree rabit header
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_SYNC_SYNC_H_
+#define XGBOOST_SYNC_SYNC_H_
+
 #include "../../subtree/rabit/include/rabit.h"
 #include "../../subtree/rabit/include/rabit/timer.h"
-#endif  // XGBOOST_SYNC_H_
-
-
+#endif  // XGBOOST_SYNC_SYNC_H_