remove using std from cpp

src/learner/dmatrix.h

@@ -90,6 +90,7 @@ struct MetaInfo {
   }
   // try to load group information from file, if exists
   inline bool TryLoadGroup(const char* fname, bool silent = false) {
+    using namespace std;
     FILE *fi = fopen64(fname, "r");
     if (fi == NULL) return false;
     group_ptr.push_back(0);
@@ -105,6 +106,7 @@ struct MetaInfo {
     return true;
   }
   inline std::vector<float>& GetFloatInfo(const char *field) {
+    using namespace std;
     if (!strcmp(field, "label")) return labels;
     if (!strcmp(field, "weight")) return weights;
     if (!strcmp(field, "base_margin")) return base_margin;
@@ -115,6 +117,7 @@ struct MetaInfo {
     return ((MetaInfo*)this)->GetFloatInfo(field);
   }
   inline std::vector<unsigned> &GetUIntInfo(const char *field) {
+    using namespace std;
     if (!strcmp(field, "root_index")) return info.root_index;
     if (!strcmp(field, "fold_index")) return info.fold_index;
     utils::Error("unknown field %s", field);
@@ -125,6 +128,7 @@ struct MetaInfo {
   }
   // try to load weight information from file, if exists
   inline bool TryLoadFloatInfo(const char *field, const char* fname, bool silent = false) {
+    using namespace std;
     std::vector<float> &data = this->GetFloatInfo(field);
     FILE *fi = fopen64(fname, "r");
     if (fi == NULL) return false;

src/learner/evaluation-inl.hpp

@@ -147,10 +147,11 @@ struct EvalAMS : public IEvaluator {
   explicit EvalAMS(const char *name) {
     name_ = name;
     // note: ams@0 will automatically select which ratio to go
-    utils::Check(sscanf(name, "ams@%f", &ratio_) == 1, "invalid ams format");
+    utils::Check(std::sscanf(name, "ams@%f", &ratio_) == 1, "invalid ams format");
   }
   virtual float Eval(const std::vector<float> &preds,
                      const MetaInfo &info) const {
+    using namespace std;
     const bst_omp_uint ndata = static_cast<bst_omp_uint>(info.labels.size());
 
     utils::Check(info.weights.size() == ndata, "we need weight to evaluate ams");
@@ -202,6 +203,7 @@ struct EvalAMS : public IEvaluator {
 struct EvalPrecisionRatio : public IEvaluator{
  public:
   explicit EvalPrecisionRatio(const char *name) : name_(name) {
+    using namespace std;
     if (sscanf(name, "apratio@%f", &ratio_) == 1) {
       use_ap = 1;
     } else {
@@ -342,6 +344,7 @@ struct EvalRankList : public IEvaluator {
 
  protected:
   explicit EvalRankList(const char *name) {
+    using namespace std;
     name_ = name;
     minus_ = false;
     if (sscanf(name, "%*[^@]@%u[-]?", &topn_) != 1) {
@@ -388,7 +391,7 @@ struct EvalNDCG : public EvalRankList{
     for (size_t i = 0; i < rec.size() && i < this->topn_; ++i) {
       const unsigned rel = rec[i].second;
       if (rel != 0) { 
-        sumdcg += ((1 << rel) - 1) / log(i + 2.0);
+        sumdcg += ((1 << rel) - 1) / std::log(i + 2.0);
       }
     }
     return static_cast<float>(sumdcg);

src/learner/evaluation.h

@@ -36,6 +36,7 @@ struct IEvaluator{
 namespace xgboost {
 namespace learner {
 inline IEvaluator* CreateEvaluator(const char *name) {
+  using namespace std;
   if (!strcmp(name, "rmse")) return new EvalRMSE();
   if (!strcmp(name, "error")) return new EvalError();
   if (!strcmp(name, "merror")) return new EvalMatchError();
@@ -56,6 +57,7 @@ inline IEvaluator* CreateEvaluator(const char *name) {
 class EvalSet{
  public:
   inline void AddEval(const char *name) {
+    using namespace std;
     for (size_t i = 0; i < evals_.size(); ++i) {
       if (!strcmp(name, evals_[i]->Name())) return;
     }

src/learner/learner-inl.hpp

@@ -79,6 +79,7 @@ class BoostLearner {
    * \param val  value of the parameter
    */
   inline void SetParam(const char *name, const char *val) {
+    using namespace std;
     // in this version, bst: prefix is no longer required 
     if (strncmp(name, "bst:", 4) != 0) {
       std::string n = "bst:"; n += name;
@@ -290,7 +291,7 @@ class BoostLearner {
       base_score = 0.5f;
       num_feature = 0;
       num_class = 0;
-      memset(reserved, 0, sizeof(reserved));
+      std::memset(reserved, 0, sizeof(reserved));
     }
     /*!
      * \brief set parameters from outside
@@ -298,6 +299,7 @@ class BoostLearner {
      * \param val value of the parameter
      */
     inline void SetParam(const char *name, const char *val) {
+      using namespace std;
       if (!strcmp("base_score", name)) base_score = static_cast<float>(atof(val));
       if (!strcmp("num_class", name)) num_class = atoi(val);
       if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);

src/learner/objective-inl.hpp

@@ -101,6 +101,7 @@ class RegLossObj : public IObjFunction{
   }
   virtual ~RegLossObj(void) {}
   virtual void SetParam(const char *name, const char *val) {
+    using namespace std;
     if (!strcmp("scale_pos_weight", name)) {
       scale_pos_weight = static_cast<float>(atof(val));
     }
@@ -156,6 +157,7 @@ class SoftmaxMultiClassObj : public IObjFunction {
   }
   virtual ~SoftmaxMultiClassObj(void) {}
   virtual void SetParam(const char *name, const char *val) {
+    using namespace std;
     if (!strcmp( "num_class", name )) nclass = atoi(val);
   }
   virtual void GetGradient(const std::vector<float> &preds,
@@ -247,6 +249,7 @@ class LambdaRankObj : public IObjFunction {
   }
   virtual ~LambdaRankObj(void) {}
   virtual void SetParam(const char *name, const char *val) {
+    using namespace std;
     if (!strcmp( "loss_type", name )) loss.loss_type = atoi(val);
     if (!strcmp( "fix_list_weight", name)) fix_list_weight = static_cast<float>(atof(val));
     if (!strcmp( "num_pairsample", name)) num_pairsample = atoi(val);

src/learner/objective.h

@@ -67,6 +67,7 @@ namespace xgboost {
 namespace learner {
 /*! \brief factory funciton to create objective function by name */
 inline IObjFunction* CreateObjFunction(const char *name) {
+  using namespace std;
   if (!strcmp("reg:linear", name)) return new RegLossObj(LossType::kLinearSquare);
   if (!strcmp("reg:logistic", name)) return new RegLossObj(LossType::kLogisticNeglik);
   if (!strcmp("binary:logistic", name)) return new RegLossObj(LossType::kLogisticClassify);