change omp loop var to bst_omp_uint, add XGB_DLL to wrapper

src/learner/evaluation-inl.hpp

@@ -26,10 +26,10 @@ struct EvalEWiseBase : public IEvaluator {
                      const MetaInfo &info) const {
     utils::Check(preds.size() == info.labels.size(),
                  "label and prediction size not match");
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     float sum = 0.0, wsum = 0.0;
     #pragma omp parallel for reduction(+: sum, wsum) schedule(static)
-    for (unsigned i = 0; i < ndata; ++i) {
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       const float wt = info.GetWeight(i);
       sum += Derived::EvalRow(info.labels[i], preds[i]) * wt;
       wsum += wt;
@@ -109,12 +109,12 @@ struct EvalAMS : public IEvaluator {
   }
   virtual float Eval(const std::vector<float> &preds,
                      const MetaInfo &info) const {
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     utils::Check(info.weights.size() == ndata, "we need weight to evaluate ams");
     std::vector< std::pair<float, unsigned> > rec(ndata);
 
     #pragma omp parallel for schedule(static)
-    for (unsigned i = 0; i < ndata; ++i) {
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       rec[i] = std::make_pair(preds[i], i);
     }
     std::sort(rec.begin(), rec.end(), CmpFirst);
@@ -211,7 +211,7 @@ struct EvalAuc : public IEvaluator {
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
     utils::Check(gptr.back() == preds.size(),
                  "EvalAuc: group structure must match number of prediction");
-    const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
+    const bst_omp_uint ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
     // sum statictis
     double sum_auc = 0.0f;
     #pragma omp parallel reduction(+:sum_auc)
@@ -219,7 +219,7 @@ struct EvalAuc : public IEvaluator {
       // each thread takes a local rec
       std::vector< std::pair<float, unsigned> > rec;
       #pragma omp for schedule(static)
-      for (unsigned k = 0; k < ngroup; ++k) {
+      for (bst_omp_uint k = 0; k < ngroup; ++k) {
         rec.clear();
         for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
           rec.push_back(std::make_pair(preds[j], j));
@@ -269,7 +269,7 @@ struct EvalRankList : public IEvaluator {
     utils::Assert(gptr.size() != 0, "must specify group when constructing rank file");
     utils::Assert(gptr.back() == preds.size(),
                    "EvalRanklist: group structure must match number of prediction");
-    const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
+    const bst_omp_uint ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
     // sum statistics
     double sum_metric = 0.0f;
     #pragma omp parallel reduction(+:sum_metric)
@@ -277,7 +277,7 @@ struct EvalRankList : public IEvaluator {
       // each thread takes a local rec
       std::vector< std::pair<float, unsigned> > rec;
       #pragma omp for schedule(static)
-      for (unsigned k = 0; k < ngroup; ++k) {
+      for (bst_omp_uint k = 0; k < ngroup; ++k) {
         rec.clear();
         for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
           rec.push_back(std::make_pair(preds[j], static_cast<int>(info.labels[j])));

src/learner/learner-inl.hpp

@@ -253,17 +253,17 @@ class BoostLearner {
                   data.info.info, out_preds);
     // add base margin
     std::vector<float> &preds = *out_preds;
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     if (data.info.base_margin.size() != 0) {
       utils::Check(preds.size() == data.info.base_margin.size(),
                    "base_margin.size does not match with prediction size");
       #pragma omp parallel for schedule(static)
-      for (unsigned j = 0; j < ndata; ++j) {
+      for (bst_omp_uint j = 0; j < ndata; ++j) {
         preds[j] += data.info.base_margin[j];
       }
     } else {
       #pragma omp parallel for schedule(static)
-      for (unsigned j = 0; j < ndata; ++j) {
+      for (bst_omp_uint j = 0; j < ndata; ++j) {
         preds[j] += mparam.base_score;
       }
     }

src/learner/objective-inl.hpp

@@ -116,9 +116,9 @@ class RegLossObj : public IObjFunction{
     gpair.resize(preds.size());
     // start calculating gradient
     const unsigned nstep = static_cast<unsigned>(info.labels.size());
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     #pragma omp parallel for schedule(static)
-    for (unsigned i = 0; i < ndata; ++i) {
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       const unsigned j = i % nstep;
       float p = loss.PredTransform(preds[i]);
       float w = info.GetWeight(j);
@@ -132,9 +132,9 @@ class RegLossObj : public IObjFunction{
   }
   virtual void PredTransform(std::vector<float> *io_preds) {
     std::vector<float> &preds = *io_preds;
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
     #pragma omp parallel for schedule(static)
-    for (unsigned j = 0; j < ndata; ++j) {
+    for (bst_omp_uint j = 0; j < ndata; ++j) {
       preds[j] = loss.PredTransform(preds[j]);
     }
   }
@@ -169,12 +169,12 @@ class SoftmaxMultiClassObj : public IObjFunction {
     std::vector<bst_gpair> &gpair = *out_gpair;
     gpair.resize(preds.size());
     const unsigned nstep = static_cast<unsigned>(info.labels.size() * nclass);
-    const unsigned ndata = static_cast<unsigned>(preds.size() / nclass);
+    const unsigned ndata = static_cast<bst_omp_uint>(preds.size() / nclass);
     #pragma omp parallel
     {
       std::vector<float> rec(nclass);
       #pragma omp for schedule(static)
-      for (unsigned i = 0; i < ndata; ++i) {
+      for (bst_omp_uint i = 0; i < ndata; ++i) {
         for (int k = 0; k < nclass; ++k) {
           rec[k] = preds[i * nclass + k];
         }
@@ -210,13 +210,13 @@ class SoftmaxMultiClassObj : public IObjFunction {
     utils::Check(nclass != 0, "must set num_class to use softmax");
     std::vector<float> &preds = *io_preds;
     std::vector<float> tmp;
-    const unsigned ndata = static_cast<unsigned>(preds.size()/nclass);
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size()/nclass);
     if (prob == 0) tmp.resize(ndata);
     #pragma omp parallel
     {
       std::vector<float> rec(nclass);
       #pragma omp for schedule(static)
-      for (unsigned j = 0; j < ndata; ++j) {
+      for (bst_omp_uint j = 0; j < ndata; ++j) {
         for (int k = 0; k < nclass; ++k) {
           rec[k] = preds[j * nclass + k];
         }
@@ -263,7 +263,7 @@ class LambdaRankObj : public IObjFunction {
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
     utils::Check(gptr.size() != 0 && gptr.back() == info.labels.size(),
                  "group structure not consistent with #rows");
-    const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
+    const bst_omp_uint ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
     #pragma omp parallel
     {
       // parall construct, declare random number generator here, so that each
@@ -273,7 +273,7 @@ class LambdaRankObj : public IObjFunction {
       std::vector<ListEntry>  lst;
       std::vector< std::pair<float, unsigned> > rec;
       #pragma omp for schedule(static)
-      for (unsigned k = 0; k < ngroup; ++k) {
+      for (bst_omp_uint k = 0; k < ngroup; ++k) {
         lst.clear(); pairs.clear();
         for (unsigned j = gptr[k]; j < gptr[k+1]; ++j) {
           lst.push_back(ListEntry(preds[j], info.labels[j], j));