add ntree limit
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@@ -212,11 +212,14 @@ class BoostLearner {
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* \param data input data
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* \param output_margin whether to only predict margin value instead of transformed prediction
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* \param out_preds output vector that stores the prediction
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* \param ntree_limit limit number of trees used for boosted tree
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* predictor, when it equals 0, this means we are using all the trees
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*/
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inline void Predict(const DMatrix &data,
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bool output_margin,
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std::vector<float> *out_preds) const {
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this->PredictRaw(data, out_preds);
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std::vector<float> *out_preds,
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unsigned ntree_limit = 0) const {
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this->PredictRaw(data, out_preds, ntree_limit);
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if (!output_margin) {
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obj_->PredTransform(out_preds);
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}
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@@ -246,11 +249,14 @@ class BoostLearner {
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* \brief get un-transformed prediction
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* \param data training data matrix
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* \param out_preds output vector that stores the prediction
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* \param ntree_limit limit number of trees used for boosted tree
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* predictor, when it equals 0, this means we are using all the trees
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*/
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inline void PredictRaw(const DMatrix &data,
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std::vector<float> *out_preds) const {
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std::vector<float> *out_preds,
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unsigned ntree_limit = 0) const {
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gbm_->Predict(data.fmat(), this->FindBufferOffset(data),
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data.info.info, out_preds);
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data.info.info, out_preds, ntree_limit);
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// add base margin
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std::vector<float> &preds = *out_preds;
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const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
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