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#ifndef ROOT_GBRForest
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#define ROOT_GBRForest
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//////////////////////////////////////////////////////////////////////////
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// //
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// GBRForest //
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// //
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// A fast minimal implementation of Gradient-Boosted Regression Trees //
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// which has been especially optimized for size on disk and in memory. //
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// //
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// Designed to built from TMVA-trained trees, but could also be //
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// generalized to otherwise-trained trees, classification, //
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// or other boosting methods in the future //
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// //
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// Josh Bendavid - MIT //
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//////////////////////////////////////////////////////////////////////////
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#include "TNamed.h"
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#include <vector>
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#include "GBRTree.h"
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namespace TMVA {
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class MethodBDT;
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}
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class GBRForest : public TNamed {
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public:
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GBRForest();
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GBRForest(const TMVA::MethodBDT *bdt);
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virtual ~GBRForest();
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Double_t GetResponse(const Float_t* vector) const;
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std::vector<GBRTree*> &Trees() { return fTrees; }
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protected:
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Double_t fInitialResponse;
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std::vector<GBRTree*> fTrees;
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ClassDef(GBRForest,1) // Node for the Decision Tree
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};
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//_______________________________________________________________________
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inline Double_t GBRForest::GetResponse(const Float_t* vector) const {
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Double_t response = fInitialResponse;
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for (std::vector<GBRTree*>::const_iterator it=fTrees.begin(); it!=fTrees.end(); ++it) {
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response += (*it)->GetResponse(vector);
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}
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return response;
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}
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#endif
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