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bendavid |
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#ifndef ROOT_GBRTree
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#define ROOT_GBRTree
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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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// The decision tree is implemented here as a set of two arrays, one for
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// intermediate nodes, containing the variable index and cut value, as well
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// as the indices of the 'left' and 'right' daughter nodes. Positive indices
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// indicate further intermediate nodes, whereas negative indices indicate
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// terminal nodes, which are stored simply as a vector of regression responses
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#include <vector>
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#include <map>
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#include "Rtypes.h"
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namespace TMVA {
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class DecisionTree;
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class DecisionTreeNode;
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}
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class GBRTree {
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public:
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GBRTree();
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GBRTree(const TMVA::DecisionTree *tree);
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virtual ~GBRTree();
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Double_t GetResponse(const Float_t* vector) const;
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protected:
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UInt_t CountIntermediateNodes(const TMVA::DecisionTreeNode *node);
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UInt_t CountTerminalNodes(const TMVA::DecisionTreeNode *node);
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void AddNode(const TMVA::DecisionTreeNode *node);
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Int_t fNIntermediateNodes;
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Int_t fNTerminalNodes;
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UChar_t *fCutIndices;//[fNIntermediateNodes]
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Float_t *fCutVals;//[fNIntermediateNodes]
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Int_t *fLeftIndices;//[fNIntermediateNodes]
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Int_t *fRightIndices;//[fNIntermediateNodes]
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Float_t *fResponses;//[fNTerminalNodes]
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ClassDef(GBRTree,1) // Node for the Decision Tree
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};
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//_______________________________________________________________________
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inline Double_t GBRTree::GetResponse(const Float_t* vector) const {
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Int_t index = 0;
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UChar_t cutindex = fCutIndices[0];
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Float_t cutval = fCutVals[0];
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while (true) {
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if (vector[cutindex] > cutval) {
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index = fRightIndices[index];
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}
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else {
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index = fLeftIndices[index];
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}
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if (index>0) {
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cutindex = fCutIndices[index];
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cutval = fCutVals[index];
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}
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else {
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return fResponses[-index];
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}
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}
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}
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#endif
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