RegressionTree.h

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#ifndef GRT_REGRESSION_TREE_HEADER
#define GRT_REGRESSION_TREE_HEADER

#include "../../CoreModules/Regressifier.h"
#include "../../CoreAlgorithms/Tree/Tree.h"
#include "RegressionTreeNode.h"

GRT_BEGIN_NAMESPACE

class GRT_API RegressionTree : public Regressifier
{
public:
 RegressionTree(const UINT numSplittingSteps=100,const UINT minNumSamplesPerNode=5,const UINT maxDepth=10,const bool removeFeaturesAtEachSpilt = false,const Tree::TrainingMode trainingMode = Tree::BEST_ITERATIVE_SPILT,const bool useScaling=false,const Float minRMSErrorPerNode = 0.01);
 
 RegressionTree(const RegressionTree &rhs);
 
 virtual ~RegressionTree(void);
 
 RegressionTree &operator=(const RegressionTree &rhs);
 
 virtual bool deepCopyFrom(const Regressifier *regressifier) override;
 
 virtual bool train_(RegressionData &trainingData) override;
 
 virtual bool predict_(VectorFloat &inputVector) override;
 
 virtual bool clear() override;
 
 virtual bool print() const override;
 
 virtual bool save( std::fstream &file ) const override;
 
 virtual bool load( std::fstream &file ) override;

 RegressionTreeNode* deepCopyTree() const;
 
 const RegressionTreeNode* getTree() const;
 
 Float getMinRMSErrorPerNode() const;

 Tree::TrainingMode getTrainingMode() const;
 
 UINT getNumSplittingSteps() const;
 
 UINT getMinNumSamplesPerNode() const;
 
 UINT getMaxDepth() const;
 
 UINT getPredictedNodeID() const;
 
 bool getRemoveFeaturesAtEachSpilt() const;
 
 bool setTrainingMode(const Tree::TrainingMode trainingMode);
 
 bool setNumSplittingSteps(const UINT numSplittingSteps);
 
 bool setMinNumSamplesPerNode(const UINT minNumSamplesPerNode);
 
 bool setMaxDepth(const UINT maxDepth);
 
 bool setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt);
 
 bool setMinRMSErrorPerNode(const Float minRMSErrorPerNode);

 static std::string getId();
 
 //Tell the compiler we are using the base class train method to stop hidden virtual function warnings
 using MLBase::save;
 using MLBase::load;
 using MLBase::train_; 
 using MLBase::predict_; 
 
protected:
 Node *tree; 
 UINT minNumSamplesPerNode;
 UINT maxDepth;
 UINT numSplittingSteps;
 bool removeFeaturesAtEachSpilt;
 Tree::TrainingMode trainingMode;
 Float minRMSErrorPerNode;
 
 RegressionTreeNode* buildTree( const RegressionData &trainingData, RegressionTreeNode *parent, Vector< UINT > features, UINT nodeID );
 bool computeBestSpilt( const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );
 bool computeBestSpiltBestIterativeSpilt( const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );
 //bool computeBestSpiltBestRandomSpilt( const RegressionData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &threshold, Float &minError );
 bool computeNodeRegressionData( const RegressionData &trainingData, VectorFloat &regressionData );

private:
 static RegisterRegressifierModule< RegressionTree > registerModule;
 static const std::string id;
 
};

GRT_END_NAMESPACE

#endif //GRT_REGRESSION_TREE_HEADER