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