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 RegressionTree : public Tree, public Regressifier
{
public:
 RegressionTree(const UINT numSplittingSteps=100,const UINT minNumSamplesPerNode=5,const UINT maxDepth=10,const bool removeFeaturesAtEachSpilt = false,const UINT trainingMode = 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);
    
    virtual bool train_(RegressionData &trainingData);
    
    virtual bool predict_(VectorFloat &inputVector);
    
    virtual bool clear();
    
    virtual bool print() const;
    
    virtual bool saveModelToFile( std::fstream &file ) const;
    
    virtual bool loadModelFromFile( std::fstream &file );

    RegressionTreeNode* deepCopyTree() const;
    
    const RegressionTreeNode* getTree() const;
    
    Float getMinRMSErrorPerNode() const;
    
    bool setMinRMSErrorPerNode(const Float minRMSErrorPerNode);
    
    //Tell the compiler we are using the base class train method to stop hidden virtual function warnings
    using MLBase::saveModelToFile;
    using MLBase::loadModelFromFile;
    using MLBase::train; 
    using MLBase::predict; 
    
protected:
    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 );

    static RegisterRegressifierModule< RegressionTree > registerModule;
    
};

GRT_END_NAMESPACE

#endif //GRT_REGRESSION_TREE_HEADER