DecisionTree.h

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#ifndef GRT_DECISION_TREE_HEADER
#define GRT_DECISION_TREE_HEADER

#include "../../CoreModules/Classifier.h"
#include "../../CoreAlgorithms/Tree/Tree.h"
#include "DecisionTreeNode.h"
#include "DecisionTreeThresholdNode.h"
#include "DecisionTreeClusterNode.h"
#include "DecisionTreeTripleFeatureNode.h"

GRT_BEGIN_NAMESPACE

class DecisionTree : public Tree, public Classifier
{
public:
 DecisionTree(const DecisionTreeNode &decisionTreeNode = DecisionTreeClusterNode(),const UINT minNumSamplesPerNode=5,const UINT maxDepth=10,const bool removeFeaturesAtEachSpilt = false,const UINT trainingMode = BEST_ITERATIVE_SPILT,const UINT numSplittingSteps=100,const bool useScaling=false );
    
    DecisionTree(const DecisionTree &rhs);
    
 virtual ~DecisionTree(void);
    
 DecisionTree &operator=(const DecisionTree &rhs);
    
 virtual bool deepCopyFrom(const Classifier *classifier);
    
    virtual bool train_(ClassificationData &trainingData);
    
    virtual bool predict_(VectorFloat &inputVector);
    
    virtual bool clear();
    
    virtual bool recomputeNullRejectionThresholds();
    
    virtual bool saveModelToFile( std::fstream &file ) const;
    
    virtual bool loadModelFromFile( std::fstream &file );
    
    virtual bool getModel( std::ostream &stream ) const;

    DecisionTreeNode* deepCopyTree() const;
    
    const DecisionTreeNode* getTree() const;
    
    DecisionTreeNode* deepCopyDecisionTreeNode() const;
    
    bool setDecisionTreeNode( const DecisionTreeNode &node );
    
    //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;
    using MLBase::print;
    
protected:
    bool loadLegacyModelFromFile_v1( std::fstream &file );
    bool loadLegacyModelFromFile_v2( std::fstream &file );
    bool loadLegacyModelFromFile_v3( std::fstream &file );
    
    DecisionTreeNode* buildTree(ClassificationData &trainingData, DecisionTreeNode *parent, Vector< UINT > features, const Vector< UINT > &classLabels, UINT nodeID );
    Float getNodeDistance( const VectorFloat &x, const UINT nodeID );
    Float getNodeDistance( const VectorFloat &x, const VectorFloat &y );
    
    DecisionTreeNode* decisionTreeNode;
    std::map< UINT, VectorFloat > nodeClusters;
    VectorFloat classClusterMean;
    VectorFloat classClusterStdDev;
    static RegisterClassifierModule< DecisionTree > registerModule;
    
};

GRT_END_NAMESPACE

#endif //GRT_DECISION_TREE_HEADER