ClusterTree.h

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#ifndef GRT_CLUSTER_TREE_HEADER
#define GRT_CLUSTER_TREE_HEADER

#include "../../CoreModules/Clusterer.h"
#include "../../CoreAlgorithms/Tree/Tree.h"
#include "ClusterTreeNode.h"

GRT_BEGIN_NAMESPACE

class ClusterTree : public Tree, public Clusterer
{
public:
    ClusterTree(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);
    
    ClusterTree(const ClusterTree &rhs);
    
     virtual ~ClusterTree(void);
    
    ClusterTree &operator=(const ClusterTree &rhs);

    virtual bool deepCopyFrom(const Clusterer *cluster);
    
    virtual bool train_(MatrixFloat &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 );

    ClusterTreeNode* deepCopyTree() const;
    
    const ClusterTreeNode* getTree() const;
    
    UINT getPredictedClusterLabel() 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;
    
    ClusterTreeNode* buildTree( const MatrixFloat &trainingData, ClusterTreeNode *parent, Vector< UINT > features, UINT &clusterLabel, UINT nodeID );
    bool computeBestSpilt( const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );
    bool computeBestSpiltBestIterativeSpilt( const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );
    bool computeBestSpiltBestRandomSpilt( const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );

    static RegisterClustererModule< ClusterTree > registerModule;
    
};

GRT_END_NAMESPACE

#endif //GRT_CLUSTER_TREE_HEADER