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 GRT_API ClusterTree : public Clusterer
{
public:
 ClusterTree(const UINT numSplittingSteps=100,const UINT minNumSamplesPerNode=5,const UINT maxDepth=10,const bool removeFeaturesAtEachSplit = false,const Tree::TrainingMode trainingMode = Tree::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) override;
 
 virtual bool train_(MatrixFloat &trainingData) override;
 
 virtual bool predict_(VectorFloat &inputVector) override;
 
 virtual bool clear() override;
 
 virtual bool print() const override;
 
 virtual bool saveModelToFile( std::fstream &file ) const override;
 
 virtual bool loadModelFromFile( std::fstream &file ) override;

 ClusterTreeNode* deepCopyTree() const;
 
 const ClusterTreeNode* getTree() const;
 
 UINT getPredictedClusterLabel() const;
 
 Float getMinRMSErrorPerNode() const;

 Tree::TrainingMode getTrainingMode() const;
 
 UINT getNumSplittingSteps() const;
 
 UINT getMinNumSamplesPerNode() const;
 
 UINT getMaxDepth() const;
 
 UINT getPredictedNodeID() const;
 
 bool getRemoveFeaturesAtEachSplit() const;
 
 bool setTrainingMode(const Tree::TrainingMode trainingMode);
 
 bool setNumSplittingSteps(const UINT numSplittingSteps);
 
 bool setMinNumSamplesPerNode(const UINT minNumSamplesPerNode);
 
 bool setMaxDepth(const UINT maxDepth);
 
 bool setRemoveFeaturesAtEachSplit(const bool removeFeaturesAtEachSplit);

 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;

 static std::string getId();
 
protected:
 Node *tree; 
 UINT minNumSamplesPerNode;
 UINT maxDepth;
 UINT numSplittingSteps;
 bool removeFeaturesAtEachSplit;
 Tree::TrainingMode trainingMode;
 Float minRMSErrorPerNode;
 
 ClusterTreeNode* buildTree( const MatrixFloat &trainingData, ClusterTreeNode *parent, Vector< UINT > features, UINT &clusterLabel, UINT nodeID );
 bool computeBestSplit( const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );
 bool computeBestSplitBestIterativeSplit( const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );
 bool computeBestSplitBestRandomSplit( const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError );

private:
 static RegisterClustererModule< ClusterTree > registerModule;
 static const std::string id;
};

GRT_END_NAMESPACE

#endif //GRT_CLUSTER_TREE_HEADER