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 GRT_API 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 save( std::fstream &file ) const;
 
 virtual bool load( 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 );

 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_;
 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;
 static std::string id;
};

GRT_END_NAMESPACE

#endif //GRT_DECISION_TREE_HEADER