DecisionTreeNode.h

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#ifndef GRT_DECISION_TREE_NODE_HEADER
#define GRT_DECISION_TREE_NODE_HEADER

#include "../../CoreAlgorithms/Tree/Node.h"
#include "../../CoreAlgorithms/Tree/Tree.h"
#include "../../DataStructures/ClassificationData.h"

GRT_BEGIN_NAMESPACE
 
class GRT_API DecisionTreeNode : public Node{
public:
 DecisionTreeNode( const std::string id = "DecisionTreeNode" );

 DecisionTreeNode(const DecisionTreeNode &rhs) = delete;
 
 virtual ~DecisionTreeNode();

 DecisionTreeNode& operator=(const DecisionTreeNode &rhs) = delete;
 
 virtual bool predict_(VectorFloat &x,VectorFloat &classLikelihoods) override;
 
 virtual bool computeBestSplit( const UINT &trainingMode, const UINT &numSplittingSteps,const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError );

 virtual bool clear() override;
 
 virtual bool getModel( std::ostream &stream ) const override;
 
 virtual Node* deepCopy() const override;
 
 UINT getNodeSize() const;
 
 UINT getNumClasses() const;
 
 VectorFloat getClassProbabilities() const;
 
 bool setLeafNode( const UINT nodeSize, const VectorFloat &classProbabilities );
 
 bool setNodeSize(const UINT nodeSize);
 
 bool setClassProbabilities(const VectorFloat &classProbabilities);
 
 static UINT getClassLabelIndexValue(UINT classLabel,const Vector< UINT > &classLabels);
 
 using Node::predict;
 using Node::predict_;
 
protected:
 virtual bool computeBestSplitBestIterativeSplit( const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError ){
 
 errorLog << __GRT_LOG__ << " Base class not overwritten!" << std::endl;
 
 return false;
 }
 
 virtual bool computeBestSplitBestRandomSplit( const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError ){
 
 errorLog << __GRT_LOG__ << " Base class not overwritten!" << std::endl;
 
 return false;
 }
 
 virtual bool saveParametersToFile( std::fstream &file ) const override{
 
 if( !file.is_open() )
 {
 errorLog << __GRT_LOG__ << " File is not open!" << std::endl;
 return false;
 }
 
 //Save the custom DecisionTreeNode parameters
 file << "NodeSize: " << nodeSize << std::endl;
 file << "NumClasses: " << classProbabilities.size() << std::endl;
 file << "ClassProbabilities: ";
 if( classProbabilities.size() > 0 ){
 for(UINT i=0; i<classProbabilities.size(); i++){
 file << classProbabilities[i];
 if( i < classProbabilities.size()-1 ) file << "\t";
 else file << std::endl;
 }
 }
 
 return true;
 }
 
 virtual bool loadParametersFromFile( std::fstream &file ) override{
 
 if( !file.is_open() )
 {
 errorLog << __GRT_LOG__ << " File is not open!" << std::endl;
 return false;
 }
 
 classProbabilities.clear();
 
 std::string word;
 UINT numClasses;
 
 //Load the custom DecisionTreeNode Parameters
 file >> word;
 if( word != "NodeSize:" ){
 errorLog << __GRT_LOG__ << " Failed to find NodeSize header!" << std::endl;
 return false;
 }
 file >> nodeSize;
 
 file >> word;
 if( word != "NumClasses:" ){
 errorLog << __GRT_LOG__ << " Failed to find NumClasses header!" << std::endl;
 return false;
 }
 file >> numClasses;
 if( numClasses > 0 )
 classProbabilities.resize( numClasses );
 
 file >> word;
 if( word != "ClassProbabilities:" ){
 errorLog << __GRT_LOG__ << " Failed to find ClassProbabilities header!" << std::endl;
 return false;
 }
 if( numClasses > 0 ){
 for(UINT i=0; i<numClasses; i++){
 file >> classProbabilities[i];
 }
 }
 
 return true;
 }
 
 UINT nodeSize;
 VectorFloat classProbabilities;
 
 static RegisterNode< DecisionTreeNode > registerModule;
};

GRT_END_NAMESPACE

#endif //GRT_DECISION_TREE_NODE_HEADER