ANBC.h

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#ifndef GRT_ANBC_HEADER
#define GRT_ANBC_HEADER

#include "ANBC_Model.h"
#include "../../CoreModules/Classifier.h"

GRT_BEGIN_NAMESPACE
 
#define MIN_SCALE_VALUE 1.0e-10
#define MAX_SCALE_VALUE 1

class ANBC : public Classifier
{
public:
 ANBC(bool useScaling=false,bool useNullRejection=false,double nullRejectionCoeff=10.0);
 
 ANBC(const ANBC &rhs);
 
 virtual ~ANBC(void);
 
 ANBC &operator=(const ANBC &rhs);
 
 virtual bool deepCopyFrom(const Classifier *classifier);
 
 virtual bool train_(ClassificationData &trainingData);
 
 virtual bool predict_(VectorFloat &inputVector);
 
 virtual bool reset();
 
 virtual bool clear();
 
 virtual bool saveModelToFile( std::fstream &file ) const;
 
 virtual bool loadModelFromFile( std::fstream &file );
 
 virtual bool recomputeNullRejectionThresholds();
 
 VectorFloat getNullRejectionThresholds() const;
 
 Vector< ANBC_Model > getModels(){ return models; }
 
 //Setters
 bool setNullRejectionCoeff(double nullRejectionCoeff);
 
 bool setWeights(const ClassificationData &weightsData);
 
 bool clearWeights(){ weightsDataSet = false; weightsData.clear(); return true; }
 
 //Tell the compiler we are using the following functions from the MLBase class to stop hidden virtual function warnings
 using MLBase::saveModelToFile;
 using MLBase::loadModelFromFile;
 using MLBase::train;
 using MLBase::train_;
 using MLBase::predict;
 using MLBase::predict_;

protected:
 bool loadLegacyModelFromFile( std::fstream &file );
 
 bool weightsDataSet; //A flag to indicate if the user has manually set the weights buffer
 ClassificationData weightsData; //The weights of each feature for each class for training the algorithm
 Vector< ANBC_Model > models; //A buffer to hold all the models
 
 static RegisterClassifierModule< ANBC > registerModule;
};

GRT_END_NAMESPACE

#endif // GRT_WEAK_CLASSIFIER_HEADER