KNN.h

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#ifndef GRT_KNN_HEADER
#define GRT_KNN_HEADER

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

GRT_BEGIN_NAMESPACE
    
#define BIG_DISTANCE 99e+99

class KNN : public Classifier
{
public:
 KNN(UINT K=10,bool useScaling=false,bool useNullRejection=false,Float nullRejectionCoeff=10.0,bool searchForBestKValue = false,UINT minKSearchValue = 1,UINT maxKSearchValue = 10);
    
    KNN(const KNN &rhs);
    
 virtual ~KNN(void);
    
 KNN &operator=(const KNN &rhs);
    
    virtual bool deepCopyFrom(const Classifier *classifier);
    
    virtual bool train_(ClassificationData &trainingData);
    
    virtual bool predict_(VectorFloat &inputVector);
    
    virtual bool clear();
    
    virtual bool saveModelToFile( std::fstream &file ) const;
    
    virtual bool loadModelFromFile( std::fstream &file );
    
    virtual bool recomputeNullRejectionThresholds();
    
    UINT getK(){ return K; }
    
    UINT getDistanceMethod(){ return distanceMethod; }
    
    //Setters
    bool setK(UINT K);
    
    bool setMinKSearchValue(UINT minKSearchValue);
    
    bool setMaxKSearchValue(UINT maxKSearchValue);
    
    bool enableBestKValueSearch(bool searchForBestKValue);
    
    bool setNullRejectionCoeff(Float nullRejectionCoeff);
    
    bool setDistanceMethod(UINT distanceMethod);
    
    //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 train_(const ClassificationData &trainingData,const UINT K);
    bool predict(const VectorFloat &inputVector,const UINT K);
    bool loadLegacyModelFromFile( std::fstream &file );
    Float computeEuclideanDistance(const VectorFloat &a,const VectorFloat &b);
    Float computeCosineDistance(const VectorFloat &a,const VectorFloat &b);
    Float computeManhattanDistance(const VectorFloat &a,const VectorFloat &b);
    
    UINT K;                                     
    UINT distanceMethod;                        
    bool searchForBestKValue;                   
    UINT minKSearchValue;                       
    UINT maxKSearchValue;                       
    ClassificationData trainingData;            
    VectorFloat trainingMu;                    
    VectorFloat trainingSigma;                 
    
    static RegisterClassifierModule< KNN > registerModule;
    
public:
    enum DistanceMethods{EUCLIDEAN_DISTANCE=0,COSINE_DISTANCE,MANHATTAN_DISTANCE};
 
};

GRT_END_NAMESPACE

#endif //GRT_KNN_HEADER