LDA.h

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#ifndef GRT_LDA_HEADER
#define GRT_LDA_HEADER

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

GRT_BEGIN_NAMESPACE
    
class LDAClassModel{
public:
    LDAClassModel(){
        classLabel = 0;
        priorProb = 0;
    }
    
    ~LDAClassModel(){
        
    }
    
    UINT getNumDimensions() const { return weights.getSize(); }
    
    UINT classLabel;
    Float priorProb;
    VectorDouble weights;
    
};
    
class LDA : public Classifier
{
public:
 LDA(bool useScaling=false,bool useNullRejection=true,Float nullRejectionCoeff=10.0);
    
 virtual ~LDA(void);
    
 LDA &operator=(const LDA &rhs){
  if( this != &rhs ){
            //LDA variables
            this->models = rhs.models;
            
            //Classifier variables
            copyBaseVariables( (Classifier*)&rhs );
  }
  return *this;
 }
    
    //Override the base class methods
    virtual bool deepCopyFrom(const Classifier *classifier){
        if( classifier == NULL ) return false;
        
        if( this->getClassifierType() == classifier->getClassifierType() ){
   
            LDA *ptr = (LDA*)classifier;
            //Clone the LDA values 
            this->models = ptr->models;
            
            //Clone the classifier variables
            return copyBaseVariables( classifier );
        }
        return false;
    }
    
    virtual bool train(ClassificationData trainingData);
    
    virtual bool predict(VectorDouble inputVector);
    
    virtual bool saveModelToFile( std::fstream &file ) const;
    
    virtual bool loadModelFromFile( std::fstream &file );
    
    //Getters
    Vector< LDAClassModel > getModels(){ if( trained ){ return models; } return Vector< LDAClassModel >(); }
    
    //Setters

    //Tell the compiler we are using the base class train method to stop hidden virtual function warnings
    using MLBase::saveModelToFile;
    using MLBase::loadModelFromFile;

private:
 MatrixFloat computeBetweenClassScatterMatrix( ClassificationData &data );
 MatrixFloat computeWithinClassScatterMatrix( ClassificationData &data );
 
    Vector< LDAClassModel > models;
};

GRT_END_NAMESPACE

#endif //GRT_LDA_HEADER