TrainingResult.h

Go to the documentation of this file.

#ifndef GRT_TRAINING_RESULT_HEADER
#define GRT_TRAINING_RESULT_HEADER

namespace GRT {
    
//Forward declartion of MLBase class
class MLBase;

class TrainingResult{
public:
    TrainingResult(){
        trainingMode = CLASSIFICATION_MODE;
        trainingIteration = 0;
        accuracy = 0;
        totalSquaredTrainingError = 0;
        rootMeanSquaredTrainingError = 0;
        trainer = NULL;
    }
    
    TrainingResult(const TrainingResult &rhs){
        *this = rhs;
    }
    
    ~TrainingResult(){
        
    }

    TrainingResult& operator=(const TrainingResult &rhs){
        if( this != &rhs ){
            this->trainingMode = rhs.trainingMode;
            this->trainingIteration = rhs.trainingIteration;
            this->accuracy = rhs.accuracy;
            this->totalSquaredTrainingError = rhs.totalSquaredTrainingError;
            this->rootMeanSquaredTrainingError = rhs.rootMeanSquaredTrainingError;
            this->trainer = rhs.trainer;
        }
        return *this;
    }
    
    unsigned int getTrainingMode() const{
        return trainingMode;
    }
    
    unsigned int getTrainingIteration() const{
        return trainingIteration;
    }
    
    Float getAccuracy() const{
        return accuracy;
    }
    
    Float getTotalSquaredTrainingError() const{
        return totalSquaredTrainingError;
    }
    
    Float getRootMeanSquaredTrainingError() const{
        return rootMeanSquaredTrainingError;
    }
    
    MLBase* getTrainer() const{
        return trainer;
    }
    
    bool setClassificationResult(unsigned int trainingIteration,Float accuracy,MLBase *trainer){
        this->trainingMode = CLASSIFICATION_MODE;
        this->trainingIteration = trainingIteration;
        this->accuracy = accuracy;
        this->trainer = trainer;
        return true;
    }
    
    bool setRegressionResult(unsigned int trainingIteration,Float totalSquaredTrainingError,Float rootMeanSquaredTrainingError,MLBase *trainer){
        this->trainingMode = REGRESSION_MODE;
        this->trainingIteration = trainingIteration;
        this->totalSquaredTrainingError = totalSquaredTrainingError;
        this->rootMeanSquaredTrainingError = rootMeanSquaredTrainingError;
        this->trainer = trainer;
        return true;
    }

protected:
    
    unsigned int trainingMode;
    unsigned int trainingIteration;
    Float accuracy;
    Float totalSquaredTrainingError;
    Float rootMeanSquaredTrainingError;
    MLBase *trainer;
    
public:
    
    enum TrainingMode{CLASSIFICATION_MODE=0,REGRESSION_MODE};

};

}//End of namespace GRT

#endif //GRT_TRAINING_RESULT_HEADER