GRT::TrainingResult Class Reference

Public Types
enum	TrainingMode { CLASSIFICATION_MODE =0, REGRESSION_MODE }

Public Member Functions
	TrainingResult ()
	TrainingResult (const TrainingResult &rhs)
	~TrainingResult ()
TrainingResult &	operator= (const TrainingResult &rhs)
unsigned int	getTrainingMode () const
unsigned int	getTrainingIteration () const
Float	getAccuracy () const
Float	getTotalSquaredTrainingError () const
Float	getRootMeanSquaredTrainingError () const
MLBase *	getTrainer () const
bool	setClassificationResult (unsigned int trainingIteration, Float accuracy, MLBase *trainer)
bool	setRegressionResult (unsigned int trainingIteration, Float totalSquaredTrainingError, Float rootMeanSquaredTrainingError, MLBase *trainer)

Protected Attributes
unsigned int	trainingMode
unsigned int	trainingIteration
Float	accuracy
Float	totalSquaredTrainingError
Float	rootMeanSquaredTrainingError
MLBase *	trainer

Detailed Description

Definition at line 17 of file TrainingResult.h.

Constructor & Destructor Documentation

GRT::TrainingResult::TrainingResult ( )

inline

Default Constructor.

Initializes the TrainingResult instance.

Definition at line 24 of file TrainingResult.h.

GRT::TrainingResult::TrainingResult ( const TrainingResult &  rhs )

inline

Copy Constructor.

Initializes this instance by copying the data from the rhs instance

Parameters

const

TrainingResult &rhs: another instance of the TrainingResult class

Definition at line 40 of file TrainingResult.h.

GRT::TrainingResult::~TrainingResult ( )

inline

Default Destructor.

Definition at line 47 of file TrainingResult.h.

Member Function Documentation

Float GRT::TrainingResult::getAccuracy ( ) const

inline

Gets the accuracy for the training result at the current training iteration. This is only used if the trainingMode is in CLASSIFICATION_MODE.

Returns

returns the accuracy

Definition at line 94 of file TrainingResult.h.

Float GRT::TrainingResult::getRootMeanSquaredTrainingError ( ) const

inline

Gets the root mean squared error for the training data at the current training iteration. This is only used if the trainingMode is in REGRESSION_MODE.

Returns

returns the rootMeanSquaredTrainingError

Definition at line 114 of file TrainingResult.h.

Float GRT::TrainingResult::getTotalSquaredTrainingError ( ) const

inline

Gets the total squared error for the training data at the current training iteration. This is only used if the trainingMode is in REGRESSION_MODE.

Returns

returns the totalSquaredTrainingError

Definition at line 104 of file TrainingResult.h.

MLBase* GRT::TrainingResult::getTrainer ( ) const

inline

Gets a pointer to the class used for training.

Returns

returns a pointer to the trainer

Definition at line 123 of file TrainingResult.h.

unsigned int GRT::TrainingResult::getTrainingIteration ( ) const

inline

Gets the training iteration, this represents which iteration (or epoch) the training results correspond to.

Returns

returns the training iteration

Definition at line 84 of file TrainingResult.h.

unsigned int GRT::TrainingResult::getTrainingMode ( ) const

inline

Gets the current training mode, this will be one of the TrainingMode enums.

Returns

returns the current training mode, this will be one of the TrainingMode enums

Definition at line 75 of file TrainingResult.h.

TrainingResult& GRT::TrainingResult::operator= ( const TrainingResult &  rhs )

inline

Defines the Equals Operator.

This copies the data from the rhs instance to this instance, returning a reference to the current instance.

Parameters

const

TrainingResult &rhs: another instance of the TrainingResult class

Definition at line 58 of file TrainingResult.h.

bool GRT::TrainingResult::setClassificationResult ( unsigned int  trainingIteration, Float  accuracy, MLBase *  trainer  )

inline

Sets the training result for classification data. This will place the training mode into CLASSIFICATION_MODE.

Parameters

trainingIteration	the current training iteration (or epoch)
accuracy	the accuracy for the current training iteration
trainer	a pointer to the class used to generate the result

Returns

returns true if the training result was set successfully

Definition at line 135 of file TrainingResult.h.

bool GRT::TrainingResult::setRegressionResult ( unsigned int  trainingIteration, Float  totalSquaredTrainingError, Float  rootMeanSquaredTrainingError, MLBase *  trainer  )

inline

Sets the training result for regression data. This will place the training mode into REGRESSION_MODE.

Parameters

trainingIteration	the current training iteration (or epoch)
totalSquaredTrainingError	the total squared training error for the current iteration
rootMeanSquaredTrainingError	the root mean squared training error for the current iteration
trainer	a pointer to the class used to generate the result

Returns

returns true if the training result was set successfully

Definition at line 152 of file TrainingResult.h.

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The documentation for this class was generated from the following file:

• TrainingResult.h