Inheritance diagram for Regressifier:

Public Types
typedef std::map< std::string, Regressifier ()() >	StringRegressifierMap

Public Types inherited from MLBase
enum	BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }

Public Member Functions
	Regressifier (void)

virtual	~Regressifier (void)

virtual bool	deepCopyFrom (const Regressifier *regressifier)

bool	copyBaseVariables (const Regressifier *regressifier)

virtual bool	reset ()

virtual bool	clear ()

std::string	getRegressifierType () const

VectorFloat	getRegressionData () const

Vector< MinMax >	getInputRanges () const

Vector< MinMax >	getOutputRanges () const

Regressifier *	createNewInstance () const

Regressifier *	deepCopy () const

const Regressifier &	getBaseRegressifier () const

Public Member Functions inherited from MLBase
	MLBase (void)

virtual	~MLBase (void)

bool	copyMLBaseVariables (const MLBase *mlBase)

virtual bool	train (ClassificationData trainingData)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	train (RegressionData trainingData)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	train (TimeSeriesClassificationData trainingData)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	train (ClassificationDataStream trainingData)

virtual bool	train_ (ClassificationDataStream &trainingData)

virtual bool	train (UnlabelledData trainingData)

virtual bool	train_ (UnlabelledData &trainingData)

virtual bool	train (MatrixFloat data)

virtual bool	train_ (MatrixFloat &data)

virtual bool	predict (VectorFloat inputVector)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	predict (MatrixFloat inputMatrix)

virtual bool	predict_ (MatrixFloat &inputMatrix)

virtual bool	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

virtual bool	print () const

virtual bool	save (const std::string filename) const

virtual bool	load (const std::string filename)

virtual bool	saveModelToFile (std::string filename) const

virtual bool	saveModelToFile (std::fstream &file) const

virtual bool	loadModelFromFile (std::string filename)

virtual bool	loadModelFromFile (std::fstream &file)

virtual bool	getModel (std::ostream &stream) const

Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

UINT	getBaseType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRootMeanSquaredTrainingError () const

Float	getTotalSquaredTrainingError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

bool	getModelTrained () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setMinChange (const Float minChange)

bool	setLearningRate (const Float learningRate)

bool	setUseValidationSet (const bool useValidationSet)

bool	setValidationSetSize (const UINT validationSetSize)

bool	setRandomiseTrainingOrder (const bool randomiseTrainingOrder)

bool	setTrainingLoggingEnabled (const bool loggingEnabled)

bool	registerTrainingResultsObserver (Observer< TrainingResult > &observer)

bool	registerTestResultsObserver (Observer< TestInstanceResult > &observer)

bool	removeTrainingResultsObserver (const Observer< TrainingResult > &observer)

bool	removeTestResultsObserver (const Observer< TestInstanceResult > &observer)

bool	removeAllTrainingObservers ()

bool	removeAllTestObservers ()

bool	notifyTrainingResultsObservers (const TrainingResult &data)

bool	notifyTestResultsObservers (const TestInstanceResult &data)

MLBase *	getMLBasePointer ()

const MLBase *	getMLBasePointer () const

Vector< TrainingResult >	getTrainingResults () const

Public Member Functions inherited from GRTBase
	GRTBase (void)

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

std::string	getClassType () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Public Member Functions inherited from Observer< TrainingResult >
virtual void	notify (const TrainingResult &data)

Public Member Functions inherited from Observer< TestInstanceResult >
virtual void	notify (const TestInstanceResult &data)

Static Public Member Functions
static Regressifier *	createInstanceFromString (const std::string &regressifierType)

static Vector< std::string >	getRegisteredRegressifiers ()

Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Protected Member Functions
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Member Functions inherited from MLBase
bool	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

Protected Member Functions inherited from GRTBase
Float	SQR (const Float &x) const

Static Protected Member Functions
static StringRegressifierMap *	getMap ()

Protected Attributes
std::string	regressifierType

VectorFloat	regressionData

Vector< MinMax >	inputVectorRanges

Vector< MinMax >	targetVectorRanges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

DataType	inputType

DataType	outputType

UINT	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	validationSetSize

Float	learningRate

Float	minChange

Float	rootMeanSquaredTrainingError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

std::vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Detailed Description

Definition at line 43 of file Regressifier.h.

Member Typedef Documentation

typedef std::map< std::string, Regressifier*(*)() > Regressifier::StringRegressifierMap

Defines a map between a string (which will contain the name of the regressifier, such as LinearRegression) and a function returns a new instance of that regressifier

Definition at line 119 of file Regressifier.h.

Constructor & Destructor Documentation

Regressifier::Regressifier ( void )

Default Regressifier Destructor

Definition at line 54 of file Regressifier.cpp.

Regressifier::~Regressifier ( void )

virtual

Default Regressifier Destructor

Definition at line 61 of file Regressifier.cpp.

Member Function Documentation

bool Regressifier::clear ( )

virtual

This function clears the regressifier module, removing any trained model and setting all the base variables to their default values.

Returns: returns true if the module was cleared succesfully, false otherwise

Reimplemented from MLBase.

Reimplemented in RegressionTree, and MLP.

Definition at line 97 of file Regressifier.cpp.

bool Regressifier::copyBaseVariables ( const Regressifier * regressifier )

This copies the Regressifier variables from the regressifier pointer to this instance.

Parameters

regressifier a pointer to a regressifier from which the values will be copied to this instance

Returns: returns true if the copy was successfull, false otherwise

Definition at line 68 of file Regressifier.cpp.

Regressifier * Regressifier::createInstanceFromString ( const std::string & regressifierType )

static

Creates a new regressifier instance based on the input string (which should contain the name of a valid regressifier such as LinearRegression).

Parameters

regressifierType the name of the regressifier

Returns: Regressifier*: a pointer to the new instance of the regressifier

Definition at line 28 of file Regressifier.cpp.

Regressifier * Regressifier::createNewInstance ( ) const

Creates a new regressifier instance based on the current regressifierType string value.

Returns: Regressifier*: a pointer to the new instance of the regressifier

Definition at line 37 of file Regressifier.cpp.

Regressifier * Regressifier::deepCopy ( ) const

This creates a new Regressifier instance and deep copies the variables and models from this instance into the deep copy. The function will then return a pointer to the new instance. It is up to the user who calls this function to delete the dynamic instance when they are finished using it.

Returns: returns a pointer to a new Regressifier instance which is a deep copy of this instance

Definition at line 41 of file Regressifier.cpp.

virtual bool Regressifier::deepCopyFrom ( const Regressifier * regressifier )

inlinevirtual

This is the base deep copy function for the Regressifier modules. This function should be overwritten by the derived class. This deep copies the variables and models from the regressifier pointer to this regressifier instance.

Parameters

regressifier a pointer to the Regressifier base class, this should be pointing to another instance of a matching derived class

Returns: returns true if the deep copy was successfull, false otherwise (the Regressifier base class will always return false)

Reimplemented in RegressionTree, MultidimensionalRegression, MLP, LinearRegression, and LogisticRegression.

Definition at line 63 of file Regressifier.h.

const Regressifier & Regressifier::getBaseRegressifier ( ) const

Returns a pointer to this regressifier. This is useful for a derived class so it can get easy access to this base regressifier.

Returns: Regressifier&: a reference to this regressifier

Definition at line 131 of file Regressifier.cpp.

Vector< MinMax > Regressifier::getInputRanges ( ) const

Returns the ranges of the input (i.e. feature) data.

Returns: returns a Vector of MinMax values representing the ranges of the input data

Definition at line 123 of file Regressifier.cpp.

Vector< MinMax > Regressifier::getOutputRanges ( ) const

Returns the ranges of the output (i.e. target) data.

Returns: returns a Vector of MinMax values representing the ranges of the target data

Definition at line 127 of file Regressifier.cpp.

static Vector< std::string > Regressifier::getRegisteredRegressifiers ( )

static

Returns a Vector of the names of all regressifiers that have been registered with the base regressifier.

Returns: Vector< string >: a Vector containing the names of the regressifiers that have been registered with the base regressifier

std::string Regressifier::getRegressifierType ( ) const

Gets the regressifier type as a string. This is the name of the regression algorithm, such as "LinearRegression".

Returns: returns the regressifier type as a string

Definition at line 112 of file Regressifier.cpp.

VectorFloat Regressifier::getRegressionData ( ) const

Gets a Vector containing the regression data output by the regression algorithm, this will be an M-dimensional Vector, where M is the number of output dimensions in the model.

Returns: returns a Vector containing the regression data output by the regression algorithm, an empty Vector will be returned if the model has not been trained

Definition at line 116 of file Regressifier.cpp.

bool Regressifier::loadBaseSettingsFromFile ( std::fstream & file )

protected

Loads the core base settings from a file.

Returns: returns true if the base settings were loaded, false otherwise

Definition at line 162 of file Regressifier.cpp.

bool Regressifier::reset ( )

virtual

This resets the regressifier. This overrides the reset function in the MLBase base class.

Returns: returns true if the regressifier was reset, false otherwise

Reimplemented from MLBase.

Definition at line 87 of file Regressifier.cpp.

bool Regressifier::saveBaseSettingsToFile ( std::fstream & file ) const

protected

Saves the core base settings to a file.

Returns: returns true if the base settings were saved, false otherwise

Definition at line 136 of file Regressifier.cpp.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation