Inheritance diagram for LinearRegression:

Public Member Functions
	LinearRegression (bool useScaling=false)

	LinearRegression (const LinearRegression &rhs)

virtual	~LinearRegression (void)

LinearRegression &	operator= (const LinearRegression &rhs)

virtual bool	deepCopyFrom (const Regressifier *regressifier)

virtual bool	train_ (RegressionData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

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

virtual bool	load (std::fstream &file)

UINT	getMaxNumIterations () const

bool	setMaxNumIterations (const UINT maxNumIterations)

Public Member Functions inherited from Regressifier
	Regressifier (const std::string &id="")

virtual	~Regressifier (void)

bool	copyBaseVariables (const Regressifier *regressifier)

virtual bool	reset () override

virtual bool	clear () override

VectorFloat	getRegressionData () const

Vector< MinMax >	getInputRanges () const

Vector< MinMax >	getOutputRanges () const

Regressifier *	deepCopy () const

const Regressifier &	getBaseRegressifier () const

Regressifier *	create () const

	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create() instead.", Regressifier *createNewInstance() const )

	GRT_DEPRECATED_MSG ("createInstanceFromString(id) is deprecated, use create(id) instead.", static Regressifier *createInstanceFromString(const std::string &id))

	GRT_DEPRECATED_MSG ("getRegressifierType is deprecated, use getId() instead", std::string getRegressifierType() const )

Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)

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, RegressionData validationData)

virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)

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 (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)

	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )

	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))

	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))

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

virtual std::string	getModelAsString () const

DataType	getInputType () const

DataType	getOutputType () const

BaseType	getType () const

UINT	getNumInputFeatures () const

UINT	getNumInputDimensions () const

UINT	getNumOutputDimensions () const

UINT	getMinNumEpochs () const

UINT	getMaxNumEpochs () const

UINT	getBatchSize () const

UINT	getNumRestarts () const

UINT	getValidationSetSize () const

UINT	getNumTrainingIterationsToConverge () const

Float	getMinChange () const

Float	getLearningRate () const

Float	getRMSTrainingError () const

	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )

Float	getTotalSquaredTrainingError () const

Float	getRMSValidationError () const

Float	getValidationSetAccuracy () const

VectorFloat	getValidationSetPrecision () const

VectorFloat	getValidationSetRecall () const

bool	getUseValidationSet () const

bool	getRandomiseTrainingOrder () const

bool	getTrained () const

	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )

bool	getConverged () const

bool	getScalingEnabled () const

bool	getIsBaseTypeClassifier () const

bool	getIsBaseTypeRegressifier () const

bool	getIsBaseTypeClusterer () const

bool	getTrainingLoggingEnabled () const

bool	getTestingLoggingEnabled () const

bool	enableScaling (const bool useScaling)

bool	setMaxNumEpochs (const UINT maxNumEpochs)

bool	setBatchSize (const UINT batchSize)

bool	setMinNumEpochs (const UINT minNumEpochs)

bool	setNumRestarts (const UINT numRestarts)

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	setTestingLoggingEnabled (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 (const std::string &id="")

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )

std::string	getId () 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)

bool	setDebugLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

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

Float	SQR (const Float &x) 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 std::string	getId ()

Static Public Member Functions inherited from Regressifier
static Vector< std::string >	getRegisteredRegressifiers ()

static Regressifier *	create (const std::string &id)

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

static std::string	getGRTRevison ()

Protected Member Functions
bool	loadLegacyModelFromFile (std::fstream &file)

Protected Member Functions inherited from Regressifier
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 Attributes
Float	w0

VectorFloat	w

Protected Attributes inherited from Regressifier
std::string	regressifierType

VectorFloat	regressionData

Vector< MinMax >	inputVectorRanges

Vector< MinMax >	targetVectorRanges

Protected Attributes inherited from MLBase
bool	trained

bool	useScaling

bool	converged

DataType	inputType

DataType	outputType

BaseType	baseType

UINT	numInputDimensions

UINT	numOutputDimensions

UINT	numTrainingIterationsToConverge

UINT	minNumEpochs

UINT	maxNumEpochs

UINT	batchSize

UINT	validationSetSize

UINT	numRestarts

Float	learningRate

Float	minChange

Float	rmsTrainingError

Float	rmsValidationError

Float	totalSquaredTrainingError

Float	validationSetAccuracy

bool	useValidationSet

bool	randomiseTrainingOrder

VectorFloat	validationSetPrecision

VectorFloat	validationSetRecall

Random	random

Vector< TrainingResult >	trainingResults

TrainingResultsObserverManager	trainingResultsObserverManager

TestResultsObserverManager	testResultsObserverManager

TrainingLog	trainingLog

TestingLog	testingLog

Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

WarningLog	warningLog

Additional Inherited Members
Public Types inherited from Regressifier
typedef std::map< std::string, Regressifier ()() >	StringRegressifierMap

Public Types inherited from MLBase
enum	BaseType { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER, PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }

Static Protected Member Functions inherited from Regressifier
static StringRegressifierMap *	getMap ()

Detailed Description

Examples:: RegressionModulesExamples/LinearRegressionExample/LinearRegressionExample.cpp, and RegressionModulesExamples/MultidimensionalRegressionExample/MultidimensionalRegressionExample.cpp.

Definition at line 38 of file LinearRegression.h.

Constructor & Destructor Documentation

LinearRegression::LinearRegression ( bool useScaling = false )

Default Constructor

Parameters

useScaling sets if the training and real-time data should be scaled between [0 1]. Default value = false

Definition at line 33 of file LinearRegression.cpp.

LinearRegression::LinearRegression ( const LinearRegression & rhs )

Copy Constructor

Parameters

rhs	copies the settings and model (if trained) from the rhs instance to this instance

Definition at line 41 of file LinearRegression.cpp.

LinearRegression::~LinearRegression ( void )

virtual

Default Destructor

Definition at line 46 of file LinearRegression.cpp.

Member Function Documentation

bool LinearRegression::deepCopyFrom ( const Regressifier * regressifier )

virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setRegressifier(...) method is called. It clones the data from the Base Class Regressifier pointer (which should be pointing to an Logistic Regression instance) into this instance

Parameters

regressifier a pointer to the Regressifier Base Class, this should be pointing to another Logistic Regression instance

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

Reimplemented from Regressifier.

Definition at line 61 of file LinearRegression.cpp.

std::string LinearRegression::getId ( )

static

Gets a string that represents the LinearRegression class.

Returns: returns a string containing the ID of this class

Definition at line 28 of file LinearRegression.cpp.

UINT LinearRegression::getMaxNumIterations ( ) const

Gets the current maxNumIterations value, this is the maximum number of iterations that can be run during the training phase.

Returns: returns the maxNumIterations value

Definition at line 320 of file LinearRegression.cpp.

bool LinearRegression::load ( std::fstream & file )

virtual

This loads a trained Logistic Regression model from a file. This overrides the load function in the Logistic Regression base class.

Parameters

file	a reference to the file the Logistic Regression model will be loaded from

Returns: returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 263 of file LinearRegression.cpp.

bool LinearRegression::loadLegacyModelFromFile ( std::fstream & file )

protected

Read the ranges if needed

Definition at line 324 of file LinearRegression.cpp.

LinearRegression & LinearRegression::operator= ( const LinearRegression & rhs )

Defines how the data from the rhs LinearRegression should be copied to this LinearRegression

Parameters

rhs	another instance of a LinearRegression

Returns: returns a pointer to this instance of the LinearRegression

Definition at line 50 of file LinearRegression.cpp.

bool LinearRegression::predict_ ( VectorFloat & inputVector )

virtual

This performs the regression by mapping the inputVector using the current Logistic Regression model. This overrides the predict function in the Regressifier base class.

Parameters

inputVector the input vector to classify

Returns: returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 200 of file LinearRegression.cpp.

bool LinearRegression::save ( std::fstream & file ) const

virtual

This saves the trained Logistic Regression model to a file. This overrides the save function in the ML base class.

Parameters

file	a reference to the file the Logistic Regression model will be saved to

Returns: returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 234 of file LinearRegression.cpp.

bool LinearRegression::setMaxNumIterations ( const UINT maxNumIterations )

Sets the maximum number of iterations that can be run during the training phase. The maxNumIterations value must be greater than zero.

Parameters

maxNumIterations the maximum number of iterations value, must be greater than zero

Returns: returns true if the value was updated successfully, false otherwise

Definition at line 316 of file LinearRegression.cpp.

bool LinearRegression::train_ ( RegressionData & trainingData )

virtual

This trains the Logistic Regression model, using the labelled regression data. This overrides the train function in the Regression base class.

Parameters

trainingData the training data that will be used to train the regression model

Returns: returns true if the LRC model was trained, false otherwise

Reimplemented from MLBase.

Definition at line 78 of file LinearRegression.cpp.

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation