#include <LinearRegression.h>

Inheritance diagram for LinearRegression:

Public Member Functions
	LinearRegression (bool useScaling=false)

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	saveModelToFile (std::fstream &file) const

virtual bool	loadModelFromFile (std::fstream &file)

UINT	getMaxNumIterations () const

bool	setMaxNumIterations (const UINT maxNumIterations)

Public Member Functions inherited from Regressifier
	Regressifier (void)

virtual	~Regressifier (void)

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 (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)

virtual bool	saveModelToFile (std::string filename) const

virtual bool	loadModelFromFile (std::string filename)

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)

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

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

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

Static Protected Attributes
static RegisterRegressifierModule< LinearRegression >	registerModule

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

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

Static Public Member Functions inherited from Regressifier
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 ()

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

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 40 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 28 of file LinearRegression.cpp.

LinearRegression::~LinearRegression ( void )

virtual

Default Destructor

Definition at line 42 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 57 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 316 of file LinearRegression.cpp.

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

protected

Read the ranges if needed

Definition at line 320 of file LinearRegression.cpp.

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

virtual

This loads a trained Logistic Regression model from a file. This overrides the loadModelFromFile 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 259 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 46 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 196 of file LinearRegression.cpp.

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

virtual

This saves the trained Logistic Regression model to a file. This overrides the saveModelToFile 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 230 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 312 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 74 of file LinearRegression.cpp.

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

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation