#include <Softmax.h>

Inheritance diagram for Softmax:

Public Member Functions
	Softmax (const bool useScaling=false, const Float learningRate=0.1, const Float minChange=1.0e-10, const UINT maxNumEpochs=1000)

	Softmax (const Softmax &rhs)

virtual	~Softmax (void)

Softmax &	operator= (const Softmax &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train_ (ClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	clear ()

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

virtual bool	loadModelFromFile (std::fstream &file)

Vector< SoftmaxModel >	getModels () const

Public Member Functions inherited from Classifier
	Classifier (void)

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	reset ()

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (UINT classLabel) const

UINT	getPredictedClassLabel () const

VectorFloat	getClassLikelihoods () const

VectorFloat	getClassDistances () const

VectorFloat	getNullRejectionThresholds () const

Vector< UINT >	getClassLabels () const

Vector< MinMax >	getRanges () const

bool	enableNullRejection (bool useNullRejection)

virtual bool	setNullRejectionCoeff (Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (VectorFloat newRejectionThresholds)

virtual bool	recomputeNullRejectionThresholds ()

bool	getTimeseriesCompatible () const

Classifier *	createNewInstance () const

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () 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 (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 (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	trainSoftmaxModel (UINT classLabel, SoftmaxModel &model, ClassificationData &data)

bool	loadLegacyModelFromFile (std::fstream &file)

Protected Member Functions inherited from Classifier
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
Vector< SoftmaxModel >	models

Protected Attributes inherited from Classifier
std::string	classifierType

bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

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 RegisterClassifierModule< Softmax >	registerModule

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

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

Static Public Member Functions inherited from Classifier
static Classifier *	createInstanceFromString (std::string const &classifierType)

static Vector< std::string >	getRegisteredClassifiers ()

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

static std::string	getGRTRevison ()

Protected Types inherited from Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

Static Protected Member Functions inherited from Classifier
static StringClassifierMap *	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 41 of file Softmax.h.

Constructor & Destructor Documentation

Softmax::Softmax	(	const bool	useScaling = `false`,
		const Float	learningRate = `0.1`,
		const Float	minChange = `1.0e-10`,
		const UINT	maxNumEpochs = `1000`
	)

Default Constructor

Parameters

useScaling	sets if the training and real-time data should be scaled between [0 1]. Default value = false
learningRate	the learningRate value used during the training phase. Default = 0.1
minChange	sets the minimum change that must be achieved between two training epochs for the training to continue. Default = 1.0e-10
maxNumEpochs	sets the maximum number of iterations that can be run during the training phase. Default = 1000

Definition at line 28 of file Softmax.cpp.

Softmax::Softmax ( const Softmax & rhs )

Defines the copy constructor.

Parameters

rhs	the instance from which all the data will be copied into this instance

Definition at line 43 of file Softmax.cpp.

Softmax::~Softmax ( void )

virtual

Default Destructor

Definition at line 54 of file Softmax.cpp.

Member Function Documentation

bool Softmax::clear ( )

virtual

This overrides the clear function in the Classifier base class. It will completely clear the ML 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 Classifier.

Definition at line 262 of file Softmax.cpp.

bool Softmax::deepCopyFrom ( const Classifier * classifier )

virtual

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

Parameters

classifier a pointer to the Classifier Base Class, this should be pointing to another Softmax instance

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

Reimplemented from Classifier.

Definition at line 71 of file Softmax.cpp.

Vector< SoftmaxModel > Softmax::getModels ( ) const

Get the softmax models for each class. The Softmax class must be trained first.

Returns: returns a vector of softmax models, with each element representing the model for a specific class

Definition at line 388 of file Softmax.cpp.

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

protected

Read the ranges if needed

Definition at line 392 of file Softmax.cpp.

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

virtual

This loads a trained Softmax model from a file. This overrides the loadModelFromFile function in the Classifier base class.

Parameters

file	a reference to the file the Softmax model will be loaded from

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

Reimplemented from MLBase.

Definition at line 305 of file Softmax.cpp.

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

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

Parameters

rhs	another instance of a Softmax

Returns: returns a pointer to this instance of the Softmax

Definition at line 58 of file Softmax.cpp.

bool Softmax::predict_ ( VectorFloat & inputVector )

virtual

This predicts the class of the inputVector. This overrides the predict function in the Classifier 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 133 of file Softmax.cpp.

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

virtual

This saves the trained Softmax model to a file. This overrides the saveModelToFile function in the Classifier base class.

Parameters

file	a reference to the file the Softmax model will be saved to

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

Reimplemented from MLBase.

Definition at line 273 of file Softmax.cpp.

bool Softmax::train_ ( ClassificationData & trainingData )

virtual

This trains the Softmax model, using the labelled classification data. This overrides the train function in the Classifier base class.

Parameters

trainingData a reference to the training data

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

Reimplemented from MLBase.

Definition at line 89 of file Softmax.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