Inheritance diagram for MLBase:

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

Public Member Functions
	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	reset ()

virtual bool	clear ()

virtual bool	print () const

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

virtual bool	load (const std::string filename)

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

virtual bool	load (std::fstream &file)

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

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	saveBaseSettingsToFile (std::fstream &file) const

bool	loadBaseSettingsFromFile (std::fstream &file)

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

Protected Attributes
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

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

static std::string	getGRTRevison ()

Detailed Description

Definition at line 70 of file MLBase.h.

Constructor & Destructor Documentation

GRT_BEGIN_NAMESPACE MLBase::MLBase ( void )

Default MLBase Constructor

Definition at line 26 of file MLBase.cpp.

MLBase::~MLBase ( void )

virtual

Default MLBase Destructor

Definition at line 46 of file MLBase.cpp.

Member Function Documentation

bool MLBase::clear ( )

virtual

This is the main clear interface for all the GRT machine learning algorithms. 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 derived class was cleared succesfully, false otherwise

Reimplemented in SelfOrganizingMap, HierarchicalClustering, DTW, HMM, FFT, FiniteStateMachine, AdaBoost, SVM, ANBC, KNN, RandomForests, DecisionTree, BAG, GMM, RBMQuantizer, ParticleClassifier, KMeansQuantizer, SOMQuantizer, FIRFilter, ClusterTree, RegressionTree, MinDist, Softmax, MLP, SwipeDetector, Clusterer, BernoulliRBM, FeatureExtraction, KMeans, PreProcessing, GaussianMixtureModels, Classifier, Regressifier, ContinuousHiddenMarkovModel, and MovementDetector.

Definition at line 127 of file MLBase.cpp.

bool MLBase::copyMLBaseVariables ( const MLBase * mlBase )

This copies all the MLBase variables from the instance mlBaseA to the instance mlBaseA.

Parameters

mlBase a pointer to a MLBase class from which the values will be copied to the instance that calls the function

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

Definition at line 50 of file MLBase.cpp.

bool MLBase::enableScaling ( const bool useScaling )

Sets if scaling should be used during the training and prediction phases.

Returns: returns true the scaling parameter was updated, false otherwise

Definition at line 271 of file MLBase.cpp.

UINT MLBase::getBaseType ( ) const

Gets the current ML base type.

Returns: returns an UINT representing the current ML base type, this will be one of the BaseTypes enumerations

Definition at line 208 of file MLBase.cpp.

DataType MLBase::getInputType ( ) const

Gets the expected input data type for the module

Returns: returns the expected input data type

Definition at line 200 of file MLBase.cpp.

bool MLBase::getIsBaseTypeClassifier ( ) const

Gets if the derived class type is CLASSIFIER.

Returns: returns true if the derived class type is CLASSIFIER, false otherwise

Definition at line 265 of file MLBase.cpp.

bool MLBase::getIsBaseTypeClusterer ( ) const

Gets if the derived class type is CLUSTERER.

Returns: returns true if the derived class type is CLUSTERER, false otherwise

Definition at line 269 of file MLBase.cpp.

bool MLBase::getIsBaseTypeRegressifier ( ) const

Gets if the derived class type is REGRESSIFIER.

Returns: returns true if the derived class type is REGRESSIFIER, false otherwise

Definition at line 267 of file MLBase.cpp.

Float MLBase::getLearningRate ( ) const

Gets the current learningRate value, this is value used to update the weights at each step of a learning algorithm such as stochastic gradient descent.

Returns: returns the current learningRate value

Definition at line 235 of file MLBase.cpp.

UINT MLBase::getMaxNumEpochs ( ) const

Gets the maximum number of epochs. This value controls the maximum number of epochs that can be used by the training algorithm. An epoch is a complete iteration of all training samples.

Returns: returns the maximum number of epochs

Definition at line 227 of file MLBase.cpp.

Float MLBase::getMinChange ( ) const

Gets the minimum change value that controls when the training algorithm should stop.

Returns: returns the minimum change value

UINT MLBase::getMinNumEpochs ( ) const

Gets the minimum number of epochs. This is the minimum number of epochs that can elapse with no change between two training epochs. An epoch is a complete iteration of all training samples.

Returns: returns the minimum number of epochs

Definition at line 223 of file MLBase.cpp.

MLBase * MLBase::getMLBasePointer ( )

This functions returns a pointer to the current instance.

Returns: returns a MLBase pointer to the current instance.

Definition at line 363 of file MLBase.cpp.

const MLBase * MLBase::getMLBasePointer ( ) const

This functions returns a const pointer to the current instance.

Returns: returns a const MLBase pointer to the current instance.

Definition at line 367 of file MLBase.cpp.

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

virtual

This function adds the current model to the formatted stream. This function should be overwritten by the derived class.

Parameters

file	a reference to the stream the model will be added to

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

Reimplemented in DecisionTree.

Definition at line 190 of file MLBase.cpp.

std::string MLBase::getModelAsString ( ) const

virtual

Gets the current model and settings as a std::string.

Returns: returns a std::string containing the model

Definition at line 192 of file MLBase.cpp.

bool MLBase::getModelTrained ( ) const

This function is now depreciated. You should use the getTrained() function instead.

Returns: returns true if the model for the derived class has been succesfully trained, false otherwise

Definition at line 261 of file MLBase.cpp.

UINT MLBase::getNumInputDimensions ( ) const

Gets the number of input dimensions in trained model.

Returns: returns the number of input dimensions

Definition at line 212 of file MLBase.cpp.

UINT MLBase::getNumInputFeatures ( ) const

Gets the number of input dimensions in trained model. This function is now depriciated and will be removed in the future, you should use getNumInputDimensions instead.

Returns: returns the number of input dimensions in the trained model, a value of 0 will be returned if the model has not been trained

Definition at line 210 of file MLBase.cpp.

UINT MLBase::getNumOutputDimensions ( ) const

Gets the number of output dimensions in trained model.

Returns: returns the number of output dimensions

Definition at line 214 of file MLBase.cpp.

UINT MLBase::getNumTrainingIterationsToConverge ( ) const

Gets the number of training iterations that were required for the algorithm to converge.

Returns: returns the number of training iterations required for the training algorithm to converge, a value of 0 will be returned if the model has not been trained

Definition at line 216 of file MLBase.cpp.

DataType MLBase::getOutputType ( ) const

Gets the expected output data type for the module

Returns: returns the expected output data type

Definition at line 204 of file MLBase.cpp.

bool MLBase::getRandomiseTrainingOrder ( ) const

Returns true if the order of the training dataset should be randomized at each epoch of training. Randomizing the order of the training dataset stops a learning algorithm from focusing too much on the first few examples in the dataset.

Returns: returns true if the order of the training dataset should be randomized, false otherwise

Float MLBase::getRootMeanSquaredTrainingError ( ) const

Gets the root mean squared error on the training data during the training phase.

Returns: returns the RMS error (on the training data during the training phase)

Definition at line 239 of file MLBase.cpp.

bool MLBase::getScalingEnabled ( ) const

Gets if the scaling has been enabled.

Returns: returns true if scaling is enabled, false otherwise

Definition at line 263 of file MLBase.cpp.

Float MLBase::getTotalSquaredTrainingError ( ) const

Gets the total squared error on the training data during the training phase.

Returns: returns the total squared error (on the training data during the training phase)

Definition at line 243 of file MLBase.cpp.

bool MLBase::getTrained ( ) const

Gets if the model for the derived class has been succesfully trained.

Returns: returns true if the model for the derived class has been succesfully trained, false otherwise

Definition at line 259 of file MLBase.cpp.

Vector< TrainingResult > MLBase::getTrainingResults ( ) const

Gets the training results from the last training phase. Each element in the vector represents the training results from 1 training iteration.

Returns: returns a vector of TrainingResult instances containing the training results from the most recent training phase

Definition at line 371 of file MLBase.cpp.

bool MLBase::getUseValidationSet ( ) const

Returns true if a validation set should be used for training. If true, then the training dataset will be partitioned into a smaller training dataset and a validation set.

The size of the partition is controlled by the validationSetSize parameter, for example, if the validationSetSize parameter is 20 then 20% of the training data will be used for a validation set leaving 80% of the original data to train the model.

Returns: returns true if a validation set should be used for training, false otherwise

Float MLBase::getValidationSetAccuracy ( ) const

Gets the accuracy of the validation set on the trained model, only valid if the model was trained with useValidationSet=true.

Returns: returns the accuracy of validation set on the trained model

Definition at line 247 of file MLBase.cpp.

VectorFloat MLBase::getValidationSetPrecision ( ) const

Gets the precision of the validation set on the trained model, only valid if the model was trained with useValidationSet=true.

Returns: returns the precision of the validation set on the trained model

Definition at line 251 of file MLBase.cpp.

VectorFloat MLBase::getValidationSetRecall ( ) const

Gets the recall of the validation set on the trained model, only valid if the model was trained with useValidationSet=true.

Returns: returns the recall of the validation set on the trained model

Definition at line 255 of file MLBase.cpp.

UINT MLBase::getValidationSetSize ( ) const

Gets the size (as a percentage) of the validation set (if one should be used). If this value returned 20 this would mean that 20% of the training data would be set aside to create a validation set and the other 80% would be used to actually train the regression model. This will only happen if the useValidationSet parameter is set to true, otherwise 100% of the training data will be used to train the regression model.

Returns: returns the size of the validation set

Definition at line 231 of file MLBase.cpp.

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

Deprecated:

use save(std::string filename) instead

Parameters

the	name of the file to save the model to

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

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

Deprecated:

use save(std::fstream &file) instead

Parameters

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

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

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

Deprecated:

use load(std::string filename) instead

Parameters

filename the name of the file to load the model from

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

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

Deprecated:

use load(std::fstream &file) instead

Parameters

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

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

bool MLBase::load ( const std::string filename )

virtual

This saves the model to a file, it calls the loadModelFromFile(std::string filename) function unless it is overwritten by the derived class.

Parameters

filename the name of the file to save the model to

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

Definition at line 167 of file MLBase.cpp.

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

virtual

This loads a trained model from a file. This function should be overwritten by the derived class.

Parameters

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

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

Reimplemented in DTW, HMM, FiniteStateMachine, RandomForests, AdaBoost, DecisionTree, SVM, ANBC, KNN, RegressionTree, BAG, MLP, SwipeDetector, RBMQuantizer, GMM, FIRFilter, PrincipalComponentAnalysis, MinDist, Softmax, Derivative, LowPassFilter, BernoulliRBM, HighPassFilter, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, MultidimensionalRegression, WeightedAverageFilter, LeakyIntegrator, LinearRegression, LogisticRegression, ParticleClassifier, DiscreteHiddenMarkovModel, ContinuousHiddenMarkovModel, and MovementDetector.

Definition at line 182 of file MLBase.cpp.

bool MLBase::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 398 of file MLBase.cpp.

bool MLBase::map ( VectorFloat inputVector )

virtual

This is the main mapping interface for all the GRT machine learning algorithms. By defaut it will call the map_ function, unless it is overwritten by the derived class.

Parameters

inputVector the input vector for mapping/regression

Returns: returns true if the mapping was completed succesfully, false otherwise (the base class always returns false)

Definition at line 121 of file MLBase.cpp.

bool MLBase::map_ ( VectorFloat & inputVector )

virtual

This is the main mapping interface by reference for all the GRT machine learning algorithms. This should be overwritten by the derived class.

Parameters

inputVector a reference to the input vector for mapping/regression

Returns: returns true if the mapping was completed succesfully, false otherwise (the base class always returns false)

Reimplemented in SelfOrganizingMap.

Definition at line 123 of file MLBase.cpp.

bool MLBase::notifyTestResultsObservers ( const TestInstanceResult & data )

Notifies all observers that have subscribed to the test results observer manager.

Parameters

data	stores the test results data for the current update

Returns: returns true if all the observers were notified, false otherwise

Definition at line 359 of file MLBase.cpp.

bool MLBase::notifyTrainingResultsObservers ( const TrainingResult & data )

Notifies all observers that have subscribed to the training results observer manager.

Parameters

data	stores the training results data for the current update

Returns: returns true if all the observers were notified, false otherwise

Definition at line 355 of file MLBase.cpp.

bool MLBase::predict ( VectorFloat inputVector )

virtual

This is the main prediction interface for all the GRT machine learning algorithms. By defaut it will call the predict_ function, unless it is overwritten by the derived class.

Parameters

inputVector the new input vector for prediction

Returns: returns true if the prediction was completed succesfully, false otherwise (the base class always returns false)

Reimplemented in LDA.

Definition at line 113 of file MLBase.cpp.

bool MLBase::predict ( MatrixFloat inputMatrix )

virtual

This is the prediction interface for time series data. By defaut it will call the predict_ function, unless it is overwritten by the derived class.

Parameters

inputMatrix the new input matrix for prediction

Returns: returns true if the prediction was completed succesfully, false otherwise (the base class always returns false)

Definition at line 117 of file MLBase.cpp.

bool MLBase::predict_ ( VectorFloat & inputVector )

virtual

This is the main prediction interface for all the GRT machine learning algorithms. This should be overwritten by the derived class.

Parameters

inputVector a reference to the input vector for prediction

Returns: returns true if the prediction was completed succesfully, false otherwise (the base class always returns false)

Reimplemented in DTW, KMeans, SVM, AdaBoost, HMM, GaussianMixtureModels, KNN, RandomForests, FiniteStateMachine, DecisionTree, ANBC, GMM, ClusterTree, RegressionTree, MLP, BAG, MinDist, Softmax, SwipeDetector, MultidimensionalRegression, LinearRegression, LogisticRegression, ParticleClassifier, BernoulliRBM, ContinuousHiddenMarkovModel, and MovementDetector.

Definition at line 115 of file MLBase.cpp.

bool MLBase::predict_ ( MatrixFloat & inputMatrix )

virtual

This is the prediction interface for time series data. This should be overwritten by the derived class.

Parameters

inputMatrix a reference to the new input matrix for prediction

Returns: returns true if the prediction was completed succesfully, false otherwise (the base class always returns false)

Reimplemented in DTW, HMM, and ContinuousHiddenMarkovModel.

Definition at line 119 of file MLBase.cpp.

bool MLBase::print ( ) const

virtual

This is the main print interface for all the GRT machine learning algorithms. This should be overwritten by the derived class. It will print the model and settings to the display log.

Returns: returns true if the model was printed succesfully, false otherwise (the base class always returns true)

Reimplemented in HMM, FiniteStateMachine, RandomForests, BernoulliRBM, ClusterTree, RegressionTree, MLP, DiscreteHiddenMarkovModel, and ContinuousHiddenMarkovModel.

Definition at line 141 of file MLBase.cpp.

bool MLBase::registerTestResultsObserver ( Observer< TestInstanceResult > & observer )

Registers the observer with the test result observer manager. The observer will then be notified when any new test result is computed.

Parameters

observer the observer you want to register with the learning algorithm

Returns: returns true the observer was added, false otherwise

Definition at line 335 of file MLBase.cpp.

bool MLBase::registerTrainingResultsObserver ( Observer< TrainingResult > & observer )

Registers the observer with the training result observer manager. The observer will then be notified when any new training result is computed.

Parameters

observer the observer you want to register with the learning algorithm

Returns: returns true the observer was added, false otherwise

Definition at line 331 of file MLBase.cpp.

bool MLBase::removeAllTestObservers ( )

Removes all observers from the training result observer manager.

Returns: returns true if all the observers were removed, false otherwise

Definition at line 351 of file MLBase.cpp.

bool MLBase::removeAllTrainingObservers ( )

Removes all observers from the training result observer manager.

Returns: returns true if all the observers were removed, false otherwise

Definition at line 347 of file MLBase.cpp.

bool MLBase::removeTestResultsObserver ( const Observer< TestInstanceResult > & observer )

Removes the observer from the test result observer manager.

Parameters

observer the observer you want to remove from the learning algorithm

Returns: returns true if the observer was removed, false otherwise

Definition at line 343 of file MLBase.cpp.

bool MLBase::removeTrainingResultsObserver ( const Observer< TrainingResult > & observer )

Removes the observer from the training result observer manager.

Parameters

observer the observer you want to remove from the learning algorithm

Returns: returns true if the observer was removed, false otherwise

Definition at line 339 of file MLBase.cpp.

bool MLBase::reset ( )

virtual

This is the main reset interface for all the GRT machine learning algorithms. It should be used to reset the model (i.e. set all values back to default settings). If you want to completely clear the model (i.e. clear any learned weights or values) then you should use the clear function.

Returns: returns true if the derived class was reset succesfully, false otherwise (the base class always returns true)

Reimplemented in SelfOrganizingMap, HierarchicalClustering, DTW, ClassLabelTimeoutFilter, FFT, HMM, FiniteStateMachine, ParticleClassifier, ANBC, SwipeDetector, ZeroCrossingCounter, BAG, ClassLabelFilter, RBMQuantizer, KMeansQuantizer, SOMQuantizer, TimeDomainFeatures, Derivative, FIRFilter, LowPassFilter, MovementTrajectoryFeatures, HighPassFilter, FFTFeatures, SavitzkyGolayFilter, Clusterer, KMeansFeatures, MovementIndex, EnvelopeExtractor, DeadZone, BernoulliRBM, ClassLabelChangeFilter, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, WeightedAverageFilter, TimeseriesBuffer, LeakyIntegrator, FeatureExtraction, Context, KMeans, PostProcessing, GaussianMixtureModels, PreProcessing, Classifier, Regressifier, DiscreteHiddenMarkovModel, ContinuousHiddenMarkovModel, MovementDetector, and Gate.

Definition at line 125 of file MLBase.cpp.

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

virtual

This saves the model to a file, it calls the saveModelToFile(std::string filename) function unless it is overwritten by the derived class.

Parameters

filename the name of the file to save the model to

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

Definition at line 143 of file MLBase.cpp.

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

virtual

This saves the trained model to a file. This function should be overwritten by the derived class.

Parameters

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

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

Reimplemented in DTW, HMM, FiniteStateMachine, RandomForests, AdaBoost, DecisionTree, SVM, ANBC, KNN, RegressionTree, BAG, MLP, SwipeDetector, RBMQuantizer, GMM, PrincipalComponentAnalysis, FIRFilter, MinDist, Softmax, BernoulliRBM, Derivative, LowPassFilter, HighPassFilter, SavitzkyGolayFilter, DeadZone, DoubleMovingAverageFilter, MedianFilter, MovingAverageFilter, MultidimensionalRegression, WeightedAverageFilter, LeakyIntegrator, LinearRegression, LogisticRegression, ParticleClassifier, DiscreteHiddenMarkovModel, ContinuousHiddenMarkovModel, and MovementDetector.

Definition at line 159 of file MLBase.cpp.

bool MLBase::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 375 of file MLBase.cpp.

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

inline

Scales the input value x (which should be in the range [minSource maxSource]) to a value in the new target range of [minTarget maxTarget].

Parameters

x	the value that should be scaled
minSource	the minimum range that x originates from
maxSource	the maximum range that x originates from
minTarget	the minimum range that x should be scaled to
maxTarget	the maximum range that x should be scaled to
constrain	sets if the scaled value should be constrained to the target range

Returns: returns a new value that has been scaled based on the input parameters

Definition at line 353 of file MLBase.h.

bool MLBase::setLearningRate ( const Float learningRate )

Sets the learningRate. This is used to update the weights at each step of learning algorithms such as stochastic gradient descent. The learningRate value must be greater than zero.

Parameters

learningRate the learningRate value used during the training phase, must be greater than zero

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

Definition at line 296 of file MLBase.cpp.

bool MLBase::setMaxNumEpochs ( const UINT maxNumEpochs )

Sets the maximum number of epochs (a complete iteration of all training samples) 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 273 of file MLBase.cpp.

bool MLBase::setMinChange ( const Float minChange )

Sets the minimum change that must be achieved between two training epochs for the training to continue. The minChange value must be greater than zero.

Parameters

minChange the minimum change value, must be greater than zero

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

Definition at line 287 of file MLBase.cpp.

bool MLBase::setMinNumEpochs ( const UINT minNumEpochs )

Sets the minimum number of epochs (a complete iteration of all training samples) that can elapse with no change between two training epochs.

Parameters

minNumEpochs the minimum number of epochs that can elapse with no change between two training epochs

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

Definition at line 282 of file MLBase.cpp.

bool MLBase::setRandomiseTrainingOrder ( const bool randomiseTrainingOrder )

Sets if the order of the training dataset should be randomized at each epoch of training. Randomizing the order of the training dataset stops a learning algorithm from focusing too much on the first few examples in the dataset.

Parameters

randomiseTrainingOrder if true then the order in which training samples are supplied to a learning algorithm will be randomised

Returns: returns true if the parameter was updated, false otherwise

Definition at line 321 of file MLBase.cpp.

bool MLBase::setTrainingLoggingEnabled ( const bool loggingEnabled )

Sets if training logging is enabled/disabled for this specific ML instance. If you want to enable/disable training logging globally, then you should use the TrainingLog::enableLogging( bool ) function.

Parameters

loggingEnabled if true then training logging will be enabled, if false then training logging will be disabled

Returns: returns true if the parameter was updated, false otherwise

Definition at line 326 of file MLBase.cpp.

bool MLBase::setUseValidationSet ( const bool useValidationSet )

Sets the size of the validation set used by some learning algorithms for training. This value represents the percentage of the main dataset that will be used for training. For example, if the validationSetSize parameter is 20 then 20% of the training data will be used for a validation set leaving 80% of the original data to train the model.

Parameters

validationSetSize the new validation set size (as a percentage)

Returns: returns true if the validationSetSize parameter was updated, false otherwise

Definition at line 316 of file MLBase.cpp.

bool MLBase::setValidationSetSize ( const UINT validationSetSize )

Sets the size of the validation set used by some learning algorithms for training. This value represents the percentage of the main dataset that will be used for training. For example, if the validationSetSize parameter is 20 then 20% of the training data will be used for a validation set leaving 80% of the original data to train the model.

Parameters

validationSetSize the new validation set size (as a percentage)

Returns: returns true if the validationSetSize parameter was updated, false otherwise

Definition at line 304 of file MLBase.cpp.

bool MLBase::train ( ClassificationData trainingData )

virtual

This is the main training interface for ClassificationData. By default it will call the train_ function, unless it is overwritten by the derived class.

Parameters

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

Returns: returns true if the classifier was successfully trained, false otherwise

Reimplemented in LDA, and HMM.

Definition at line 89 of file MLBase.cpp.

bool MLBase::train ( RegressionData trainingData )

virtual

This is the main training interface for regression data. By default it will call the train_ function, unless it is overwritten by the derived class.

Parameters

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

Returns: returns true if a new regression model was trained, false otherwise

Definition at line 93 of file MLBase.cpp.

bool MLBase::train ( TimeSeriesClassificationData trainingData )

virtual

This is the main training interface for TimeSeriesClassificationData. By default it will call the train_ function, unless it is overwritten by the derived class.

Parameters

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

Returns: returns true if the classifier was successfully trained, false otherwise

Definition at line 97 of file MLBase.cpp.

bool MLBase::train ( ClassificationDataStream trainingData )

virtual

This is the main training interface for ClassificationDataStream. By default it will call the train_ function, unless it is overwritten by the derived class.

Parameters

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

Returns: returns true if the classifier was successfully trained, false otherwise

Definition at line 101 of file MLBase.cpp.

bool MLBase::train ( UnlabelledData trainingData )

virtual

This is the main training interface for UnlabelledData. By default it will call the train_ function, unless it is overwritten by the derived class.

Parameters

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

Returns: returns true if the classifier was successfully trained, false otherwise

Definition at line 105 of file MLBase.cpp.

bool MLBase::train ( MatrixFloat data )

virtual

This is the main training interface for MatrixFloat data. By default it will call the train_ function, unless it is overwritten by the derived class.

Parameters

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

Returns: returns true if the classifier was successfully trained, false otherwise

Definition at line 109 of file MLBase.cpp.

bool MLBase::train_ ( ClassificationData & trainingData )

virtual

This is the main training interface for referenced ClassificationData. This should be overwritten by the derived class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

Returns: returns true if the classifier was successfully trained, false otherwise

Reimplemented in SelfOrganizingMap, HierarchicalClustering, SOMQuantizer, KMeansFeatures, RBMQuantizer, KMeansQuantizer, KMeans, SVM, AdaBoost, KNN, RandomForests, GaussianMixtureModels, DecisionTree, ANBC, GMM, BAG, MinDist, Softmax, SwipeDetector, FiniteStateMachine, MLP, and Clusterer.

Definition at line 91 of file MLBase.cpp.

bool MLBase::train_ ( RegressionData & trainingData )

virtual

This is the main training interface for all the regression algorithms. This should be overwritten by the derived class.

Parameters

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

Returns: returns true if a new regression model was trained, false otherwise

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

Definition at line 95 of file MLBase.cpp.

bool MLBase::train_ ( TimeSeriesClassificationData & trainingData )

virtual

This is the main training interface for referenced TimeSeriesClassificationData. This should be overwritten by the derived class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

Returns: returns true if the classifier was successfully trained, false otherwise

Reimplemented in SOMQuantizer, DTW, KMeansFeatures, RBMQuantizer, KMeansQuantizer, HMM, FiniteStateMachine, and ParticleClassifier.

Definition at line 99 of file MLBase.cpp.

bool MLBase::train_ ( ClassificationDataStream & trainingData )

virtual

This is the main training interface for referenced ClassificationDataStream. This should be overwritten by the derived class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

Returns: returns true if the classifier was successfully trained, false otherwise

Reimplemented in SOMQuantizer, KMeansFeatures, RBMQuantizer, KMeansQuantizer, and FiniteStateMachine.

Definition at line 103 of file MLBase.cpp.

bool MLBase::train_ ( UnlabelledData & trainingData )

virtual

This is the main training interface for referenced UnlabelledData. This should be overwritten by the derived class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

Returns: returns true if the classifier was successfully trained, false otherwise

Reimplemented in SelfOrganizingMap, HierarchicalClustering, SOMQuantizer, KMeansFeatures, RBMQuantizer, KMeansQuantizer, KMeans, GaussianMixtureModels, and Clusterer.

Definition at line 107 of file MLBase.cpp.

bool MLBase::train_ ( MatrixFloat & data )

virtual

This is the main training interface for referenced MatrixFloat data. This should be overwritten by the derived class.

Parameters

trainingData a reference to the training data that will be used to train the ML model

Returns: returns true if the classifier was successfully trained, false otherwise

Reimplemented in SelfOrganizingMap, HierarchicalClustering, SOMQuantizer, KMeansFeatures, RBMQuantizer, KMeansQuantizer, KMeans, GaussianMixtureModels, ClusterTree, BernoulliRBM, and Clusterer.

Definition at line 111 of file MLBase.cpp.

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

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation