This class acts as the main interface for using a Hidden Markov Model. More...

#include <HMM.h>

Inheritance diagram for HMM:

Public Member Functions
	HMM (const UINT hmmType=HMM_CONTINUOUS, const UINT modelType=HMM_LEFTRIGHT, const UINT delta=1, const bool useScaling=false, const bool useNullRejection=false)

	HMM (const HMM &rhs)

virtual	~HMM (void)

HMM &	operator= (const HMM &rhs)

virtual bool	deepCopyFrom (const Classifier *classifier)

virtual bool	train (ClassificationData trainingData)

virtual bool	train_ (TimeSeriesClassificationData &trainingData)

virtual bool	predict_ (VectorFloat &inputVector)

virtual bool	predict_ (MatrixFloat &timeseries)

virtual bool	reset ()

virtual bool	clear ()

virtual bool	print () const

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

virtual bool	load (std::fstream &file)

UINT	getHMMType () const

UINT	getModelType () const

UINT	getDelta () const

UINT	getNumStates () const

UINT	getNumSymbols () const

UINT	getNumRandomTrainingIterations () const

Vector< DiscreteHiddenMarkovModel >	getDiscreteModels () const

Vector< ContinuousHiddenMarkovModel >	getContinuousModels () const

bool	setHMMType (const UINT hmmType)

bool	setModelType (const UINT modelType)

bool	setDelta (const UINT delta)

bool	setDownsampleFactor (const UINT downsampleFactor)

bool	setCommitteeSize (const UINT committeeSize)

bool	setNumStates (const UINT numStates)

bool	setNumSymbols (const UINT numStates)

bool	setNumRandomTrainingIterations (const UINT numRandomTrainingIterations)

bool	setSigma (const Float sigma)

bool	setAutoEstimateSigma (const bool autoEstimateSigma)

Public Member Functions inherited from Classifier
	Classifier (const std::string &classifierId="")

virtual	~Classifier (void)

bool	copyBaseVariables (const Classifier *classifier)

virtual bool	computeAccuracy (const ClassificationData &data, Float &accuracy)

std::string	getClassifierType () const

bool	getSupportsNullRejection () const

bool	getNullRejectionEnabled () const

Float	getNullRejectionCoeff () const

Float	getMaximumLikelihood () const

Float	getBestDistance () const

Float	getPhase () const

Float	getTrainingSetAccuracy () const

virtual UINT	getNumClasses () const

UINT	getClassLabelIndexValue (const 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 (const bool useNullRejection)

virtual bool	setNullRejectionCoeff (const Float nullRejectionCoeff)

virtual bool	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)

virtual bool	recomputeNullRejectionThresholds ()

bool	getTimeseriesCompatible () const

Classifier *	create () const

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

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

Classifier *	deepCopy () const

const Classifier *	getClassifierPointer () const

const Classifier &	getBaseClassifier () 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 (RegressionData 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 (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	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

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

static Vector< std::string >	getRegisteredClassifiers ()

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

static std::string	getGRTRevison ()

Protected Member Functions
bool	train_discrete (TimeSeriesClassificationData &trainingData)

bool	train_continuous (TimeSeriesClassificationData &trainingData)

bool	predict_discrete (VectorFloat &inputVector)

bool	predict_continuous (VectorFloat &inputVector)

bool	predict_discrete (MatrixFloat &timeseries)

bool	predict_continuous (MatrixFloat &timeseries)

bool	convertDataToObservationSequence (TimeSeriesClassificationData &classData, Vector< Vector< UINT > > &observationSequences)

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 Attributes
UINT	hmmType
	Controls if this is a HMM_DISCRETE or a HMM_CONTINUOUS.

UINT	modelType

UINT	delta

UINT	numStates

UINT	numSymbols

UINT	numRandomTrainingIterations

Vector< DiscreteHiddenMarkovModel >	discreteModels

UINT	downsampleFactor

UINT	committeeSize

Float	sigma

bool	autoEstimateSigma

Vector< ContinuousHiddenMarkovModel >	continuousModels

Protected Attributes inherited from Classifier
bool	supportsNullRejection

bool	useNullRejection

UINT	numClasses

UINT	predictedClassLabel

UINT	classifierMode

Float	nullRejectionCoeff

Float	maxLikelihood

Float	bestDistance

Float	phase

Float	trainingSetAccuracy

VectorFloat	classLikelihoods

VectorFloat	classDistances

VectorFloat	nullRejectionThresholds

Vector< UINT >	classLabels

Vector< MinMax >	ranges

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 Classifier
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }

typedef std::map< std::string, Classifier ()() >	StringClassifierMap

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 Classifier
static StringClassifierMap *	getMap ()

Detailed Description

This class acts as the main interface for using a Hidden Markov Model.

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.

Remarks: This implementation is based on Rabiner, Lawrence. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2 (1989): 257-286.

Definition at line 41 of file HMM.h.

Constructor & Destructor Documentation

HMM::HMM	(	const UINT	hmmType = `HMM_CONTINUOUS`,
		const UINT	modelType = `HMM_LEFTRIGHT`,
		const UINT	delta = `1`,
		const bool	useScaling = `false`,
		const bool	useNullRejection = `false`
	)

Default Constructor.

Sets up the HMM instance with default parameters.

Parameters

hmmType	sets the HMM type, this should be either HMM_DISCRETE or HMM_CONTINUOUS. Default: HMM_CONTINUOUS
modelType	sets the model type used by either the discrete or continuous hmm. This should be either HMM_ERGODIC or HMM_LEFTRIGHT. Default: HMM_LEFTRIGHT
delta	sets the number of states a model can transistion to for a HMM_LEFTRIGHT model. Default: 1
useScaling	sets if the training/input data should be scaled to the range [0 1]. Default: false
useNullRejection	sets if the algorithm should use null rejection (i.e., automatically gesture spotting). Default: false

Definition at line 33 of file HMM.cpp.

HMM::HMM ( const HMM & rhs )

Default Constructor.

Copies the settings/models from the rhs HMM instance to this instance.

Parameters

rhs	another HMM instance from which the settings/models will be copied to this instance

Definition at line 57 of file HMM.cpp.

HMM::~HMM ( void )

virtual

Default Destructor.

Definition at line 63 of file HMM.cpp.

Member Function Documentation

bool HMM::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 644 of file HMM.cpp.

bool HMM::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 HMM instance) into this instance

Parameters

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

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

Reimplemented from Classifier.

Definition at line 87 of file HMM.cpp.

Vector< ContinuousHiddenMarkovModel > HMM::getContinuousModels ( ) const

This function gets returns a Vector of trained ContinuousHiddenMarkovModels. There will be one HiddenMarkovModel for each sample in the training data. This is only relevant if the HMM model type is HMM_CONTINUOUS.

Returns: returns the trained ContinuousHiddenMarkovModels

Definition at line 949 of file HMM.cpp.

UINT HMM::getDelta ( ) const

This function gets the delta value used for each HMM. The delta value controls how many states a model can transition to if the LEFTRIGHT model type is used.

Returns: returns the delta parameter for each HMM

Definition at line 929 of file HMM.cpp.

Vector< DiscreteHiddenMarkovModel > HMM::getDiscreteModels ( ) const

This function gets returns a Vector of trained DiscreteHiddenMarkovModels. There will be one HiddenMarkovModel for each class in the training data. This is only relevant if the HMM model type is HMM_DISCRETE.

Returns: returns the trained DiscreteHiddenMarkovModels

Definition at line 945 of file HMM.cpp.

UINT HMM::getHMMType ( ) const

This function gets the HMM type. This will be either HMM_DISCRETE or HMM_CONTINUOUS.

Returns: returns the current HMM type

Definition at line 921 of file HMM.cpp.

std::string HMM::getId ( )

static

Gets a string that represents the KNN class.

Returns: returns a string containing the ID of this class

Definition at line 28 of file HMM.cpp.

UINT HMM::getModelType ( ) const

This function gets the model type for each HMM, this will be one of the HMM enum ModelTypes.

Returns: returns the model type for each HMM

Definition at line 925 of file HMM.cpp.

UINT HMM::getNumRandomTrainingIterations ( ) const

This function gets the number of random training iterations used to train each discrete HMM.

The accuracy of the Baum Welch algorithm can be effected by the starting values of the A, B, and PI matrices. The HMM algorithm therefore tries several different starting values and then continues to train the algorithm with the best settings.

The number of random training iterations parameter therefore controls the number of different starting values.

Returns: returns the maximum number of iterations used to trained each HMM

Definition at line 941 of file HMM.cpp.

UINT HMM::getNumStates ( ) const

This function gets the number of states in each HMM. This is only relevant if the HMM model type is HMM_DISCRETE.

Returns: returns the number of states in each discrete HMM

Definition at line 933 of file HMM.cpp.

UINT HMM::getNumSymbols ( ) const

This function gets the number of symbols in each HMM. This is only relevant if the HMM model type is HMM_DISCRETE.

Returns: returns the number of symbols in each discrete HMM

Definition at line 937 of file HMM.cpp.

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

virtual

This loads a trained HMM model from a file. This overrides the load function in the Classifier base class.

Parameters

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

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

Reimplemented from MLBase.

Definition at line 753 of file HMM.cpp.

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

Custom Equals Operator.

Copies the settings/models from the rhs HMM instance to this instance.

Parameters

rhs	another HMM instance from which the settings/models will be copied to this instance

Returns: returns a reference to this instance

Definition at line 67 of file HMM.cpp.

bool HMM::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 279 of file HMM.cpp.

bool HMM::predict_ ( MatrixFloat & timeseries )

virtual

This predicts the class of the timeseries. This overrides the predict function in the Classifier base class.

Parameters

timeSeries the input timeseries to classify

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

Reimplemented from MLBase.

Definition at line 451 of file HMM.cpp.

bool HMM::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 from MLBase.

Definition at line 655 of file HMM.cpp.

bool HMM::reset ( )

virtual

This resets the HMM classifier.

Returns: returns true if the HMM model was successfully reset, false otherwise.

Reimplemented from Classifier.

Definition at line 623 of file HMM.cpp.

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

virtual

This saves the trained HMM model to a file. This overrides the save function in the Classifier base class.

Parameters

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

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

Reimplemented from MLBase.

Definition at line 698 of file HMM.cpp.

bool HMM::setCommitteeSize ( const UINT committeeSize )

This function sets the committeeSize used for a HMM_CONTINUOUS. The commitee size controls the number of votes used to make a prediction for a continuous HMM. For example, if the committeeSize is 5, then the top 5 estimations will be combined togethere

The parameter must be greater than zero. This will NOT clear any trained model.

Parameters

committeeSize the committeeSize used for a continuous HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 1002 of file HMM.cpp.

bool HMM::setDelta ( const UINT delta )

This function sets the delta parameter in each HMM.

The delta value controls how many states a model can transition to if the LEFTRIGHT model type is used.

The parameter must be greater than zero.

This will clear any trained model.

Parameters

delta the delta parameter used for each HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 979 of file HMM.cpp.

bool HMM::setDownsampleFactor ( const UINT downsampleFactor )

This function sets the downsample factor used for a HMM_CONTINUOUS. The downsample factor controls the resampling of each training timeseries for the continuous HMM. A downsample factor of 5, for instance, will result in each timeseries being resized (smaller) by a factor of 5. Increasing the downsample factor will significantly increase the realtime prediction time for each model, however, setting this value too high may reduce the overall accuracy of the model.

The parameter must be greater than zero. This will clear any trained model.

Parameters

downsampleFactor the downsample factor used for a continuous HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 992 of file HMM.cpp.

bool HMM::setHMMType ( const UINT hmmType )

This function sets the hmmType. This should be either a HMM_DISCRETE, or HMM_CONTINUOUS.

This will clear any trained model.

Parameters

hmmType the new hmmType

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 953 of file HMM.cpp.

bool HMM::setModelType ( const UINT modelType )

This function sets the modelType used for each HMM. This should be one of the HMM modelType enums.

This will clear any trained model.

Parameters

modelType the modelType in each HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 966 of file HMM.cpp.

bool HMM::setNumRandomTrainingIterations ( const UINT numRandomTrainingIterations )

This function sets the number of random training iterations used to train each HMM.

The accuracy of the Baum Welch algorithm can be effected by the starting values of the A, B, and PI matrices. The HMM algorithm therefore tries several different starting values and then continues to train the algorithm with the best settings. The number of random training iterations parameter therefore controls the number of different starting values.

This will clear any trained model.

Parameters

numRandomTrainingIterations the number of random training iterations used to train each HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 1038 of file HMM.cpp.

bool HMM::setNumStates ( const UINT numStates )

This function sets the number of states for a HMM_DISCRETE.

The parameter must be greater than zero. This will clear any trained model.

Parameters

numStates the number of states in each HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 1012 of file HMM.cpp.

bool HMM::setNumSymbols ( const UINT numStates )

This function sets the number of symbols for a HMM_DISCRETE. The parameter must be greater than zero. This will clear any trained model.

Parameters

symbols the number of symbols in each HMM

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 1025 of file HMM.cpp.

bool HMM::setSigma ( const Float sigma )

This function sets the sigma parameter used for HMM_CONTINUOUS.

Sigma controls the width of the Gaussian distance function that estimates the similarity between the input sample and the model.

Modifing sigma will NOT clear the trained model, enabling the user to tune this parameter when needed.

Sigma must be greater than zero.

Parameters

sigma the new sigma parameter

Returns: returns true if the parameter was set correctly, false otherwise

Definition at line 1051 of file HMM.cpp.

bool HMM::train ( ClassificationData trainingData )

virtual

This overrides the train function in the Classifier base class. It simply prints a warning message stating that the bool train(LabelledTimeSeriesClassificationData trainingData) function should be used to train the HMM model.

Parameters

trainingData a reference to the training data

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

Reimplemented from MLBase.

Definition at line 115 of file HMM.cpp.

bool HMM::train_ ( TimeSeriesClassificationData & trainingData )

virtual

This trains the HMM model, using the labelled timeseries classification data. This is the main training function for the HMM class. This overrides the train function in the Classifier base class.

Parameters

trainingData a reference to the training data

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

Reimplemented from MLBase.

Definition at line 121 of file HMM.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