#include <ContinuousHiddenMarkovModel.h>

Inheritance diagram for ContinuousHiddenMarkovModel:

Public Member Functions
	ContinuousHiddenMarkovModel (const UINT downsampleFactor=5, const UINT delta=1, const bool autoEstimateSigma=true, const Float sigma=10.0)

	ContinuousHiddenMarkovModel (const ContinuousHiddenMarkovModel &rhs)

ContinuousHiddenMarkovModel &	operator= (const ContinuousHiddenMarkovModel &rhs)

virtual bool	predict_ (VectorFloat &x)

virtual bool	predict_ (MatrixFloat &obs)

virtual bool	train_ (TimeSeriesClassificationSample &trainingData)

virtual bool	reset ()

virtual bool	clear ()

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

virtual bool	loadModelFromFile (std::fstream &file)

virtual bool	print () const

UINT	getNumStates () const

UINT	getClassLabel () const

Float	getLoglikelihood () const

Float	getPhase () const

Vector< UINT >	getEstimatedStates () const

MatrixFloat	getAlpha () const

bool	setDownsampleFactor (const UINT downsampleFactor)

bool	setModelType (const UINT modelType)

bool	setDelta (const UINT delta)

bool	setSigma (const Float sigma)

bool	setAutoEstimateSigma (const bool autoEstimateSigma)

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_ (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	map (VectorFloat inputVector)

virtual bool	map_ (VectorFloat &inputVector)

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
Float	gauss (const MatrixFloat &x, const MatrixFloat &y, const MatrixFloat &sigma, const unsigned int i, const unsigned int j, const unsigned int N)

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
UINT	downsampleFactor

UINT	numStates
	The number of states for this model.

UINT	classLabel
	The class label associated with this model.

UINT	timeseriesLength
	The length of the training timeseries.

bool	autoEstimateSigma

Float	sigma

Float	phase

MatrixFloat	a
	The transitions probability matrix.

MatrixFloat	b
	The emissions probability matrix.

VectorFloat	pi
	The state start probability vector.

MatrixFloat	alpha

VectorFloat	c

CircularBuffer< VectorFloat >	observationSequence
	A buffer to store data for realtime prediction.

MatrixFloat	obsSequence

Vector< UINT >	estimatedStates
	The estimated states for prediction.

MatrixFloat	sigmaStates
	The sigma value for each state.

UINT	modelType
	The model type (LEFTRIGHT, or ERGODIC)

UINT	delta
	The number of states a model can move to in a LEFTRIGHT model.

Float	loglikelihood
	The log likelihood of an observation sequence given the modal, calculated by the forward method.

Float	cThreshold
	The classification threshold for this model.

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

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

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

static std::string	getGRTRevison ()

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 ContinuousHiddenMarkovModel.h.

Member Function Documentation

bool ContinuousHiddenMarkovModel::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 from MLBase.

Definition at line 385 of file ContinuousHiddenMarkovModel.cpp.

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

virtual

This loads a trained model from a file.

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 from MLBase.

Definition at line 580 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::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 from MLBase.

Definition at line 110 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::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 from MLBase.

Definition at line 135 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::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 406 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::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 from MLBase.

Definition at line 371 of file ContinuousHiddenMarkovModel.cpp.

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

virtual

This saves the trained model to a file.

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 from MLBase.

Definition at line 515 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::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

const UINT delta: the delta parameter used for each CHMM

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

Definition at line 475 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::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

const UINT modelType: the modelType in each HMM

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

Definition at line 465 of file ContinuousHiddenMarkovModel.cpp.

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

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation