GestureRecognitionToolkit  Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
Public Member Functions | Protected Member Functions | Protected Attributes | List of all members
ContinuousHiddenMarkovModel Class Reference

#include <ContinuousHiddenMarkovModel.h>

Inheritance diagram for ContinuousHiddenMarkovModel:
MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

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

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 386 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::load ( std::fstream &  file)
virtual

This loads a trained model from a file.

Parameters
filea 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 581 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
inputVectora 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 111 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
inputMatrixa 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 136 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 407 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 372 of file ContinuousHiddenMarkovModel.cpp.

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

This saves the trained model to a file.

Parameters
filea 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 516 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
constUINT delta: the delta parameter used for each CHMM
Returns
returns true if the parameter was set correctly, false otherwise

Definition at line 476 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
constUINT modelType: the modelType in each HMM
Returns
returns true if the parameter was set correctly, false otherwise

Definition at line 466 of file ContinuousHiddenMarkovModel.cpp.

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