GestureRecognitionToolkit  Version: 0.2.5
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) override
 
virtual bool predict_ (MatrixFloat &obs) override
 
virtual bool train_ (TimeSeriesClassificationSample &trainingData)
 
virtual bool reset () override
 
virtual bool clear () override
 
virtual bool save (std::fstream &file) const override
 
virtual bool load (std::fstream &file) override
 
virtual bool print () const override
 
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 (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_ (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_ (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)
 

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 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
 
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 MLBase
enum  BaseType {
  BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,
  PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT
}
 
- 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 ( )
overridevirtual

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 375 of file ContinuousHiddenMarkovModel.cpp.

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

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 570 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::predict_ ( VectorFloat &  inputVector)
overridevirtual

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 99 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::predict_ ( MatrixFloat &  inputMatrix)
overridevirtual

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 124 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::print ( ) const
overridevirtual

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 396 of file ContinuousHiddenMarkovModel.cpp.

bool ContinuousHiddenMarkovModel::reset ( )
overridevirtual

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 361 of file ContinuousHiddenMarkovModel.cpp.

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

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 505 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 465 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 455 of file ContinuousHiddenMarkovModel.cpp.

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