GestureRecognitionToolkit  Version: 1.0 Revision: 04-03-15
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
GRT::BernoulliRBM Class Reference
Inheritance diagram for GRT::BernoulliRBM:
GRT::MLBase GRT::GRTBase GRT::Observer< TrainingResult > GRT::Observer< TestInstanceResult >

Classes

struct  BatchIndexs
 

Public Member Functions

 BernoulliRBM (const UINT numHiddenUnits=100, const UINT maxNumEpochs=1000, const double learningRate=1, const double learningRateUpdate=1, const double momentum=0.5, const bool useScaling=true, const bool randomiseTrainingOrder=true)
 
bool predict_ (VectorDouble &inputData)
 
bool predict_ (VectorDouble &inputData, VectorDouble &outputData)
 
bool predict_ (const MatrixDouble &inputData, MatrixDouble &outputData, const UINT rowIndex)
 
virtual bool train_ (MatrixDouble &data)
 
virtual bool reset ()
 
virtual bool clear ()
 
virtual bool saveModelToFile (fstream &file) const
 
virtual bool loadModelFromFile (fstream &file)
 
bool reconstruct (const VectorDouble &input, VectorDouble &output)
 
virtual bool print () const
 
bool getRandomizeWeightsForTraining () const
 
UINT getNumVisibleUnits () const
 
UINT getNumHiddenUnits () const
 
VectorDouble getOutputData () const
 
const MatrixDoublegetWeights () const
 
bool setNumHiddenUnits (const UINT numHiddenUnits)
 
bool setMomentum (const double momentum)
 
bool setLearningRateUpdate (const double learningRateUpdate)
 
bool setRandomizeWeightsForTraining (const bool randomizeWeightsForTraining)
 
bool setBatchSize (const UINT batchSize)
 
bool setBatchStepSize (const UINT batchStepSize)
 
- Public Member Functions inherited from GRT::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 (TimeSeriesClassificationDataStream trainingData)
 
virtual bool train_ (TimeSeriesClassificationDataStream &trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool train (MatrixDouble data)
 
virtual bool predict (VectorDouble inputVector)
 
virtual bool predict (MatrixDouble inputMatrix)
 
virtual bool predict_ (MatrixDouble &inputMatrix)
 
virtual bool map (VectorDouble inputVector)
 
virtual bool map_ (VectorDouble &inputVector)
 
virtual bool save (const string filename) const
 
virtual bool load (const string filename)
 
virtual bool saveModelToFile (string filename) const
 
virtual bool loadModelFromFile (string filename)
 
virtual bool getModel (ostream &stream) const
 
double scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false)
 
virtual string getModelAsString () 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
 
double getMinChange () const
 
double getLearningRate () const
 
double getRootMeanSquaredTrainingError () const
 
double getTotalSquaredTrainingError () 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 (bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setMinChange (const double minChange)
 
bool setLearningRate (double learningRate)
 
bool setUseValidationSet (const bool useValidationSet)
 
bool setValidationSetSize (const UINT validationSetSize)
 
bool setRandomiseTrainingOrder (const bool randomiseTrainingOrder)
 
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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () const
 
vector< TrainingResultgetTrainingResults () const
 
- Public Member Functions inherited from GRT::GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
string getClassType () const
 
string getLastWarningMessage () const
 
string getLastErrorMessage () const
 
string getLastInfoMessage () const
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
- Public Member Functions inherited from GRT::Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from GRT::Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Types

typedef struct BatchIndexs BatchIndexs
 

Protected Member Functions

bool loadLegacyModelFromFile (fstream &file)
 <Tell the compiler we are using the base class predict method to stop hidden virtual function warnings
 
double sigmoid (const double &x)
 
double sigmoidRandom (const double &x)
 
- Protected Member Functions inherited from GRT::MLBase
bool saveBaseSettingsToFile (fstream &file) const
 
bool loadBaseSettingsFromFile (fstream &file)
 
- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 

Protected Attributes

bool randomizeWeightsForTraining
 
UINT numVisibleUnits
 
UINT numHiddenUnits
 
UINT batchSize
 
UINT batchStepSize
 
double momentum
 
double learningRateUpdate
 
MatrixDouble weightsMatrix
 
VectorDouble visibleLayerBias
 
VectorDouble hiddenLayerBias
 
VectorDouble ph_mean
 
VectorDouble ph_sample
 
VectorDouble nv_means
 
VectorDouble nv_samples
 
VectorDouble nh_means
 
VectorDouble nh_samples
 
VectorDouble outputData
 
vector< MinMaxranges
 
Random rand
 
- Protected Attributes inherited from GRT::MLBase
bool trained
 
bool useScaling
 
UINT baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT validationSetSize
 
double learningRate
 
double minChange
 
double rootMeanSquaredTrainingError
 
double totalSquaredTrainingError
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
Random random
 
vector< TrainingResulttrainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Additional Inherited Members

- Public Types inherited from GRT::MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 

Detailed Description

Definition at line 41 of file BernoulliRBM.h.

Member Function Documentation

bool GRT::BernoulliRBM::clear ( )
virtual

This function will completely clear the RBM instance, removing any trained model and setting all the base variables to their default values.

Returns
returns true if the instance was cleared succesfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 402 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::loadModelFromFile ( fstream &  file)
virtual

This loads a trained model from a file.

Parameters
fstream&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 GRT::MLBase.

Definition at line 484 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::predict_ ( VectorDouble &  inputData)
virtual

This is the prediction interface for referenced VectorDouble data, it calls the main prediction interface below. The RBM should be trained first before you use this function. The size of the input data must match the number of visible units.

Parameters
VectorDouble&inputData: a reference to the input data that will be used to train the RBM model
Returns
returns true if the prediction was successful, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 32 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::predict_ ( VectorDouble &  inputData,
VectorDouble &  outputData 
)

This is the main prediction interface for referenced VectorDouble data. It propagates the input data up through the RBM. The RBM should be trained first before you use this function. The size of the input data must match the number of visible units.

Parameters
VectorDouble&inputData: a reference to the input data that will be used to train the RBM model
VectorDouble&outputData: a reference to the output data that will be used to train the RBM model
Returns
returns true if the prediction was successful, false otherwise

Definition at line 41 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::predict_ ( const MatrixDouble inputData,
MatrixDouble outputData,
const UINT  rowIndex 
)

This function is used during the training phase to propagate the input data up through the RBM, this gives P( h_j = 1 | input ) If you are using this function then you should make sure the RBM is trained first before you use it. The size of the matrices must match the size of the model.

Parameters
constMatrixDouble &inputData: a reference to the input data
MatrixDouble&outputData: a reference to the output data that will store the results of the propagation
constUINT rowIndex: the row in the inputData/outputData that should be used for the propagation
Returns
returns true if the prediction was successful, false otherwise

Definition at line 77 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::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 GRT::MLBase.

Definition at line 632 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::reset ( )
virtual

This function will 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 instance was reset succesfully, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 394 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::saveModelToFile ( fstream &  file) const
virtual

This saves the trained model to a file.

Parameters
fstream&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 GRT::MLBase.

Definition at line 425 of file BernoulliRBM.cpp.

bool GRT::BernoulliRBM::train_ ( MatrixDouble data)
virtual

This is the main training interface for referenced MatrixDouble data.

Parameters
MatrixDouble&trainingData: a reference to the training data that will be used to train the RBM model
Returns
returns true if the model was successfully trained, false otherwise

Reimplemented from GRT::MLBase.

Definition at line 111 of file BernoulliRBM.cpp.


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