GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
Classes | Public Member Functions | Protected Types | Protected Member Functions | Protected Attributes | List of all members
BernoulliRBM Class Reference

#include <BernoulliRBM.h>

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

Classes

struct  BatchIndexs
 

Public Member Functions

 BernoulliRBM (const UINT numHiddenUnits=100, const UINT maxNumEpochs=1000, const Float learningRate=1, const Float learningRateUpdate=1, const Float momentum=0.5, const bool useScaling=true, const bool randomiseTrainingOrder=true)
 
bool predict_ (VectorFloat &inputData)
 
bool predict_ (VectorFloat &inputData, VectorFloat &outputData)
 
bool predict_ (const MatrixFloat &inputData, MatrixFloat &outputData, const UINT rowIndex)
 
virtual bool train_ (MatrixFloat &data)
 
virtual bool reset ()
 
virtual bool clear ()
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
bool reconstruct (const VectorFloat &input, VectorFloat &output)
 
virtual bool print () const
 
bool getRandomizeWeightsForTraining () const
 
UINT getNumVisibleUnits () const
 
UINT getNumHiddenUnits () const
 
VectorFloat getOutputData () const
 
const MatrixFloat & getWeights () const
 
bool setNumHiddenUnits (const UINT numHiddenUnits)
 
bool setMomentum (const Float momentum)
 
bool setLearningRateUpdate (const Float learningRateUpdate)
 
bool setRandomizeWeightsForTraining (const bool randomizeWeightsForTraining)
 
bool setBatchSize (const UINT batchSize)
 
bool setBatchStepSize (const UINT batchStepSize)
 
- 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 predict (VectorFloat inputVector)
 
virtual bool predict (MatrixFloat inputMatrix)
 
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 Types

typedef struct BatchIndexs BatchIndexs
 

Protected Member Functions

bool loadLegacyModelFromFile (std::fstream &file)
 <Tell the compiler we are using the base class predict method to stop hidden virtual function warnings
 
Float sigmoidRandom (const Float &x)
 
- 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

bool randomizeWeightsForTraining
 
UINT numVisibleUnits
 
UINT numHiddenUnits
 
UINT batchSize
 
UINT batchStepSize
 
Float momentum
 
Float learningRateUpdate
 
MatrixFloat weightsMatrix
 
VectorFloat visibleLayerBias
 
VectorFloat hiddenLayerBias
 
VectorFloat ph_mean
 
VectorFloat ph_sample
 
VectorFloat nv_means
 
VectorFloat nv_samples
 
VectorFloat nh_means
 
VectorFloat nh_samples
 
VectorFloat outputData
 
Vector< MinMax > ranges
 
Random rand
 
- 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 42 of file BernoulliRBM.h.

Member Function Documentation

bool 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 MLBase.

Definition at line 402 of file BernoulliRBM.cpp.

bool BernoulliRBM::loadModelFromFile ( 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 484 of file BernoulliRBM.cpp.

bool BernoulliRBM::predict_ ( VectorFloat &  inputData)
virtual

This is the prediction interface for referenced VectorFloat 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
inputDataa 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 MLBase.

Definition at line 32 of file BernoulliRBM.cpp.

bool BernoulliRBM::predict_ ( VectorFloat &  inputData,
VectorFloat &  outputData 
)

This is the main prediction interface for referenced VectorFloat 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
inputDataa reference to the input data that will be used to train the RBM model
outputDataa 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 BernoulliRBM::predict_ ( const MatrixFloat &  inputData,
MatrixFloat &  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
inputDataa reference to the input data
outputDataa reference to the output data that will store the results of the propagation
rowIndexthe 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 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 MLBase.

Definition at line 632 of file BernoulliRBM.cpp.

bool 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 MLBase.

Definition at line 394 of file BernoulliRBM.cpp.

bool BernoulliRBM::saveModelToFile ( 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 425 of file BernoulliRBM.cpp.

bool BernoulliRBM::train_ ( MatrixFloat &  data)
virtual

This is the main training interface for referenced MatrixFloat data.

Parameters
trainingDataa 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 MLBase.

Definition at line 111 of file BernoulliRBM.cpp.

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