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 Attributes | Static Protected Attributes | List of all members
TimeseriesBuffer Class Reference

#include <TimeseriesBuffer.h>

Inheritance diagram for TimeseriesBuffer:
FeatureExtraction MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 TimeseriesBuffer (UINT bufferSize=5, UINT numDimensions=1)
 
 TimeseriesBuffer (const TimeseriesBuffer &rhs)
 
virtual ~TimeseriesBuffer ()
 
TimeseriesBuffer & operator= (const TimeseriesBuffer &rhs)
 
virtual bool deepCopyFrom (const FeatureExtraction *featureExtraction)
 
virtual bool computeFeatures (const VectorFloat &inputVector)
 
virtual bool reset ()
 
virtual bool saveModelToFile (std::string filename) const
 
virtual bool loadModelFromFile (std::string filename)
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
bool init (UINT bufferSize, UINT numDimensions)
 
VectorFloat update (Float x)
 
VectorFloat update (const VectorFloat &x)
 
bool setBufferSize (UINT bufferSize)
 
UINT getBufferSize ()
 
Vector< VectorFloat > getDataBuffer ()
 
- Public Member Functions inherited from FeatureExtraction
 FeatureExtraction ()
 
virtual ~FeatureExtraction ()
 
bool copyBaseVariables (const FeatureExtraction *featureExtractionModule)
 
virtual bool computeFeatures (const MatrixFloat &inputMatrix)
 
virtual bool clear ()
 
std::string getFeatureExtractionType () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
bool getInitialized () const
 
bool getFeatureDataReady () const
 
const VectorFloat & getFeatureVector () const
 
const MatrixFloat & getFeatureMatrix () const
 
FeatureExtraction * createNewInstance () const
 
- 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_ (VectorFloat &inputVector)
 
virtual bool predict (MatrixFloat inputMatrix)
 
virtual bool predict_ (MatrixFloat &inputMatrix)
 
virtual bool map (VectorFloat inputVector)
 
virtual bool map_ (VectorFloat &inputVector)
 
virtual bool print () const
 
virtual bool save (const std::string filename) const
 
virtual bool load (const std::string filename)
 
virtual bool save (std::fstream &file) const
 
virtual bool load (std::fstream &file)
 
 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 Attributes

UINT bufferSize
 
CircularBuffer< VectorFloat > dataBuffer
 A buffer used to store the timeseries data.
 
- Protected Attributes inherited from FeatureExtraction
std::string featureExtractionType
 
bool initialized
 
bool featureDataReady
 
VectorFloat featureVector
 
MatrixFloat featureMatrix
 
- 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
 

Static Protected Attributes

static RegisterFeatureExtractionModule< TimeseriesBuffer > registerModule
 

Additional Inherited Members

- Public Types inherited from FeatureExtraction
typedef std::map< std::string, FeatureExtraction *(*)() > StringFeatureExtractionMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from FeatureExtraction
static FeatureExtraction * createInstanceFromString (const std::string &featureExtractionType)
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- Protected Member Functions inherited from FeatureExtraction
bool init ()
 
bool saveFeatureExtractionSettingsToFile (std::fstream &file) const
 
bool loadFeatureExtractionSettingsFromFile (std::fstream &file)
 
- 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
 
- Static Protected Member Functions inherited from FeatureExtraction
static StringFeatureExtractionMap * getMap ()
 

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 36 of file TimeseriesBuffer.h.

Constructor & Destructor Documentation

TimeseriesBuffer::TimeseriesBuffer ( UINT  bufferSize = 5,
UINT  numDimensions = 1 
)

Constructor, sets the size of the timeseries buffer and number of input dimensions.

Parameters
bufferSizesets the size of the timeseries buffer. Default value = 5
numDimensionssets the number of dimensions that will be input to the feature extraction. Default value = 1

Definition at line 29 of file TimeseriesBuffer.cpp.

TimeseriesBuffer::TimeseriesBuffer ( const TimeseriesBuffer &  rhs)

Copy constructor, copies the TimeseriesBuffer from the rhs instance to this instance.

Parameters
rhsanother instance of the TimeseriesBuffer class from which the data will be copied to this instance

Definition at line 40 of file TimeseriesBuffer.cpp.

TimeseriesBuffer::~TimeseriesBuffer ( )
virtual

Default Destructor

Definition at line 52 of file TimeseriesBuffer.cpp.

Member Function Documentation

bool TimeseriesBuffer::computeFeatures ( const VectorFloat &  inputVector)
virtual

Sets the FeatureExtraction computeFeatures function, overwriting the base FeatureExtraction function. This function is called by the GestureRecognitionPipeline when any new input data needs to be processed (during the prediction phase for example). This function calls the TimeseriesBuffer's update function.

Parameters
inputVectorthe inputVector that should be processed. Must have the same dimensionality as the FeatureExtraction module
Returns
returns true if the data was processed, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 83 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::deepCopyFrom ( const FeatureExtraction *  featureExtraction)
virtual

Sets the FeatureExtraction deepCopyFrom function, overwriting the base FeatureExtraction function. This function is used to deep copy the values from the input pointer to this instance of the FeatureExtraction module. This function is called by the GestureRecognitionPipeline when the user adds a new FeatureExtraction module to the pipeleine.

Parameters
featureExtractiona pointer to another instance of a TimeseriesBuffer, the values of that instance will be cloned to this instance
Returns
returns true if the deep copy was successful, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 66 of file TimeseriesBuffer.cpp.

UINT TimeseriesBuffer::getBufferSize ( )

Gets the buffer size.

Returns
returns an unsigned int representing the buffer size, returns zero if the feature extraction module has not been initialized

Definition at line 265 of file TimeseriesBuffer.cpp.

Vector< VectorFloat > TimeseriesBuffer::getDataBuffer ( )

Gets the current values in the timeseries buffer. An empty vector will be returned if the buffer has not been initialized.

Returns
returns a vector containing the timeseries values, an empty vector will be returned if the module has not been initialized

Definition at line 270 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::init ( UINT  bufferSize,
UINT  numDimensions 
)

Initializes the TimeseriesBuffer, setting the bufferSize and the dimensionality of the data it will buffer. The search bufferSize and numDimensions values must be larger than 0. Sets all the data buffer values to zero.

Parameters
bufferSizesets the size of the timeseries buffer
numDimensionssets the number of dimensions that will be input to the feature extraction
Returns
true if the TimeseriesBuffer was initiliazed, false otherwise

Definition at line 191 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::loadModelFromFile ( std::string  filename)
virtual

This saves the feature extraction settings to a file.

Parameters
filea reference to the file to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Definition at line 121 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::loadModelFromFile ( std::fstream &  file)
virtual

This loads the feature extraction settings from a file. This overrides the loadSettingsFromFile function in the FeatureExtraction base class.

Parameters
filea reference to the file to load the settings from
Returns
returns true if the settings were loaded successfully, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 158 of file TimeseriesBuffer.cpp.

TimeseriesBuffer & TimeseriesBuffer::operator= ( const TimeseriesBuffer &  rhs)

Sets the equals operator, copies the data from the rhs instance to this instance.

Parameters
rhsanother instance of the TimeseriesBuffer class from which the data will be copied to this instance
Returns
a reference to this instance of TimeseriesBuffer

Definition at line 56 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::reset ( )
virtual

Sets the FeatureExtraction reset function, overwriting the base FeatureExtraction function. This function is called by the GestureRecognitionPipeline when the pipelines main reset() function is called. This function resets the feature extraction by re-initiliazing the instance.

Returns
true if the filter was reset, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 100 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::saveModelToFile ( std::string  filename) const
virtual

This saves the feature extraction settings to a file.

Parameters
filenamethe filename to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Definition at line 107 of file TimeseriesBuffer.cpp.

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

This saves the feature extraction settings to a file. This overrides the saveSettingsToFile function in the FeatureExtraction base class.

Parameters
filea reference to the file to save the settings to
Returns
returns true if the settings were saved successfully, false otherwise

Reimplemented from FeatureExtraction.

Definition at line 136 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::setBufferSize ( UINT  bufferSize)

Sets the timeseries buffer size. The buffer size must be larger than zero. Calling this function will reset the feature extraction.

Parameters
bufferSizesets the size of the timeseries buffer
Returns
true if the bufferSize value was updated, false otherwise

Definition at line 255 of file TimeseriesBuffer.cpp.

VectorFloat TimeseriesBuffer::update ( Float  x)

Updates the timeseries buffer with the new data x, this should only be called if the dimensionality of this instance was set to 1.

Parameters
xthe value to add to the buffer, this should only be called if the dimensionality of the filter was set to 1
Returns
a vector containing the timeseries buffer, an empty vector will be returned if the buffer is not initialized

Definition at line 220 of file TimeseriesBuffer.cpp.

VectorFloat TimeseriesBuffer::update ( const VectorFloat &  x)

Updates the timeseries buffer with the new data x, the dimensionality of x should match that of this instance.

Parameters
xa vector containing the values to be processed, must be the same size as the numInputDimensions
Returns
a vector containing the timeseries buffer, an empty vector will be returned if the buffer is not initialized

Definition at line 224 of file TimeseriesBuffer.cpp.

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