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 | Static Public Member Functions | 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 (const UINT bufferSize=5, const 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 save (std::fstream &file) const
 
virtual bool load (std::fstream &file)
 
bool init (const UINT bufferSize, const UINT numDimensions)
 
VectorFloat update (const Float x)
 
VectorFloat update (const VectorFloat &x)
 
bool setBufferSize (const UINT bufferSize)
 
UINT getBufferSize () const
 
Vector< VectorFloat > getDataBuffer () const
 
- Public Member Functions inherited from FeatureExtraction
 FeatureExtraction (const std::string id="")
 
virtual ~FeatureExtraction ()
 
bool copyBaseVariables (const FeatureExtraction *featureExtractionModule)
 
virtual bool computeFeatures (const MatrixFloat &inputMatrix)
 
virtual bool clear () override
 
bool getInitialized () const
 
bool getFeatureDataReady () const
 
const VectorFloat & getFeatureVector () const
 
const MatrixFloat & getFeatureMatrix () const
 
FeatureExtraction * create () const
 
 GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create() instead.", FeatureExtraction *createNewInstance() const )
 
 GRT_DEPRECATED_MSG ("createInstanceFromString(id) is deprecated, use create(id) instead.", static FeatureExtraction *createInstanceFromString(const std::string &id))
 
 GRT_DEPRECATED_MSG ("getFeatureExtractionType is deprecated, use getId() instead", std::string getFeatureExtractionType() const )
 
- 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_ (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)
 
 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)
 

Static Public Member Functions

static std::string getId ()
 
- Static Public Member Functions inherited from FeatureExtraction
static FeatureExtraction * create (const std::string &id)
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 

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
 
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 FeatureExtraction
typedef std::map< std::string, FeatureExtraction *(*)() > StringFeatureExtractionMap
 
- Public Types inherited from MLBase
enum  BaseType {
  BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,
  PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT
}
 
- 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)
 
- 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 ( const UINT  bufferSize = 5,
const 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 33 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 38 of file TimeseriesBuffer.cpp.

TimeseriesBuffer::~TimeseriesBuffer ( )
virtual

Default Destructor

Definition at line 44 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 75 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 58 of file TimeseriesBuffer.cpp.

UINT TimeseriesBuffer::getBufferSize ( ) const

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 228 of file TimeseriesBuffer.cpp.

Vector< VectorFloat > TimeseriesBuffer::getDataBuffer ( ) const

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 233 of file TimeseriesBuffer.cpp.

std::string TimeseriesBuffer::getId ( )
static

Gets a string that represents the TimeseriesBuffer class.

Returns
returns a string containing the ID of this class

Definition at line 28 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::init ( const UINT  bufferSize,
const 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 154 of file TimeseriesBuffer.cpp.

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

This loads the feature extraction settings from a file. This overrides the load 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 MLBase.

Definition at line 121 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 48 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 MLBase.

Definition at line 92 of file TimeseriesBuffer.cpp.

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

This saves the feature extraction settings to a file. This overrides the save 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 MLBase.

Definition at line 99 of file TimeseriesBuffer.cpp.

bool TimeseriesBuffer::setBufferSize ( const 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 218 of file TimeseriesBuffer.cpp.

VectorFloat TimeseriesBuffer::update ( const 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 183 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 187 of file TimeseriesBuffer.cpp.

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