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.
ZeroCrossingCounter Class Reference
Inheritance diagram for ZeroCrossingCounter:
FeatureExtraction MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Types

enum  ZeroCrossingFeatureIDs { NUM_ZERO_CROSSINGS_COUNTED =0, ZERO_CROSSING_MAGNITUDE, TOTAL_NUM_ZERO_CROSSING_FEATURES }
 
enum  FeatureModes { INDEPENDANT_FEATURE_MODE =0, COMBINED_FEATURE_MODE }
 
- 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 }
 

Public Member Functions

 ZeroCrossingCounter (UINT searchWindowSize=20, Float deadZoneThreshold=0.01, UINT numDimensions=1, UINT featureMode=INDEPENDANT_FEATURE_MODE)
 
 ZeroCrossingCounter (const ZeroCrossingCounter &rhs)
 
virtual ~ZeroCrossingCounter ()
 
ZeroCrossingCounteroperator= (const ZeroCrossingCounter &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 searchWindowSize, Float deadZoneThreshold, UINT numDimensions, UINT featureMode)
 
VectorFloat update (Float x)
 
VectorFloat update (const VectorFloat &x)
 
bool setSearchWindowSize (UINT searchWindowSize)
 
bool setFeatureMode (UINT featureMode)
 
bool setDeadZoneThreshold (Float deadZoneThreshold)
 
UINT getSearchWindowSize ()
 
UINT getNumFeatures ()
 
UINT getFeatureMode ()
 
Float getDeadZoneThreshold ()
 
CircularBuffer< VectorFloatgetDataBuffer ()
 
- 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 VectorFloatgetFeatureVector () const
 
const MatrixFloatgetFeatureMatrix () const
 
FeatureExtractioncreateNewInstance () 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 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)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () 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)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () 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 searchWindowSize
 The size of the search window, i.e. the amount of previous data stored and searched.
 
UINT featureMode
 The featureMode controls how the features are added to the feature vector.
 
Float deadZoneThreshold
 The threshold value used for the dead zone filter.
 
Derivative derivative
 Used to compute the derivative of the input signal.
 
DeadZone deadZone
 Used to remove small amounts of noise from the data.
 
CircularBuffer< VectorFloatdataBuffer
 A buffer used to store the previous derivative 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< ZeroCrossingCounterregisterModule
 

Additional Inherited Members

- Static Public Member Functions inherited from FeatureExtraction
static FeatureExtractioncreateInstanceFromString (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 StringFeatureExtractionMapgetMap ()
 

Detailed Description

Definition at line 50 of file ZeroCrossingCounter.h.

Constructor & Destructor Documentation

ZeroCrossingCounter::ZeroCrossingCounter ( UINT  searchWindowSize = 20,
Float  deadZoneThreshold = 0.01,
UINT  numDimensions = 1,
UINT  featureMode = INDEPENDANT_FEATURE_MODE 
)

Constructor, sets the search window size, deadZoneThreshold, and the dimensionality of the input data. The search window size sets how much data should be held in memory and searched each time the update function is called.

Parameters
searchWindowSizesets how much data should be held in memory and searched each time the update function is called. Default value = 20
deadZoneThresholdsets the dead zone threshold. Default value = 0.01
numDimensionsthe dimensionality of the input data to filter. Default numDimensions = 1
featureModesets how the features are added to the feature vector, shoule be either INDEPENDANT_FEATURE_MODE or COMBINED_FEATURE_MODE. Default is featureMode = INDEPENDANT_FEATURE_MODE

Definition at line 28 of file ZeroCrossingCounter.cpp.

ZeroCrossingCounter::ZeroCrossingCounter ( const ZeroCrossingCounter rhs)

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

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

Definition at line 39 of file ZeroCrossingCounter.cpp.

ZeroCrossingCounter::~ZeroCrossingCounter ( )
virtual

Default Destructor

Definition at line 51 of file ZeroCrossingCounter.cpp.

Member Function Documentation

bool ZeroCrossingCounter::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 ZeroCrossingCounter'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 84 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::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 ZeroCrossingCounter, 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 69 of file ZeroCrossingCounter.cpp.

CircularBuffer< VectorFloat > ZeroCrossingCounter::getDataBuffer ( )
inline

Gets the current values in the data buffer. An empty circular buffer will be returned if the ZeroCrossingCounter has not been initialized.

Returns
returns a curcular buffer containing the data buffer values, an empty circular buffer will be returned if the ZeroCrossingCounter has not been initialized

Definition at line 237 of file ZeroCrossingCounter.h.

Float ZeroCrossingCounter::getDeadZoneThreshold ( )
inline

Gets the deadZoneThreshold value.

Returns
returns a Float representing the deadZoneThreshold, returns zero if the feature extraction module has not been initialized

Definition at line 229 of file ZeroCrossingCounter.h.

UINT ZeroCrossingCounter::getFeatureMode ( )
inline

Gets the current featureMode, this will be either INDEPENDANT_FEATURE_MODE (0) or COMBINED_FEATURE_MODE (1).

Returns
returns an unsigned int representing the current feature mode

Definition at line 222 of file ZeroCrossingCounter.h.

UINT ZeroCrossingCounter::getNumFeatures ( )
inline

Gets the number of feature values computed for each input dimensions. The size of the feature vector will be getNumFeatures() * numInputDimensions.

Returns
returns an unsigned int representing the total number of zero crossing features per input dimension

Definition at line 215 of file ZeroCrossingCounter.h.

UINT ZeroCrossingCounter::getSearchWindowSize ( )
inline

Gets the search window size.

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

Definition at line 207 of file ZeroCrossingCounter.h.

bool ZeroCrossingCounter::init ( UINT  searchWindowSize,
Float  deadZoneThreshold,
UINT  numDimensions,
UINT  featureMode 
)

Initializes the ZeroCrossingCounter, setting the searchWindowSize, deadZoneThreshold, and dimensionality of the data it will filter. The search window size, deadZoneThreshold, and numDimensions values must be larger than 0. Sets all the data buffer values to zero.

Parameters
searchWindowSizesets how much data should be held in memory and searched each time the update function is called
deadZoneThresholdsets the dead zone threshold value
numDimensionsthe dimensionality of the input data to filter
featureModesets how the features are added to the feature vector, shoule be either INDEPENDANT_FEATURE_MODE or COMBINED_FEATURE_MODE
Returns
true if the filter was initiliazed, false otherwise

Definition at line 208 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::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

Reimplemented from MLBase.

Definition at line 122 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::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 161 of file ZeroCrossingCounter.cpp.

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

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

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

Definition at line 55 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::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 101 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::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

Reimplemented from MLBase.

Definition at line 108 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::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 137 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::setDeadZoneThreshold ( Float  deadZoneThreshold)

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

Parameters
deadZoneThresholdsets the dead zone threshold value
Returns
true if the deadZoneThreshold value was updated, false otherwise

Definition at line 326 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::setFeatureMode ( UINT  featureMode)

Sets the featureMode, this should be either INDEPENDANT_FEATURE_MODE (0) or COMBINED_FEATURE_MODE (1). Calling this function will reset the feature extraction.

Parameters
featureModesets the featureMode, options are either INDEPENDANT_FEATURE_MODE (0) or COMBINED_FEATURE_MODE (1)
Returns
true if the featureMode value was updated, false otherwise

Definition at line 316 of file ZeroCrossingCounter.cpp.

bool ZeroCrossingCounter::setSearchWindowSize ( UINT  searchWindowSize)

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

Parameters
searchWindowSizesets how much data should be held in memory and searched each time the update function is called
Returns
true if the searchWindowSize value was updated, false otherwise

Definition at line 306 of file ZeroCrossingCounter.cpp.

VectorFloat ZeroCrossingCounter::update ( Float  x)

Computes the ZeroCrossingCounter features from the input, this should only be called if the dimensionality of this instance was set to 1.

Parameters
xthe value to compute features from, this should only be called if the dimensionality of the filter was set to 1
Returns
a vector containing the ZeroCrossingCounter features, an empty vector will be returned if the features were not computed

Definition at line 251 of file ZeroCrossingCounter.cpp.

VectorFloat ZeroCrossingCounter::update ( const VectorFloat x)

Computes the ZeroCrossingCounter features from the input, 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 ZeroCrossingCounter features, an empty vector will be returned if the features were not computed

Definition at line 255 of file ZeroCrossingCounter.cpp.


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