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.
HighPassFilter Class Reference

#include <HighPassFilter.h>

Inheritance diagram for HighPassFilter:
PreProcessing MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 HighPassFilter (Float filterFactor=0.1, Float gain=1, UINT numDimensions=1, Float cutoffFrequency=-1, Float delta=-1)
 
 HighPassFilter (const HighPassFilter &rhs)
 
virtual ~HighPassFilter ()
 
HighPassFilteroperator= (const HighPassFilter &rhs)
 
virtual bool deepCopyFrom (const PreProcessing *preProcessing)
 
virtual bool process (const VectorFloat &inputVector)
 
virtual bool reset ()
 
virtual bool saveModelToFile (std::string filename) const
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::string filename)
 
virtual bool loadModelFromFile (std::fstream &file)
 
bool init (Float filterFactor, Float gain, UINT numDimensions)
 
Float filter (const Float x)
 
VectorFloat filter (const VectorFloat &x)
 
bool setGain (Float gain)
 
bool setFilterFactor (Float filterFactor)
 
bool setCutoffFrequency (Float cutoffFrequency, Float delta)
 
Float getFilterFactor ()
 
Float getGain ()
 
VectorFloat getFilteredValues ()
 
- Public Member Functions inherited from PreProcessing
 PreProcessing (void)
 
virtual ~PreProcessing (void)
 
bool copyBaseVariables (const PreProcessing *preProcessingModule)
 
virtual bool clear ()
 
std::string getPreProcessingType () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
bool getInitialized () const
 
VectorFloat getProcessedData () const
 
PreProcessingcreateNewInstance () 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

Float filterFactor
 The filter factor (alpha) of the filter.
 
Float gain
 The gain factor of the filter.
 
VectorFloat xx
 The previous input value(s)
 
VectorFloat yy
 The previous output value(s)
 
- Protected Attributes inherited from PreProcessing
std::string preProcessingType
 
bool initialized
 
VectorFloat processedData
 
- 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 RegisterPreProcessingModule< HighPassFilterregisterModule
 

Additional Inherited Members

- Public Types inherited from PreProcessing
typedef std::map< std::string, PreProcessing *(*)() > StringPreProcessingMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from PreProcessing
static PreProcessingcreateInstanceFromString (std::string const &preProcessingType)
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- Protected Member Functions inherited from PreProcessing
bool init ()
 
bool savePreProcessingSettingsToFile (std::fstream &file) const
 
bool loadPreProcessingSettingsFromFile (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 PreProcessing
static StringPreProcessingMapgetMap ()
 

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 38 of file HighPassFilter.h.

Constructor & Destructor Documentation

HighPassFilter::HighPassFilter ( Float  filterFactor = 0.1,
Float  gain = 1,
UINT  numDimensions = 1,
Float  cutoffFrequency = -1,
Float  delta = -1 
)

Constructor, sets the filter factor, gain and dimensionality of the high pass filter. If the cutoffFrequency and delta values are set then the filter will be initialized with these values rather than the filterFactor. If the cutoffFrequency and delta values are kept at their default values of -1 then the values will be ignored and the filter factor will be used instead. Otherwise the fiterFactor will control the high pass filter, with a smaller filterFactor (i.e. 0.1) resulting in a more aggresive attenuation of low frequency signals in the input signal. The filterFactor should be in the range [0.0 1.0].

Parameters
filterFactorcontrols the high pass filter, a smaller value will result in a more aggresive attenuation of low frequency signals in the input signal. Default value filterFactor = 0.1
gainmultiples the filtered values by a constant ampltidue. Default value = 1.0
numDimensionsthe dimensionality of the input data to filter. Default numDimensions = 1
cutoffFrequencysets the cutoffFrequency of the filter (in Hz). If the cutoffFrequency and delta values are set then the filter will be initialized with these values rather than the filterFactor. Default value cutoffFrequency = -1.0
deltathe sampling rate of your sensor, delta should be set as 1.0/SR, where SR is the sampling rate of your sensor. Default value delta = -1.0

Definition at line 28 of file HighPassFilter.cpp.

HighPassFilter::HighPassFilter ( const HighPassFilter rhs)

Copy Constructor, copies the HighPassFilter from the rhs instance to this instance

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

Definition at line 42 of file HighPassFilter.cpp.

HighPassFilter::~HighPassFilter ( )
virtual

Default Destructor

Definition at line 57 of file HighPassFilter.cpp.

Member Function Documentation

bool HighPassFilter::deepCopyFrom ( const PreProcessing preProcessing)
virtual

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

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

Reimplemented from PreProcessing.

Definition at line 72 of file HighPassFilter.cpp.

Float HighPassFilter::filter ( const Float  x)

Filters the input, this should only be called if the dimensionality of the filter was set to 1.

Parameters
xthe value to filter, this should only be called if the dimensionality of the filter was set to 1
Returns
the filtered value. Zero will be returned if the value was not filtered

Definition at line 259 of file HighPassFilter.cpp.

VectorFloat HighPassFilter::filter ( const VectorFloat x)

Filters the input, the dimensionality of the input vector should match that of the filter.

Parameters
xthe values to filter, the dimensionality of the input vector should match that of the filter
Returns
the filtered values. An empty vector will be returned if the values were not filtered

Definition at line 274 of file HighPassFilter.cpp.

VectorFloat HighPassFilter::getFilteredValues ( )
inline

Returns the last value(s) that were filtered.

Returns
the filtered values. An empty vector will be returned if the values were not filtered

Definition at line 214 of file HighPassFilter.h.

Float HighPassFilter::getFilterFactor ( )
inline

Gets the current filter factor if the filter has been initialized.

Returns
the current filter factor if the filter has been initialized, zero otherwise

Definition at line 200 of file HighPassFilter.h.

Float HighPassFilter::getGain ( )
inline

Gets the current gain value if the filter has been initialized.

Returns
the currentgain value if the filter has been initialized, zero otherwise

Definition at line 207 of file HighPassFilter.h.

bool HighPassFilter::init ( Float  filterFactor,
Float  gain,
UINT  numDimensions 
)

Initializes the filter, setting the filter size and dimensionality of the data it will filter. Sets all the filter values to zero.

Parameters
filterFactorcontrols the high pass filter, a smaller value will result in a more aggresive attenuation of low frequency signals in the input signal
gainmultiples the filtered values by a constant ampltidue
numDimensionsthe dimensionality of the input data to filter
Returns
true if the filter was initiliazed, false otherwise

Definition at line 225 of file HighPassFilter.cpp.

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

This loads the HighPassFilter settings from a file. This overrides the loadModelFromFile function in the PreProcessing base class.

Parameters
filenamethe name of the file to load the settings from
Returns
returns true if the settings were loaded successfully, false otherwise

Reimplemented from PreProcessing.

Definition at line 156 of file HighPassFilter.cpp.

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

This loads the HighPassFilter settings from a file. This overrides the loadModelFromFile function in the PreProcessing base class.

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

Reimplemented from PreProcessing.

Definition at line 172 of file HighPassFilter.cpp.

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

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

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

Definition at line 61 of file HighPassFilter.cpp.

bool HighPassFilter::process ( const VectorFloat inputVector)
virtual

Sets the PreProcessing process function, overwriting the base PreProcessing 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 HighPassFilter's filter function.

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

Reimplemented from PreProcessing.

Definition at line 95 of file HighPassFilter.cpp.

bool HighPassFilter::reset ( )
virtual

Sets the PreProcessing reset function, overwriting the base PreProcessing function. This function is called by the GestureRecognitionPipeline when the pipelines main reset() function is called. This function resets the filter values by re-initiliazing the filter.

Returns
true if the filter was reset, false otherwise

Reimplemented from PreProcessing.

Definition at line 114 of file HighPassFilter.cpp.

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

This saves the current settings of the HighPassFilter to a file. This overrides the saveModelToFile function in the PreProcessing base class.

Parameters
filenamethe name of the file to save the settings to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from PreProcessing.

Definition at line 119 of file HighPassFilter.cpp.

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

This saves the current settings of the HighPassFilter to a file. This overrides the saveModelToFile function in the PreProcessing base class.

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

Reimplemented from PreProcessing.

Definition at line 139 of file HighPassFilter.cpp.

bool HighPassFilter::setCutoffFrequency ( Float  cutoffFrequency,
Float  delta 
)

Sets the cutoff frequency of the filter, this updates the filterFactor. The cutoffFrequency should in Hz. This will also reset the filter.

Parameters
cutoffFrequencythe cutoff frequency of the filter in Hz
deltathe sampling rate of your sensor, delta should be set as 1.0/SR, where SR is the sampling rate of your sensor
Returns
true if the filterFactor value was set, false otherwise

Definition at line 317 of file HighPassFilter.cpp.

bool HighPassFilter::setFilterFactor ( Float  filterFactor)

Sets the filter factor, this controls the high pass filter, a smaller value will result in a more aggresive attenuation of low frequency signals in the input signal. This should be a value in the range [0.0 1.0]. This will also reset the filter.

Parameters
filterFactorthe new filterFactor value
Returns
true if the filterFactor value was set, false otherwise

Definition at line 308 of file HighPassFilter.cpp.

bool HighPassFilter::setGain ( Float  gain)

Sets the gain of the high pass filter. This will also reset the filter.

Parameters
gainthe new gain value, this multiples the filtered values by a constant ampltidue
Returns
true if the gain value was set, false otherwise

Definition at line 299 of file HighPassFilter.cpp.


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