![]() |
GestureRecognitionToolkit
Version: 1.0 Revision: 04-03-15
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
|
Public Member Functions | |
KMeansQuantizer (const UINT numClusters=10) | |
KMeansQuantizer (const KMeansQuantizer &rhs) | |
virtual | ~KMeansQuantizer () |
KMeansQuantizer & | operator= (const KMeansQuantizer &rhs) |
virtual bool | deepCopyFrom (const FeatureExtraction *featureExtraction) |
virtual bool | computeFeatures (const VectorDouble &inputVector) |
virtual bool | reset () |
virtual bool | clear () |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
bool | train_ (ClassificationData &trainingData) |
bool | train_ (TimeSeriesClassificationData &trainingData) |
bool | train_ (TimeSeriesClassificationDataStream &trainingData) |
bool | train_ (UnlabelledData &trainingData) |
bool | train_ (MatrixDouble &trainingData) |
UINT | quantize (double inputValue) |
UINT | quantize (const VectorDouble &inputVector) |
bool | getQuantizerTrained () const |
UINT | getNumClusters () const |
UINT | getQuantizedValue () const |
VectorDouble | getQuantizationDistances () const |
MatrixDouble | getQuantizationModel () const |
bool | setNumClusters (const UINT numClusters) |
![]() | |
FeatureExtraction () | |
virtual | ~FeatureExtraction () |
bool | copyBaseVariables (const FeatureExtraction *featureExtractionModule) |
string | getFeatureExtractionType () const |
UINT | getNumInputDimensions () const |
UINT | getNumOutputDimensions () const |
bool | getInitialized () const |
bool | getFeatureDataReady () const |
VectorDouble | getFeatureVector () const |
FeatureExtraction * | createNewInstance () const |
![]() | |
MLBase (void) | |
virtual | ~MLBase (void) |
bool | copyMLBaseVariables (const MLBase *mlBase) |
virtual bool | train (ClassificationData trainingData) |
virtual bool | train (RegressionData trainingData) |
virtual bool | train_ (RegressionData &trainingData) |
virtual bool | train (TimeSeriesClassificationData trainingData) |
virtual bool | train (TimeSeriesClassificationDataStream trainingData) |
virtual bool | train (UnlabelledData trainingData) |
virtual bool | train (MatrixDouble data) |
virtual bool | predict (VectorDouble inputVector) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | predict (MatrixDouble inputMatrix) |
virtual bool | predict_ (MatrixDouble &inputMatrix) |
virtual bool | map (VectorDouble inputVector) |
virtual bool | map_ (VectorDouble &inputVector) |
virtual bool | print () const |
virtual bool | save (const string filename) const |
virtual bool | load (const string filename) |
virtual bool | saveModelToFile (string filename) const |
virtual bool | loadModelFromFile (string filename) |
virtual bool | getModel (ostream &stream) const |
double | scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false) |
virtual string | getModelAsString () 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 |
double | getMinChange () const |
double | getLearningRate () const |
double | getRootMeanSquaredTrainingError () const |
double | getTotalSquaredTrainingError () 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 (bool useScaling) |
bool | setMaxNumEpochs (const UINT maxNumEpochs) |
bool | setMinNumEpochs (const UINT minNumEpochs) |
bool | setMinChange (const double minChange) |
bool | setLearningRate (double learningRate) |
bool | setUseValidationSet (const bool useValidationSet) |
bool | setValidationSetSize (const UINT validationSetSize) |
bool | setRandomiseTrainingOrder (const bool randomiseTrainingOrder) |
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 |
![]() | |
GRTBase (void) | |
virtual | ~GRTBase (void) |
bool | copyGRTBaseVariables (const GRTBase *GRTBase) |
string | getClassType () const |
string | getLastWarningMessage () const |
string | getLastErrorMessage () const |
string | getLastInfoMessage () const |
GRTBase * | getGRTBasePointer () |
const GRTBase * | getGRTBasePointer () const |
![]() | |
virtual void | notify (const TrainingResult &data) |
![]() | |
virtual void | notify (const TestInstanceResult &data) |
Protected Attributes | |
UINT | numClusters |
MatrixDouble | clusters |
VectorDouble | quantizationDistances |
![]() | |
string | featureExtractionType |
bool | initialized |
bool | featureDataReady |
VectorDouble | featureVector |
![]() | |
bool | trained |
bool | useScaling |
UINT | baseType |
UINT | numInputDimensions |
UINT | numOutputDimensions |
UINT | numTrainingIterationsToConverge |
UINT | minNumEpochs |
UINT | maxNumEpochs |
UINT | validationSetSize |
double | learningRate |
double | minChange |
double | rootMeanSquaredTrainingError |
double | totalSquaredTrainingError |
bool | useValidationSet |
bool | randomiseTrainingOrder |
Random | random |
vector< TrainingResult > | trainingResults |
TrainingResultsObserverManager | trainingResultsObserverManager |
TestResultsObserverManager | testResultsObserverManager |
![]() | |
string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
Static Protected Attributes | |
static RegisterFeatureExtractionModule< KMeansQuantizer > | registerModule |
Additional Inherited Members | |
![]() | |
typedef std::map< string, FeatureExtraction *(*)() > | StringFeatureExtractionMap |
![]() | |
enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
![]() | |
static FeatureExtraction * | createInstanceFromString (string const &featureExtractionType) |
![]() | |
static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
![]() | |
bool | init () |
bool | saveFeatureExtractionSettingsToFile (fstream &file) const |
bool | loadFeatureExtractionSettingsFromFile (fstream &file) |
![]() | |
bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
![]() | |
double | SQR (const double &x) const |
![]() | |
static StringFeatureExtractionMap * | getMap () |
Definition at line 49 of file KMeansQuantizer.h.
GRT::KMeansQuantizer::KMeansQuantizer | ( | const UINT | numClusters = 10 | ) |
Default constructor. Initalizes the KMeansQuantizer, setting the number of input dimensions and the number of clusters to use in the quantization model.
const | UINT numClusters: the number of quantization clusters |
Definition at line 28 of file KMeansQuantizer.cpp.
GRT::KMeansQuantizer::KMeansQuantizer | ( | const KMeansQuantizer & | rhs | ) |
Copy constructor, copies the KMeansQuantizer from the rhs instance to this instance.
const | KMeansQuantizer &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 39 of file KMeansQuantizer.cpp.
|
virtual |
Default Destructor
Definition at line 52 of file KMeansQuantizer.cpp.
|
virtual |
Sets the FeatureExtraction clear function, overwriting the base FeatureExtraction function.
Reimplemented from GRT::FeatureExtraction.
Definition at line 105 of file KMeansQuantizer.cpp.
|
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).
const | VectorDouble &inputVector: the inputVector that should be processed. Must have the same dimensionality as the FeatureExtraction module |
Reimplemented from GRT::FeatureExtraction.
Definition at line 86 of file KMeansQuantizer.cpp.
|
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.
const | FeatureExtraction *featureExtraction: a pointer to another instance of this class, the values of that instance will be cloned to this instance |
Reimplemented from GRT::FeatureExtraction.
Definition at line 68 of file KMeansQuantizer.cpp.
UINT GRT::KMeansQuantizer::getNumClusters | ( | ) | const |
Gets the number of clusters in the quantizer.
Definition at line 298 of file KMeansQuantizer.cpp.
|
inline |
Gets the quantization distances from the most recent quantization.
Definition at line 213 of file KMeansQuantizer.h.
|
inline |
Gets the quantization model. This will be a [K N] matrix containing the quantization clusters, where K is the number of clusters and N is the number of dimensions in the input data.
Definition at line 223 of file KMeansQuantizer.h.
|
inline |
Gets the most recent quantized value. This can also be accessed by using the first element in the featureVector.
Definition at line 206 of file KMeansQuantizer.h.
|
inline |
Gets if the quantization model has been trained.
Definition at line 192 of file KMeansQuantizer.h.
|
virtual |
This loads the feature extraction settings from a file. This overrides the loadSettingsFromFile function in the FeatureExtraction base class.
fstream | &file: a reference to the file to load the settings from |
Reimplemented from GRT::FeatureExtraction.
Definition at line 150 of file KMeansQuantizer.cpp.
KMeansQuantizer & GRT::KMeansQuantizer::operator= | ( | const KMeansQuantizer & | rhs | ) |
Sets the equals operator, copies the data from the rhs instance to this instance.
const | KMeansQuantizer &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 55 of file KMeansQuantizer.cpp.
UINT GRT::KMeansQuantizer::quantize | ( | double | inputValue | ) |
Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.
double | inputValue: the value you want to quantize |
Definition at line 260 of file KMeansQuantizer.cpp.
UINT GRT::KMeansQuantizer::quantize | ( | const VectorDouble & | inputVector | ) |
Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.
const | VectorDouble &inputVector: the vector you want to quantize |
Definition at line 264 of file KMeansQuantizer.cpp.
|
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.
Reimplemented from GRT::FeatureExtraction.
Definition at line 94 of file KMeansQuantizer.cpp.
|
virtual |
This saves the feature extraction settings to a file. This overrides the saveSettingsToFile function in the FeatureExtraction base class. You should add your own custom code to this function to define how your feature extraction module is saved to a file.
fstream | &file: a reference to the file to save the settings to |
Reimplemented from GRT::FeatureExtraction.
Definition at line 116 of file KMeansQuantizer.cpp.
bool GRT::KMeansQuantizer::setNumClusters | ( | const UINT | numClusters | ) |
Sets the number of clusters in the quantizer. This will clear any previously trained model.
Definition at line 302 of file KMeansQuantizer.cpp.
|
virtual |
Trains the quantization model using the training dataset.
ClassificationData | &trainingData: the training dataset that will be used to train the quantizer |
Reimplemented from GRT::MLBase.
Definition at line 211 of file KMeansQuantizer.cpp.
|
virtual |
Trains the quantization model using the training dataset.
TimeSeriesClassificationData | &trainingData: the training dataset that will be used to train the quantizer |
Reimplemented from GRT::MLBase.
Definition at line 216 of file KMeansQuantizer.cpp.
|
virtual |
Trains the quantization model using the training dataset.
TimeSeriesClassificationDataStream | &trainingData: the training dataset that will be used to train the quantizer |
Reimplemented from GRT::MLBase.
Definition at line 221 of file KMeansQuantizer.cpp.
|
virtual |
Trains the quantization model using the training dataset.
UnlabelledData | &trainingData: the training dataset that will be used to train the quantizer |
Reimplemented from GRT::MLBase.
Definition at line 226 of file KMeansQuantizer.cpp.
|
virtual |
Trains the quantization model using the training dataset.
MatrixDouble | &trainingData: the training dataset that will be used to train the quantizer |
Reimplemented from GRT::MLBase.
Definition at line 231 of file KMeansQuantizer.cpp.