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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.
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Public Member Functions | |
SOMQuantizer (const UINT numClusters=10) | |
SOMQuantizer (const SOMQuantizer &rhs) | |
virtual | ~SOMQuantizer () |
SOMQuantizer & | operator= (const SOMQuantizer &rhs) |
virtual bool | deepCopyFrom (const FeatureExtraction *featureExtraction) |
virtual bool | computeFeatures (const VectorDouble &inputVector) |
virtual bool | reset () |
virtual bool | clear () |
virtual bool | saveModelToFile (string filename) const |
virtual bool | loadModelFromFile (string filename) |
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 (const double inputValue) |
UINT | quantize (const VectorDouble &inputVector) |
bool | getQuantizerTrained () const |
UINT | getNumClusters () const |
UINT | getQuantizedValue () const |
VectorDouble | getQuantizationDistances () const |
SelfOrganizingMap | getSelfOrganizingMap () const |
bool | setNumClusters (const UINT numClusters) |
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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 |
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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 | 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 |
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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 |
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virtual void | notify (const TrainingResult &data) |
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virtual void | notify (const TestInstanceResult &data) |
Protected Attributes | |
UINT | numClusters |
SelfOrganizingMap | som |
VectorDouble | quantizationDistances |
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string | featureExtractionType |
bool | initialized |
bool | featureDataReady |
VectorDouble | featureVector |
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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 |
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string | classType |
DebugLog | debugLog |
ErrorLog | errorLog |
InfoLog | infoLog |
TrainingLog | trainingLog |
TestingLog | testingLog |
WarningLog | warningLog |
Static Protected Attributes | |
static RegisterFeatureExtractionModule< SOMQuantizer > | registerModule |
Additional Inherited Members | |
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typedef std::map< string, FeatureExtraction *(*)() > | StringFeatureExtractionMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
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static FeatureExtraction * | createInstanceFromString (string const &featureExtractionType) |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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bool | init () |
bool | saveFeatureExtractionSettingsToFile (fstream &file) const |
bool | loadFeatureExtractionSettingsFromFile (fstream &file) |
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bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (fstream &file) |
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double | SQR (const double &x) const |
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static StringFeatureExtractionMap * | getMap () |
Definition at line 49 of file SOMQuantizer.h.
GRT::SOMQuantizer::SOMQuantizer | ( | const UINT | numClusters = 10 | ) |
Default constructor. Initalizes the SOMQuantizer, 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 SOMQuantizer.cpp.
GRT::SOMQuantizer::SOMQuantizer | ( | const SOMQuantizer & | rhs | ) |
Copy constructor, copies the SOMQuantizer from the rhs instance to this instance.
const | SOMQuantizer &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 39 of file SOMQuantizer.cpp.
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Default Destructor
Definition at line 51 of file SOMQuantizer.cpp.
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Sets the FeatureExtraction clear function, overwriting the base FeatureExtraction function.
Reimplemented from GRT::FeatureExtraction.
Definition at line 107 of file SOMQuantizer.cpp.
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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 SOMQuantizer.cpp.
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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 SOMQuantizer.cpp.
UINT GRT::SOMQuantizer::getNumClusters | ( | ) | const |
Gets the number of clusters in the quantizer.
Definition at line 332 of file SOMQuantizer.cpp.
VectorDouble GRT::SOMQuantizer::getQuantizationDistances | ( | ) | const |
Gets the quantization distances from the most recent quantization.
Definition at line 340 of file SOMQuantizer.cpp.
UINT GRT::SOMQuantizer::getQuantizedValue | ( | ) | const |
Gets the most recent quantized value. This can also be accessed by using the first element in the featureVector.
Definition at line 336 of file SOMQuantizer.cpp.
bool GRT::SOMQuantizer::getQuantizerTrained | ( | ) | const |
Gets if the quantization model has been trained.
Definition at line 328 of file SOMQuantizer.cpp.
SelfOrganizingMap GRT::SOMQuantizer::getSelfOrganizingMap | ( | ) | const |
Gets the som model.
Definition at line 344 of file SOMQuantizer.cpp.
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This saves the feature extraction settings to a file.
fstream | &file: a reference to the file to save the settings to |
Reimplemented from GRT::MLBase.
Definition at line 132 of file SOMQuantizer.cpp.
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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 177 of file SOMQuantizer.cpp.
SOMQuantizer & GRT::SOMQuantizer::operator= | ( | const SOMQuantizer & | rhs | ) |
Sets the equals operator, copies the data from the rhs instance to this instance.
const | SOMQuantizer &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 55 of file SOMQuantizer.cpp.
UINT GRT::SOMQuantizer::quantize | ( | const double | inputValue | ) |
Quantizes the input value using the quantization model. The quantization model must be trained first before you call this function.
const | double inputValue: the value you want to quantize |
Definition at line 289 of file SOMQuantizer.cpp.
UINT GRT::SOMQuantizer::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 293 of file SOMQuantizer.cpp.
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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 SOMQuantizer.cpp.
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This saves the feature extraction settings to a file.
const | string filename: the filename to save the settings to |
Reimplemented from GRT::MLBase.
Definition at line 118 of file SOMQuantizer.cpp.
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This saves the feature extraction settings to a file.
fstream | &file: a reference to the file to save the settings to |
Reimplemented from GRT::FeatureExtraction.
Definition at line 147 of file SOMQuantizer.cpp.
bool GRT::SOMQuantizer::setNumClusters | ( | const UINT | numClusters | ) |
Sets the number of clusters in the quantizer. This will clear any previously trained model.
Definition at line 348 of file SOMQuantizer.cpp.
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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 236 of file SOMQuantizer.cpp.
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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 241 of file SOMQuantizer.cpp.
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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 246 of file SOMQuantizer.cpp.
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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 251 of file SOMQuantizer.cpp.
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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 256 of file SOMQuantizer.cpp.