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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 | |
RBMQuantizer (const UINT numClusters=10) | |
RBMQuantizer (const RBMQuantizer &rhs) | |
virtual | ~RBMQuantizer () |
RBMQuantizer & | operator= (const RBMQuantizer &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 |
BernoulliRBM | getBernoulliRBM () 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 |
BernoulliRBM | rbm |
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< RBMQuantizer > | 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 RBMQuantizer.h.
GRT::RBMQuantizer::RBMQuantizer | ( | const UINT | numClusters = 10 | ) |
Default constructor. Initalizes the RBMQuantizer, setting the number of clusters to use in the quantization model.
const | UINT numClusters: the number of quantization clusters |
Definition at line 28 of file RBMQuantizer.cpp.
GRT::RBMQuantizer::RBMQuantizer | ( | const RBMQuantizer & | rhs | ) |
Copy constructor, copies the RBMQuantizer from the rhs instance to this instance.
const | RBMQuantizer &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 38 of file RBMQuantizer.cpp.
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Default Destructor
Definition at line 50 of file RBMQuantizer.cpp.
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Sets the FeatureExtraction clear function, overwriting the base FeatureExtraction function.
Reimplemented from GRT::FeatureExtraction.
Definition at line 103 of file RBMQuantizer.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). This is where you should add your main feature extraction code.
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 82 of file RBMQuantizer.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 65 of file RBMQuantizer.cpp.
BernoulliRBM GRT::RBMQuantizer::getBernoulliRBM | ( | ) | const |
Gets the RBM model.
Definition at line 332 of file RBMQuantizer.cpp.
UINT GRT::RBMQuantizer::getNumClusters | ( | ) | const |
Gets the number of clusters in the quantizer.
Definition at line 320 of file RBMQuantizer.cpp.
VectorDouble GRT::RBMQuantizer::getQuantizationDistances | ( | ) | const |
Gets the quantization distances from the most recent quantization.
Definition at line 328 of file RBMQuantizer.cpp.
UINT GRT::RBMQuantizer::getQuantizedValue | ( | ) | const |
Gets the most recent quantized value. This can also be accessed by using the first element in the featureVector.
Definition at line 324 of file RBMQuantizer.cpp.
bool GRT::RBMQuantizer::getQuantizerTrained | ( | ) | const |
Gets if the quantization model has been trained.
Definition at line 316 of file RBMQuantizer.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 128 of file RBMQuantizer.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 172 of file RBMQuantizer.cpp.
RBMQuantizer & GRT::RBMQuantizer::operator= | ( | const RBMQuantizer & | rhs | ) |
Sets the equals operator, copies the data from the rhs instance to this instance.
const | RBMQuantizer &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 53 of file RBMQuantizer.cpp.
UINT GRT::RBMQuantizer::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 277 of file RBMQuantizer.cpp.
UINT GRT::RBMQuantizer::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 281 of file RBMQuantizer.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 90 of file RBMQuantizer.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 114 of file RBMQuantizer.cpp.
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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 143 of file RBMQuantizer.cpp.
bool GRT::RBMQuantizer::setNumClusters | ( | const UINT | numClusters | ) |
Sets the number of clusters in the quantizer. This will clear any previously trained model.
Definition at line 336 of file RBMQuantizer.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 224 of file RBMQuantizer.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 229 of file RBMQuantizer.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 234 of file RBMQuantizer.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 239 of file RBMQuantizer.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 244 of file RBMQuantizer.cpp.