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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 | |
KMeansFeatures (const vector< UINT > numClustersPerLayer=vector< UINT >(1, 100), const double alpha=0.2, const bool useScaling=true) | |
KMeansFeatures (const KMeansFeatures &rhs) | |
virtual | ~KMeansFeatures () |
KMeansFeatures & | operator= (const KMeansFeatures &rhs) |
virtual bool | deepCopyFrom (const FeatureExtraction *featureExtraction) |
virtual bool | computeFeatures (const VectorDouble &inputVector) |
virtual bool | reset () |
virtual bool | saveModelToFile (string filename) const |
virtual bool | loadModelFromFile (string filename) |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | train_ (TimeSeriesClassificationData &trainingData) |
virtual bool | train_ (TimeSeriesClassificationDataStream &trainingData) |
virtual bool | train_ (UnlabelledData &trainingData) |
virtual bool | train_ (MatrixDouble &trainingData) |
bool | computeFeatures (VectorDouble &inputVector, VectorDouble &outputVector) |
bool | init (const vector< UINT > numClustersPerLayer) |
bool | projectDataThroughLayer (const VectorDouble &input, VectorDouble &output, const UINT layer) |
UINT | getNumLayers () const |
UINT | getLayerSize (const UINT layerIndex) const |
vector< MatrixDouble > | getClusters () const |
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FeatureExtraction () | |
virtual | ~FeatureExtraction () |
bool | copyBaseVariables (const FeatureExtraction *featureExtractionModule) |
virtual bool | clear () |
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 | |
double | alpha |
vector< UINT > | numClustersPerLayer |
vector< MinMax > | ranges |
vector< MatrixDouble > | clusters |
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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< KMeansFeatures > | 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 41 of file KMeansFeatures.h.
GRT::KMeansFeatures::KMeansFeatures | ( | const vector< UINT > | numClustersPerLayer = vector< UINT >(1,100) , |
const double | alpha = 0.2 , |
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const bool | useScaling = true |
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Default constructor. Initalizes the KMeansFeatures, setting the number of input dimensions and the number of clusters to use in the quantization model.
UINT | numDimensions: the number of dimensions in the input data |
UINT | numClusters: the number of quantization clusters |
Definition at line 28 of file KMeansFeatures.cpp.
GRT::KMeansFeatures::KMeansFeatures | ( | const KMeansFeatures & | rhs | ) |
Copy constructor, copies the KMeansQuantizer from the rhs instance to this instance.
const | KMeansFeatures &rhs: another instance of this class from which the data will be copied to this instance |
Definition at line 46 of file KMeansFeatures.cpp.
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Default Destructor
Definition at line 59 of file KMeansFeatures.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 92 of file KMeansFeatures.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 74 of file KMeansFeatures.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 141 of file KMeansFeatures.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 206 of file KMeansFeatures.cpp.
KMeansFeatures & GRT::KMeansFeatures::operator= | ( | const KMeansFeatures & | 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 63 of file KMeansFeatures.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. You should add any custom reset code to this function to define how your feature extraction module should be reset.
Reimplemented from GRT::FeatureExtraction.
Definition at line 123 of file KMeansFeatures.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 127 of file KMeansFeatures.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 156 of file KMeansFeatures.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 330 of file KMeansFeatures.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 335 of file KMeansFeatures.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 340 of file KMeansFeatures.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 345 of file KMeansFeatures.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 350 of file KMeansFeatures.cpp.