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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 | |
Softmax (const bool useScaling=false, const double learningRate=0.1, const double minChange=1.0e-10, const UINT maxNumEpochs=1000) | |
Softmax (const Softmax &rhs) | |
virtual | ~Softmax (void) |
Softmax & | operator= (const Softmax &rhs) |
virtual bool | deepCopyFrom (const Classifier *classifier) |
virtual bool | train_ (ClassificationData &trainingData) |
virtual bool | predict_ (VectorDouble &inputVector) |
virtual bool | clear () |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
vector< SoftmaxModel > | getModels () const |
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Classifier (void) | |
virtual | ~Classifier (void) |
bool | copyBaseVariables (const Classifier *classifier) |
virtual bool | reset () |
string | getClassifierType () const |
bool | getSupportsNullRejection () const |
bool | getNullRejectionEnabled () const |
double | getNullRejectionCoeff () const |
double | getMaximumLikelihood () const |
double | getBestDistance () const |
double | getPhase () const |
virtual UINT | getNumClasses () const |
UINT | getClassLabelIndexValue (UINT classLabel) const |
UINT | getPredictedClassLabel () const |
VectorDouble | getClassLikelihoods () const |
VectorDouble | getClassDistances () const |
VectorDouble | getNullRejectionThresholds () const |
vector< UINT > | getClassLabels () const |
vector< MinMax > | getRanges () const |
bool | enableNullRejection (bool useNullRejection) |
virtual bool | setNullRejectionCoeff (double nullRejectionCoeff) |
virtual bool | setNullRejectionThresholds (VectorDouble newRejectionThresholds) |
virtual bool | recomputeNullRejectionThresholds () |
bool | getTimeseriesCompatible () const |
Classifier * | createNewInstance () const |
Classifier * | deepCopy () const |
const Classifier * | getClassifierPointer () const |
const Classifier & | getBaseClassifier () 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_ (TimeSeriesClassificationData &trainingData) |
virtual bool | train (TimeSeriesClassificationDataStream trainingData) |
virtual bool | train_ (TimeSeriesClassificationDataStream &trainingData) |
virtual bool | train (UnlabelledData trainingData) |
virtual bool | train_ (UnlabelledData &trainingData) |
virtual bool | train (MatrixDouble data) |
virtual bool | train_ (MatrixDouble &data) |
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 |
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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 Member Functions | |
bool | trainSoftmaxModel (UINT classLabel, SoftmaxModel &model, ClassificationData &data) |
bool | loadLegacyModelFromFile (fstream &file) |
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bool | saveBaseSettingsToFile (fstream &file) const |
bool | loadBaseSettingsFromFile (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 |
Protected Attributes | |
vector< SoftmaxModel > | models |
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string | classifierType |
bool | supportsNullRejection |
bool | useNullRejection |
UINT | numClasses |
UINT | predictedClassLabel |
UINT | classifierMode |
double | nullRejectionCoeff |
double | maxLikelihood |
double | bestDistance |
double | phase |
VectorDouble | classLikelihoods |
VectorDouble | classDistances |
VectorDouble | nullRejectionThresholds |
vector< UINT > | classLabels |
vector< MinMax > | ranges |
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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 RegisterClassifierModule< Softmax > | registerModule |
Additional Inherited Members | |
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typedef std::map< string, Classifier *(*)() > | StringClassifierMap |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
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static Classifier * | createInstanceFromString (string const &classifierType) |
static vector< string > | getRegisteredClassifiers () |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
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enum | ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE } |
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static StringClassifierMap * | getMap () |
GRT::Softmax::Softmax | ( | const bool | useScaling = false , |
const double | learningRate = 0.1 , |
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const double | minChange = 1.0e-10 , |
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const UINT | maxNumEpochs = 1000 |
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Default Constructor
const | bool useScaling: sets if the training and real-time data should be scaled between [0 1]. Default value = false |
double | learningRate: the learningRate value used during the training phase. Default = 0.1 |
double | minChange: sets the minimum change that must be achieved between two training epochs for the training to continue. Default = 1.0e-10 |
UINT | maxNumEpochs: sets the maximum number of iterations that can be run during the training phase. Default = 1000 |
Definition at line 28 of file Softmax.cpp.
GRT::Softmax::Softmax | ( | const Softmax & | rhs | ) |
Defines the copy constructor.
const | Softmax &rhs: the instance from which all the data will be copied into this instance |
Definition at line 43 of file Softmax.cpp.
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virtual |
Default Destructor
Definition at line 54 of file Softmax.cpp.
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This overrides the clear function in the Classifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.
Reimplemented from GRT::Classifier.
Definition at line 262 of file Softmax.cpp.
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This is required for the Gesture Recognition Pipeline for when the pipeline.setClassifier(...) method is called. It clones the data from the Base Class Classifier pointer (which should be pointing to an Softmax instance) into this instance
Classifier | *classifier: a pointer to the Classifier Base Class, this should be pointing to another Softmax instance |
Reimplemented from GRT::Classifier.
Definition at line 71 of file Softmax.cpp.
vector< SoftmaxModel > GRT::Softmax::getModels | ( | ) | const |
Get the softmax models for each class. The Softmax class must be trained first.
Definition at line 388 of file Softmax.cpp.
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Read the ranges if needed
Definition at line 392 of file Softmax.cpp.
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This loads a trained Softmax model from a file. This overrides the loadModelFromFile function in the Classifier base class.
fstream | &file: a reference to the file the Softmax model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 305 of file Softmax.cpp.
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This predicts the class of the inputVector. This overrides the predict function in the Classifier base class.
VectorDouble | inputVector: the input vector to classify |
Reimplemented from GRT::MLBase.
Definition at line 133 of file Softmax.cpp.
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This saves the trained Softmax model to a file. This overrides the saveModelToFile function in the Classifier base class.
fstream | &file: a reference to the file the Softmax model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 273 of file Softmax.cpp.
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This trains the Softmax model, using the labelled classification data. This overrides the train function in the Classifier base class.
ClassificationData | trainingData: a reference to the training data |
Reimplemented from GRT::MLBase.
Definition at line 89 of file Softmax.cpp.