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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 | |
ContinuousHiddenMarkovModel (const UINT downsampleFactor=5, const UINT delta=1, const bool autoEstimateSigma=true, const double sigma=10.0) | |
ContinuousHiddenMarkovModel (const ContinuousHiddenMarkovModel &rhs) | |
ContinuousHiddenMarkovModel & | operator= (const ContinuousHiddenMarkovModel &rhs) |
virtual bool | predict_ (VectorDouble &x) |
virtual bool | predict_ (MatrixDouble &obs) |
virtual bool | train_ (TimeSeriesClassificationSample &trainingData) |
virtual bool | reset () |
virtual bool | clear () |
virtual bool | saveModelToFile (fstream &file) const |
virtual bool | loadModelFromFile (fstream &file) |
virtual bool | print () const |
UINT | getNumStates () const |
UINT | getClassLabel () const |
double | getLoglikelihood () const |
double | getPhase () const |
vector< UINT > | getEstimatedStates () const |
MatrixDouble | getAlpha () const |
bool | setDownsampleFactor (const UINT downsampleFactor) |
bool | setModelType (const UINT modelType) |
bool | setDelta (const UINT delta) |
bool | setSigma (const double sigma) |
bool | setAutoEstimateSigma (const bool autoEstimateSigma) |
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MLBase (void) | |
virtual | ~MLBase (void) |
bool | copyMLBaseVariables (const MLBase *mlBase) |
virtual bool | train (ClassificationData trainingData) |
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 | map (VectorDouble inputVector) |
virtual bool | map_ (VectorDouble &inputVector) |
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 | |
double | gauss (const MatrixDouble &x, const MatrixDouble &y, const MatrixDouble &sigma, const unsigned int i, const unsigned int j, const unsigned int N) |
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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 | |
UINT | downsampleFactor |
UINT | numStates |
The number of states for this model. | |
UINT | classLabel |
The class label associated with this model. | |
UINT | timeseriesLength |
The length of the training timeseries. | |
bool | autoEstimateSigma |
double | sigma |
double | phase |
MatrixDouble | a |
The transitions probability matrix. | |
MatrixDouble | b |
The emissions probability matrix. | |
VectorDouble | pi |
The state start probability vector. | |
MatrixDouble | alpha |
VectorDouble | c |
CircularBuffer< VectorDouble > | observationSequence |
A buffer to store data for realtime prediction. | |
MatrixDouble | obsSequence |
vector< UINT > | estimatedStates |
The estimated states for prediction. | |
MatrixDouble | sigmaStates |
The sigma value for each state. | |
UINT | modelType |
The model type (LEFTRIGHT, or ERGODIC) | |
UINT | delta |
The number of states a model can move to in a LEFTRIGHT model. | |
double | loglikelihood |
The log likelihood of an observation sequence given the modal, calculated by the forward method. | |
double | cThreshold |
The classification threshold for this model. | |
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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 |
Additional Inherited Members | |
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enum | BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER } |
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static string | getGRTVersion (bool returnRevision=true) |
static string | getGRTRevison () |
Definition at line 40 of file ContinuousHiddenMarkovModel.h.
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virtual |
This is the main clear interface for all the GRT machine learning algorithms. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.
Reimplemented from GRT::MLBase.
Definition at line 384 of file ContinuousHiddenMarkovModel.cpp.
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virtual |
This loads a trained model from a file.
fstream | &file: a reference to the file the model will be loaded from |
Reimplemented from GRT::MLBase.
Definition at line 579 of file ContinuousHiddenMarkovModel.cpp.
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virtual |
This is the main prediction interface for all the GRT machine learning algorithms. This should be overwritten by the derived class.
VectorDouble | &inputVector: a reference to the input vector for prediction |
Reimplemented from GRT::MLBase.
Definition at line 109 of file ContinuousHiddenMarkovModel.cpp.
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virtual |
This is the prediction interface for time series data. This should be overwritten by the derived class.
MatrixDouble | inputMatrix: a reference to the new input matrix for prediction |
Reimplemented from GRT::MLBase.
Definition at line 134 of file ContinuousHiddenMarkovModel.cpp.
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virtual |
This is the main print interface for all the GRT machine learning algorithms. This should be overwritten by the derived class. It will print the model and settings to the display log.
Reimplemented from GRT::MLBase.
Definition at line 405 of file ContinuousHiddenMarkovModel.cpp.
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virtual |
This is the main reset interface for all the GRT machine learning algorithms. It should be used to reset the model (i.e. set all values back to default settings). If you want to completely clear the model (i.e. clear any learned weights or values) then you should use the clear function.
Reimplemented from GRT::MLBase.
Definition at line 370 of file ContinuousHiddenMarkovModel.cpp.
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virtual |
This saves the trained model to a file.
fstream | &file: a reference to the file the model will be saved to |
Reimplemented from GRT::MLBase.
Definition at line 514 of file ContinuousHiddenMarkovModel.cpp.
bool ContinuousHiddenMarkovModel::setDelta | ( | const UINT | delta | ) |
This function sets the delta parameter in each HMM.
The delta value controls how many states a model can transition to if the LEFTRIGHT model type is used.
The parameter must be greater than zero.
This will clear any trained model.
const | UINT delta: the delta parameter used for each CHMM |
Definition at line 474 of file ContinuousHiddenMarkovModel.cpp.
bool ContinuousHiddenMarkovModel::setModelType | ( | const UINT | modelType | ) |
This function sets the modelType used for each HMM. This should be one of the HMM modelType enums.
This will clear any trained model.
const | UINT modelType: the modelType in each HMM |
Definition at line 464 of file ContinuousHiddenMarkovModel.cpp.