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GestureRecognitionToolkit
Version: 0.2.5
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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GestureRecognitionToolkit
Version: 0.2.5
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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| ►NLIBSVM | |
| CAdaBoost | |
| CAdaBoostClassModel | |
| CANBC | |
| CANBC_Model | |
| CAngleMagnitude | |
| CBAG | |
| ►CBernoulliRBM | |
| CCholesky | |
| CCircularBuffer | |
| CClassificationData | |
| CClassificationDataStream | |
| CClassificationResult | |
| CClassificationSample | This class stores the class label and raw data for a single labelled classification sample |
| CClassifier | This is the main base class that all GRT Classification algorithms should inherit from |
| CClassLabelAndTimer | |
| CClassLabelChangeFilter | |
| CClassLabelFilter | |
| CClassLabelTimeoutFilter | |
| CClassTracker | |
| CClusterer | |
| CClusterInfo | |
| CClusterLevel | |
| CClusterTree | |
| CClusterTreeNode | |
| CCommandLineParser | |
| CContext | |
| CContinuousHiddenMarkovModel | |
| CDeadZone | Sets any values in the input signal that fall within the dead-zone region to zero. Any values outside of the dead-zone region will be offset by the dead zone's lower limit and upper limit |
| CDebugLog | |
| CDebugLogMessage | |
| CDecisionStump | |
| CDecisionTree | |
| CDecisionTreeClusterNode | |
| CDecisionTreeNode | |
| CDecisionTreeThresholdNode | |
| CDecisionTreeTripleFeatureNode | This class implements a DecisionTreeTripleFeatureNode, which is a specific type of node used for a DecisionTree |
| CDerivative | Computes either the first or second order derivative of the input signal |
| CDict | This class implements a flexible dictionary that supports multiple data types. Elements in the dictionary consist of key-value pairs, where keys are std::strings and values can be any data type, as they are stored as a GRT::DynamicType |
| CDiscreteHiddenMarkovModel | |
| CDoubleMovingAverageFilter | The class implements a Float moving average filter |
| CDTW | |
| CDTWTemplate | |
| CDynamicType | |
| CEigenvalueDecomposition | |
| CEnvelopeExtractor | |
| CErrorLog | |
| CErrorLogMessage | |
| CEvolutionaryAlgorithm | This class implements a template based EvolutionaryAlgorithm |
| CException | |
| CFastFourierTransform | |
| CFeatureExtraction | |
| CFFT | |
| CFFTFeatures | |
| CFileParser | |
| CFiniteStateMachine | |
| CFIRFilter | This class implements a Finite Impulse Response (FIR) Filter. It can support a low pass filter, high pass filter, or band pass filter |
| CFSMParticle | |
| CFSMParticleFilter | |
| CGate | |
| CGaussianMixtureModels | |
| CGaussNeuron | |
| CGestureRecognitionPipeline | |
| CGMM | |
| CGridSearch | |
| CGridSearchParam | |
| CGridSearchRange | This class implements a basic grid search algorithm |
| Cgrt_numeric_limits | |
| CGRTBase | |
| CGuassModel | |
| CHierarchicalClustering | |
| CHighPassFilter | This class implements a High Pass Filter |
| CHMM | This class acts as the main interface for using a Hidden Markov Model |
| CHMMTrainingObject | |
| CIndexDist | |
| CIndexedDouble | |
| CIndividual | |
| CInfoLog | |
| CInfoLogMessage | |
| CKMeans | |
| CKMeansFeatures | |
| CKMeansQuantizer | |
| CKNN | |
| CLeakyIntegrator | Computes the following signal: y = y*z + x, where x is the input, y is the output and z is the leakrate |
| CLinearLeastSquares | |
| CLinearRegression | |
| CLog | Base class for all GRT logging functionality |
| CLogisticRegression | |
| CLowPassFilter | The class implements a low pass filter, this is based on an Exponential moving average filter: https://en.wikipedia.org/wiki/Exponential_smoothing |
| CLUDecomposition | |
| CMatrix | |
| CMatrixFloat | |
| CMeanShift | |
| CMedianFilter | The MedianFilter implements a simple median filter: https://en.wikipedia.org/wiki/Median_filter |
| CMetrics | |
| CMinDist | |
| CMinDistModel | |
| CMinMax | |
| CMixtureModel | |
| CMLBase | This is the main base class that all GRT machine learning algorithms should inherit from |
| CMLP | |
| CMovementDetector | |
| CMovementIndex | |
| CMovementTrajectoryFeatures | |
| CMovingAverageFilter | The MovingAverageFilter implements a low pass moving average filter |
| CMultidimensionalRegression | |
| CNeuron | |
| CNode | |
| CObserver | |
| CObserverManager | |
| CParticle | |
| CParticleClassifier | |
| CParticleClassifierGestureTemplate | |
| CParticleClassifierParticleFilter | |
| CParticleFilter | |
| CParticleSwarmOptimization | |
| CPeakDetection | |
| CPeakInfo | |
| CPostProcessing | This is the main base class that all GRT PostProcessing algorithms should inherit from. A large number of the functions in this class are virtual and simply return false as these functions must be overwridden by the inheriting class |
| CPreProcessing | |
| CPrincipalComponentAnalysis | This class runs the Principal Component Analysis (PCA) algorithm, a dimensionality reduction algorithm that projects an [M N] matrix (where M==samples and N==dimensions) onto a new K dimensional subspace, where K is normally much less than N |
| CPSOParticle | |
| CRadialBasisFunction | |
| CRandom | This file contains the Random class, a useful wrapper for generating cross platform random functions. This includes functions for uniform distributions (both integer and Float) and Gaussian distributions |
| CRandomForests | |
| CRangeTracker | |
| CRBMQuantizer | |
| CRegisterClassifierModule | This class provides an interface for classes to register themselves with the classifier base class, this enables Classifier algorithms to be automatically be created from just a string, e.g.: Classifier *knn = create( "KNN" ); |
| CRegisterClustererModule | This class provides an interface for classes to register themselves with the clusterer base class, this enables Cluterer algorithms to be automatically be created from just a string, e.g.: Clusterer *kmeans = create( "KMeans" ); |
| CRegisterContextModule | This class provides an interface for classes to register themselves with the Context base class, this enables Context algorithms to be automatically be created from just a string, e.g.: Context *gate = create( "Gate" ); |
| CRegisterFeatureExtractionModule | |
| CRegisterNode | |
| CRegisterPostProcessingModule | |
| CRegisterPreProcessingModule | This class provides an interface for classes to register themselves with the preprocessing base class, this enables PreProcessing algorithms to be automatically be created from just a string, e.g.: PreProcessing *lpf = create( "LowPassFilter" ); |
| CRegisterRegressifierModule | This class provides an interface for classes to register themselves with the regressifier base class, this enables Regression algorithms to be automatically be created from just a string, e.g.: Regressifier *mlp = create( "MLP" ); |
| CRegisterWeakClassifierModule | |
| CRegressifier | |
| CRegressionData | |
| CRegressionSample | |
| CRegressionTree | This class implements a basic Regression Tree |
| CRegressionTreeNode | |
| CRMSFilter | The RMSFilter implements a root mean squared (RMS) filter |
| CSavitzkyGolayFilter | This implements a Savitzky-Golay filter. This code is based on the Savitzky Golay filter code from Numerical Recipes 3 |
| CSelfOrganizingMap | |
| CSoftmax | |
| CSoftmaxModel | |
| CSOMQuantizer | |
| CSVD | |
| CSVM | |
| CSwipeDetector | This class implements a basic swipe detection classification algorithm |
| CTestingLog | |
| CTestingLogMessage | |
| CTestInstanceResult | |
| CTestResult | |
| CTestResultsObserverManager | |
| CThreadPool | |
| CThresholdCrossingDetector | |
| CTimeDomainFeatures | |
| CTimer | |
| CTimeseriesBuffer | |
| CTimeSeriesClassificationData | |
| CTimeSeriesClassificationSample | |
| CTimeSeriesClassificationSampleTrimmer | |
| CTimeSeriesPositionTracker | |
| CTimeStamp | |
| CTrainingDataRecordingTimer | |
| CTrainingLog | |
| CTrainingLogMessage | |
| CTrainingResult | |
| CTrainingResultsObserverManager | |
| CTree | |
| CUnlabelledData | |
| CUtil | |
| CVector | |
| CVectorFloat | |
| CWarningLog | |
| CWarningLogMessage | |
| CWeakClassifier | |
| CWeightedAverageFilter | The WeightedAverageFilter implements a weighted average filter that gives a larger weight to more recent samples, and a smaller weight to older samples |
| CZeroCrossingCounter |
1.8.11 
