CAdaBoostClassModel | |
CANBC_Model | |
CAngleMagnitude | |
CBernoulliRBM::BatchIndexs | |
CLIBSVM::Cache | |
CCholesky | |
CCircularBuffer< T > | |
CCircularBuffer< Float > | |
CCircularBuffer< UINT > | |
CCircularBuffer< unsigned int > | |
CCircularBuffer< VectorFloat > | |
CClassificationDataStream | |
CClassificationResult | |
CClassificationSample | This class stores the class label and raw data for a single labelled classification sample |
CClassLabelAndTimer | |
CClassTracker | |
CClusterInfo | |
CClusterLevel | |
CCommandLineParser | |
CDebugLogMessage | |
CLIBSVM::decision_function | |
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 |
CDTWTemplate | |
CDynamicType | |
CEigenvalueDecomposition | |
CErrorLogMessage | |
►Cexception | |
CException | |
CFileParser | |
CGaussNeuron | |
CGridSearchParam< T > | |
CGridSearchRange< T > | This class implements a basic grid search algorithm |
Cgrt_numeric_limits< T > | |
►CGRTBase | |
CClassificationData | |
CFastFourierTransform | |
►CMLBase | This is the main base class that all GRT machine learning algorithms should inherit from |
CBernoulliRBM | |
►CClassifier | This is the main base class that all GRT Classification algorithms should inherit from |
CAdaBoost | |
CANBC | |
CBAG | |
CDecisionTree | |
CDTW | |
CFiniteStateMachine | |
CGMM | |
CHMM | This class acts as the main interface for using a Hidden Markov Model |
CKNN | |
CMinDist | |
CParticleClassifier | |
CRandomForests | |
CRegisterClassifierModule< T > | 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" ); |
CRegisterClassifierModule< AdaBoost > | |
CRegisterClassifierModule< ANBC > | |
CRegisterClassifierModule< BAG > | |
CRegisterClassifierModule< DecisionTree > | |
CRegisterClassifierModule< DTW > | |
CRegisterClassifierModule< FiniteStateMachine > | |
CRegisterClassifierModule< GMM > | |
CRegisterClassifierModule< HMM > | |
CRegisterClassifierModule< KNN > | |
CRegisterClassifierModule< MinDist > | |
CRegisterClassifierModule< ParticleClassifier > | |
CRegisterClassifierModule< RandomForests > | |
CRegisterClassifierModule< Softmax > | |
CRegisterClassifierModule< SVM > | |
CRegisterClassifierModule< SwipeDetector > | |
CSoftmax | |
CSVM | |
CSwipeDetector | This class implements a basic swipe detection classification algorithm |
►CClusterer | |
CClusterTree | |
CGaussianMixtureModels | |
CHierarchicalClustering | |
CKMeans | |
CRegisterClustererModule< T > | 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" ); |
CRegisterClustererModule< ClusterTree > | |
CRegisterClustererModule< GaussianMixtureModels > | |
CRegisterClustererModule< HierarchicalClustering > | |
CRegisterClustererModule< KMeans > | |
CRegisterClustererModule< SelfOrganizingMap > | |
CSelfOrganizingMap | |
►CContext | |
CGate | |
CRegisterContextModule< T > | 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" ); |
CRegisterContextModule< Gate > | |
CContinuousHiddenMarkovModel | |
CDiscreteHiddenMarkovModel | |
CEvolutionaryAlgorithm< INDIVIDUAL > | This class implements a template based EvolutionaryAlgorithm |
►CFeatureExtraction | |
CEnvelopeExtractor | |
CFFT | |
CFFTFeatures | |
CKMeansFeatures | |
CKMeansQuantizer | |
CMovementIndex | |
CMovementTrajectoryFeatures | |
CRBMQuantizer | |
CRegisterFeatureExtractionModule< T > | |
CRegisterFeatureExtractionModule< EnvelopeExtractor > | |
CRegisterFeatureExtractionModule< FFT > | |
CRegisterFeatureExtractionModule< FFTFeatures > | |
CRegisterFeatureExtractionModule< KMeansFeatures > | |
CRegisterFeatureExtractionModule< KMeansQuantizer > | |
CRegisterFeatureExtractionModule< MovementIndex > | |
CRegisterFeatureExtractionModule< MovementTrajectoryFeatures > | |
CRegisterFeatureExtractionModule< RBMQuantizer > | |
CRegisterFeatureExtractionModule< SOMQuantizer > | |
CRegisterFeatureExtractionModule< TimeDomainFeatures > | |
CRegisterFeatureExtractionModule< TimeseriesBuffer > | |
CRegisterFeatureExtractionModule< ZeroCrossingCounter > | |
CSOMQuantizer | |
CTimeDomainFeatures | |
CTimeseriesBuffer | |
CZeroCrossingCounter | |
CGestureRecognitionPipeline | |
CGridSearch< T > | |
CLinearLeastSquares | |
CMeanShift | |
CMovementDetector | |
►CNode | |
CClusterTreeNode | |
►CDecisionTreeNode | |
CDecisionTreeClusterNode | |
CDecisionTreeThresholdNode | |
CDecisionTreeTripleFeatureNode | This class implements a DecisionTreeTripleFeatureNode, which is a specific type of node used for a DecisionTree |
CRegisterNode< T > | |
CRegisterNode< ClusterTreeNode > | |
CRegisterNode< DecisionTreeClusterNode > | |
CRegisterNode< DecisionTreeNode > | |
CRegisterNode< DecisionTreeThresholdNode > | |
CRegisterNode< DecisionTreeTripleFeatureNode > | |
CRegisterNode< RegressionTreeNode > | |
CRegressionTreeNode | |
►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 |
CClassLabelChangeFilter | |
CClassLabelFilter | |
CClassLabelTimeoutFilter | |
CRegisterPostProcessingModule< T > | |
CRegisterPostProcessingModule< ClassLabelChangeFilter > | |
CRegisterPostProcessingModule< ClassLabelFilter > | |
CRegisterPostProcessingModule< ClassLabelTimeoutFilter > | |
►CPreProcessing | |
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 |
CDerivative | Computes either the first or second order derivative of the input signal |
CDoubleMovingAverageFilter | The class implements a Float moving average filter |
CFIRFilter | This class implements a Finite Impulse Response (FIR) Filter. It can support a low pass filter, high pass filter, or band pass filter |
CHighPassFilter | This class implements a High Pass Filter |
CLeakyIntegrator | Computes the following signal: y = y*z + x, where x is the input, y is the output and z is the leakrate |
CLowPassFilter | The class implements a low pass filter, this is based on an Exponential moving average filter: https://en.wikipedia.org/wiki/Exponential_smoothing |
CMedianFilter | The MedianFilter implements a simple median filter: https://en.wikipedia.org/wiki/Median_filter |
CMovingAverageFilter | The MovingAverageFilter implements a low pass moving average filter |
CRegisterPreProcessingModule< T > | 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" ); |
CRegisterPreProcessingModule< DeadZone > | |
CRegisterPreProcessingModule< Derivative > | |
CRegisterPreProcessingModule< DoubleMovingAverageFilter > | |
CRegisterPreProcessingModule< FIRFilter > | |
CRegisterPreProcessingModule< HighPassFilter > | |
CRegisterPreProcessingModule< LeakyIntegrator > | |
CRegisterPreProcessingModule< LowPassFilter > | |
CRegisterPreProcessingModule< MedianFilter > | |
CRegisterPreProcessingModule< MovingAverageFilter > | |
CRegisterPreProcessingModule< RMSFilter > | |
CRegisterPreProcessingModule< SavitzkyGolayFilter > | |
CRegisterPreProcessingModule< WeightedAverageFilter > | |
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 |
CWeightedAverageFilter | The WeightedAverageFilter implements a weighted average filter that gives a larger weight to more recent samples, and a smaller weight to older samples |
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 |
►CRegressifier | |
CLinearRegression | |
CLogisticRegression | |
CMLP | |
CMultidimensionalRegression | |
CRegisterRegressifierModule< T > | 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" ); |
CRegisterRegressifierModule< LinearRegression > | |
CRegisterRegressifierModule< LogisticRegression > | |
CRegisterRegressifierModule< MLP > | |
CRegisterRegressifierModule< MultidimensionalRegression > | |
CRegisterRegressifierModule< RegressionTree > | |
CRegressionTree | This class implements a basic Regression Tree |
CTree | |
CGuassModel | |
CHMMTrainingObject | |
CIndexDist | |
CIndexedDouble | |
CIndividual | |
CInfoLogMessage | |
►CLog | Base class for all GRT logging functionality |
CDebugLog | |
CErrorLog | |
CInfoLog | |
CTestingLog | |
CTrainingLog | |
►CLog | |
CWarningLog | |
CLUDecomposition | |
CMatrix< T > | |
►CMatrix< Float > | |
CMatrixFloat | |
CMatrix< GaussNeuron > | |
CMetrics | |
CMinDistModel | |
CMinMax | |
CMixtureModel | |
CNeuron | |
CObserver< NotifyType > | |
►CObserver< TestInstanceResult > | |
CMLBase | This is the main base class that all GRT machine learning algorithms should inherit from |
►CObserver< TrainingResult > | |
CMLBase | This is the main base class that all GRT machine learning algorithms should inherit from |
CObserverManager< NotifyType > | |
CObserverManager< DebugLogMessage > | |
CObserverManager< ErrorLogMessage > | |
CObserverManager< InfoLogMessage > | |
CObserverManager< TestingLogMessage > | |
►CObserverManager< TestInstanceResult > | |
CTestResultsObserverManager | |
CObserverManager< TrainingLogMessage > | |
►CObserverManager< TrainingResult > | |
CTrainingResultsObserverManager | |
CObserverManager< WarningLogMessage > | |
►CParticle | |
CFSMParticle | |
CParticleClassifierGestureTemplate | |
CParticleFilter< PARTICLE, SENSOR_DATA > | |
►CParticleFilter< FSMParticle, VectorFloat > | |
CFSMParticleFilter | |
►CParticleFilter< Particle, VectorFloat > | |
CParticleClassifierParticleFilter | |
CParticleSwarmOptimization< PARTICLE_TYPE, OBSERVATION_TYPE > | |
CPeakDetection | |
CPeakInfo | |
CPSOParticle< OBSERVATION_TYPE > | |
►CLIBSVM::QMatrix | |
►CLIBSVM::Kernel | |
CLIBSVM::ONE_CLASS_Q | |
CLIBSVM::SVC_Q | |
CLIBSVM::SVR_Q | |
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 |
CRangeTracker | |
CRegressionData | |
CRegressionSample | |
CSoftmaxModel | |
CLIBSVM::Solver::SolutionInfo | |
►CLIBSVM::Solver | |
CLIBSVM::Solver_NU | |
CSVD | |
CLIBSVM::svm_model | |
CLIBSVM::svm_node | |
CLIBSVM::svm_parameter | |
CLIBSVM::svm_problem | |
CTestingLogMessage | |
CTestInstanceResult | |
CTestResult | |
CThreadPool | |
CThresholdCrossingDetector | |
CTimer | |
CTimeSeriesClassificationData | |
CTimeSeriesClassificationSample | |
CTimeSeriesClassificationSampleTrimmer | |
CTimeSeriesPositionTracker | |
CTimeStamp | |
CTrainingDataRecordingTimer | |
CTrainingLogMessage | |
CTrainingResult | |
CUnlabelledData | |
CUtil | |
►Cvector | |
CVector< T > | |
CVector< AdaBoostClassModel > | |
CVector< ANBC_Model > | |
CVector< ClassificationSample > | |
CVector< Classifier * > | |
CVector< ClassLabelAndTimer > | |
CVector< ClassTracker > | |
CVector< ClusterInfo > | |
CVector< ClusterLevel > | |
CVector< ContinuousHiddenMarkovModel > | |
CVector< DecisionTreeNode * > | |
CVector< DiscreteHiddenMarkovModel > | |
CVector< DTWTemplate > | |
CVector< FastFourierTransform > | |
CVector< FeatureExtraction * > | |
►CVector< Float > | |
CVectorFloat | |
CVector< FSMParticle > | |
CVector< GridSearchParam< unsigned int > > | |
CVector< GuassModel > | |
CVector< IndexedDouble > | |
CVector< INDIVIDUAL > | |
CVector< int > | |
CVector< MatrixFloat > | |
CVector< MinDistModel > | |
CVector< MinMax > | |
CVector< MixtureModel > | |
CVector< Neuron > | |
CVector< Observer< DebugLogMessage > * > | |
CVector< Observer< ErrorLogMessage > * > | |
CVector< Observer< InfoLogMessage > * > | |
CVector< Observer< NotifyType > * > | |
CVector< Observer< TestingLogMessage > * > | |
CVector< Observer< TestInstanceResult > * > | |
CVector< Observer< TrainingLogMessage > * > | |
CVector< Observer< TrainingResult > * > | |
CVector< Observer< WarningLogMessage > * > | |
CVector< Particle > | |
CVector< PARTICLE > | |
CVector< PARTICLE_TYPE > | |
CVector< ParticleClassifierGestureTemplate > | |
CVector< PeakInfo > | |
CVector< PostProcessing * > | |
CVector< PreProcessing * > | |
CVector< Regressifier * > | |
CVector< RegressionSample > | |
CVector< SoftmaxModel > | |
CVector< TestInstanceResult > | |
CVector< TestResult > | |
CVector< TimeSeriesClassificationSample > | |
CVector< TimeSeriesPositionTracker > | |
CVector< TrainingResult > | |
CVector< UINT > | |
CVector< unsigned int > | |
CVector< Vector< Context * > > | |
CVector< Vector< IndexDist > > | |
CVector< Vector< IndexedDouble > > | |
CVector< Vector< INDIVIDUAL > > | |
CVector< Vector< int > > | |
CVector< Vector< PARTICLE_TYPE > > | |
CVector< Vector< UINT > > | |
CVector< Vector< VectorFloat > > | |
CVector< VectorFloat > | |
CVector< WeakClassifier * > | |
CWarningLogMessage | |
►CWeakClassifier | |
CDecisionStump | |
CRadialBasisFunction | |
CRegisterWeakClassifierModule< T > | |
CRegisterWeakClassifierModule< DecisionStump > | |
CRegisterWeakClassifierModule< RadialBasisFunction > | |