Cbetter_enums::_eat_assign< EnumType > | |
Cbetter_enums::_initialize_at_program_start< Enum > | |
Cbetter_enums::_Iterable< Element > | |
CNeuralEngine::MachineLearning::AfArma | ArrayFire Armadillo conversation. |
CNeuralEngine::MachineLearning::AfCv | ArrayFire OpenCV conversation. |
CNeuralEngine::Array2< T > |
The Array2 class represents a 2-dimensional array that minimizes the number of new and delete calls. The T objects are stored in a contiguous array. |
CNeuralEngine::Array3< T > | The Array3 class represents a 3-dimensional array that minimizes the number of new and delete calls. The T objects are stored in a contiguous array.
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CNeuralEngine::Array4< T > | The Array4 class represents a 4-dimensional array that minimizes the number of new and delete calls. The T objects are stored in a contiguous array.
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CNeuralEngine::MachineLearning::Color | |
CNeuralEngine::MachineLearning::CommonUtil< Scalar > | |
CNeuralEngine::ComputeModel | Core item ComputeModel. |
CNeuralEngine::MachineLearning::EigenvalueDecomposition | Eigenvalue Decomposition. |
CNeuralEngine::Environment | |
CNeuralEngine::MachineLearning::Figure | |
►Cstd::fstream | |
CNeuralEngine::EFStream | |
CNeuralEngine::MachineLearning::GaussHermiteQuadrature< Scalar > | Gauss-Hermite Quadrature. |
►CNeuralEngine::MachineLearning::GPNode< Scalar > | This class represents grouping nodes in a hiearchy. |
►CNeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar > |
Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
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►CNeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar > |
Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
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CNeuralEngine::MachineLearning::GPModels::AEP::SDGPLVM< Scalar > | Sparse deep GPLVM via Approximated Expectation Propagation (AEP). |
►CNeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar > | Base class for all sparse GPLVM models. |
CNeuralEngine::MachineLearning::GPModels::AEP::SGPLVM< Scalar > | Sparse GPLVM via Approximated Expectation Propagation (AEP). |
►CNeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar > |
Base class with abstract and basic function definitions. All GP state space models will be derived from this class.
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►CNeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar > |
Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
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CNeuralEngine::MachineLearning::GPModels::AEP::SDGPSSM< Scalar > | Sparse deep GPSSM via Approximated Expectation Propagation (AEP). |
►CNeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar > | Base class for all sparse GPSSM models. |
CNeuralEngine::MachineLearning::GPModels::AEP::SGPSSM< Scalar > | Sparse GPSSM via Approximated Expectation Propagation (AEP). |
CNeuralEngine::MachineLearning::HiddenLayerDescription | Description of the layer. |
CNeuralEngine::MachineLearning::HighGUI | |
►CNeuralEngine::MachineLearning::GPModels::IBackconstraint< Scalar > |
Abstract class for back-constraints, a kind of prior knowledge to force topological positions of uncertain latent inputs. All types of back-constraints will be derived from this class.
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CNeuralEngine::MachineLearning::GPModels::KBR< Scalar > |
Back-constraints via kernel based regression.
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CNeuralEngine::MachineLearning::GPModels::PTC< Scalar > |
Back-constraints via Periodic topological constraint.
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►CNeuralEngine::MachineLearning::IEmbed | IEmbed. |
CNeuralEngine::MachineLearning::Isomap | Isomap. |
CNeuralEngine::MachineLearning::LLE | Locally-Linear Embedding. |
CNeuralEngine::MachineLearning::PCA | Pirincipal Component Analyses. |
CNeuralEngine::MachineLearning::IGraph< T, U > | |
►CNeuralEngine::MachineLearning::IGraph< DiscreteFactorNode, DiscreteVariableNode > | |
CNeuralEngine::MachineLearning::DiscreteGraph | |
►CNeuralEngine::MachineLearning::IKernel< Scalar > | |
CNeuralEngine::MachineLearning::ARDKernel< Scalar > | Automatic Relevance Determination kernel. |
CNeuralEngine::MachineLearning::CompoundKernel< Scalar > | Compound kernel function. |
CNeuralEngine::MachineLearning::InterDomainKernel< Scalar > | Inter Domain kernel function. |
CNeuralEngine::MachineLearning::LinearAccelerationKernel< Scalar > | Linear kernel function for second order state space models. |
CNeuralEngine::MachineLearning::LinearKernel< Scalar > | Linear kernel function. |
CNeuralEngine::MachineLearning::RBFAccelerationKernel< Scalar > | Radial basis kernel function for second order state space models. |
CNeuralEngine::MachineLearning::RBFKernel< Scalar > | Radial basis kernel function. |
CNeuralEngine::MachineLearning::StyleKernel< Scalar > | Linear style kernel function. |
CNeuralEngine::MachineLearning::TensorKernel< Scalar > | Tensor kernel function. |
CNeuralEngine::MachineLearning::WhiteKernel< Scalar > | White kernel function. |
►CNeuralEngine::MachineLearning::ILayer< Scalar > | Abstract class for different kind of layers. |
►CNeuralEngine::MachineLearning::GPModels::GPBaseLayer< Scalar > | Abstract class for different GP likelihood layers. |
►CNeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar > | Abstract class for different GP likelihood layers. |
CNeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar > | Sparse GP layer. |
CNeuralEngine::MachineLearning::GPModels::PowerEP::SGPLayer< Scalar > | Sparse GP layer. |
CNeuralEngine::MachineLearning::GPModels::PowerEP::SGPLayer2nd< Scalar > | Sparse GP layer. |
CNeuralEngine::MachineLearning::GPModels::GaussEmission< Scalar > | Likelihood emission layer for State Space Models based on Gaussian distribution. |
►CNeuralEngine::MachineLearning::GPModels::LikelihoodBaseLayer< Scalar > | Abstract class for different GP likelihood layers. |
CNeuralEngine::MachineLearning::GPModels::GaussLikLayer< Scalar > | Likelihood estimation based on Gaussian distribution. |
CNeuralEngine::MachineLearning::GPModels::ProbitLikLayer< Scalar > | Likelihood estimation based on Probit distribution. |
►CNeuralEngine::MachineLearning::ILineSearch< Scalar > | |
CNeuralEngine::MachineLearning::ArmijoBracketingLineSearch< Scalar > | Armijo–Goldstein. |
CNeuralEngine::MachineLearning::ArmijoLineSearch< Scalar > | Armijo–Goldstein. |
CNeuralEngine::MachineLearning::MoreThuenteLineSearch< Scalar > | More Thuente. |
CNeuralEngine::MachineLearning::StrongWolfeBacktrackingLineSearch< Scalar > | Strong Wolfe. |
CNeuralEngine::MachineLearning::StrongWolfeBracketingLineSearch< Scalar > | Strong Wolfe. |
CNeuralEngine::MachineLearning::WolfeBacktrackingLineSearch< Scalar > | Strong Wolfe. |
CNeuralEngine::MachineLearning::WolfeBracketingLineSearch< Scalar > | Strong Wolfe. |
►CNeuralEngine::MachineLearning::IMessage | |
CNeuralEngine::MachineLearning::DiscreteMessage | |
CNeuralEngine::MachineLearning::GaussianMessage | |
►CNeuralEngine::MachineLearning::IModel< Scalar > |
Base class with abstract and basic function definitions. All models will be derived from this class.
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►CNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > |
Base class with abstract and basic function definitions. All GP models will be derived from this class.
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►CNeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar > |
Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
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►CNeuralEngine::MachineLearning::GPModels::SparseDeepGPBaseModel< Scalar > |
Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
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CNeuralEngine::MachineLearning::GPModels::AEP::SDGPR< Scalar > | Deep sparse Gaussian process via Approximated Expectation Propagation (AEP). |
CNeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar > |
Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
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CNeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar > |
Base class with abstract and basic function definitions. All GP state space models will be derived from this class.
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►CNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | Base class for all sparse GP models. |
►CNeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > | Sparse Gaussian process via Approximated Expectation Propagation (AEP). |
CNeuralEngine::MachineLearning::GPModels::AEP::BSGPR< Scalar > | Blendshape Inference based on AEP_SGPR. |
CNeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar > |
Sparse Gaussian Process Latent Variable Model (SGPLVM) with optimization through Power Expectation Propagation (PEP). |
CNeuralEngine::MachineLearning::GPModels::PowerEP::SGPR< Scalar > |
Sparse Gaussian Process Regression (SGPR) with optimization through Power Expectation Propagation (PEP). |
CNeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar > |
Sparse Gaussian Process Regression (SGPR2nd) with optimization through Power Expectation Propagation (PEP). |
CNeuralEngine::MachineLearning::GPModels::VFE::SGPLVM< Scalar > |
Sparse Gaussian Process Latent Variable Model (SGPLVM) with optimization through Variational Free Energy (VFE). |
►CNeuralEngine::MachineLearning::INode | Base Class INode. |
►CNeuralEngine::MachineLearning::IFactorNode | |
CNeuralEngine::MachineLearning::DiscreteFactorNode | Base Class INode. |
►CNeuralEngine::MachineLearning::IVariableNode | |
CNeuralEngine::MachineLearning::DiscreteVariableNode | Class Discrete Variable Node. |
►Cstd::integral_constant | |
CNeuralEngine::MachineLearning::is_Scalar< T > | |
CNeuralEngine::MachineLearning::is_float< T > | |
►CNeuralEngine::MachineLearning::IObjectiveFunction< Scalar > | |
CNeuralEngine::MachineLearning::NonlinearObjectiveFunction< Scalar > | |
►CNeuralEngine::MachineLearning::IOptimizationMethod< Scalar > | Common interface for function optimization methods. |
►CNeuralEngine::MachineLearning::BaseOptimizationMethod< Scalar > | Base class for optimization methods. |
►CNeuralEngine::MachineLearning::BaseGradientOptimizationMethod< Scalar, MoreThuente > | |
CNeuralEngine::MachineLearning::AdaMaxSolver< Scalar, LSType > | AdaMax optimizer. |
CNeuralEngine::MachineLearning::AdamSolver< Scalar, LSType > | Adam optimizer. |
CNeuralEngine::MachineLearning::LBFGSBSolver< Scalar, LSType > | Limited-memory BFGS (L-BFGS or LM-BFGS). |
CNeuralEngine::MachineLearning::LBFGSSolver< Scalar, LSType > | Limited-memory BFGS (L-BFGS or LM-BFGS). |
CNeuralEngine::MachineLearning::NadamSolver< Scalar, LSType > | AdaMax optimizer. |
CNeuralEngine::MachineLearning::ScaledConjugateGradient< Scalar, LSType > | Conjugate Gradient exit codes. |
CNeuralEngine::MachineLearning::BaseGradientOptimizationMethod< Scalar, LSType > | Base class for gradient-based optimization methods. |
CNeuralEngine::MachineLearning::IGradientOptimizationMethod< Scalar > | Common interface for function optimization methods which depend on having both an objective function and a gradient function definition available. |
CNeuralEngine::MachineLearning::KMeans< Scalar > | KMeans cluster. |
Clayertype_serializer | |
CNeuralEngine::LexicoArray2< RowMajor, Real, Dimensions > | |
CNeuralEngine::LexicoArray2< false, Real > | |
CLexicoArray2< false, Real, NumRows, NumCols > | |
CNeuralEngine::LexicoArray2< true, Real > | |
CLexicoArray2< true, Real, NumRows, NumCols > | |
►CNeuralEngine::Listener | |
CNeuralEngine::LogToFile | |
CNeuralEngine::LogToMessageBox | |
CNeuralEngine::LogToOutputWindow | |
CNeuralEngine::LogToStdout | |
CNeuralEngine::LogToStringArray | |
CNeuralEngine::Logger | |
CNeuralEngine::LogReporter | |
Cbetter_enums::map< Enum, T, Compare > | |
►Cstd::map | |
CNeuralEngine::MachineLearning::TMsgBox< T > | |
Cbetter_enums::map_compare< T > | |
Cbetter_enums::map_compare< const char * > | |
Cbetter_enums::map_compare< const wchar_t * > | |
CNeuralEngine::MatlabIO | Matlab Mat file parser for C++ OpenCV. |
CNeuralEngine::MatlabIOContainer | A container class for storing type agnostic variables. |
CNeuralEngine::MachineLearning::Metrics< Scalar > | |
CNeuralEngine::MinHeap< KeyType, ValueType > | Minimum heap binary tree. |
CNeuralEngine::MachineLearning::Offset | |
CNeuralEngine::MachineLearning::OptimizationMethod< TCode > | Common interface for function optimization methods. |
Cbetter_enums::optional< T > | |
CNeuralEngine::MachineLearning::Point2 | |
CNeuralEngine::MachineLearning::Point3 | |
CNeuralEngine::MachineLearning::Potential | It is simply a std::vector with an interface designed for dealing with probability mass functions. It is a flattend version of an D dimensional propability table |
CNeuralEngine::MinHeap< KeyType, ValueType >::Record | |
CNeuralEngine::MachineLearning::Rect | |
CNeuralEngine::ReversalObject< Iterator > | Reversal object. |
CNeuralEngine::MachineLearning::Series | |
CNeuralEngine::MachineLearning::GPModels::VFE::SGPLayer | Sparse GP layer. |
CNeuralEngine::MachineLearning::GPModels::VFE::SGPR |
Sparse Gaussian Process Regression Model (SGPR) with optimization through Variational Free Energy (VFE). |
CNeuralEngine::MachineLearning::Size | |
CNeuralEngine::MachineLearning::GPModels::Style< Scalar > | Style variable. |
CNeuralEngine::ThreadSafeMap< Key, Value > | |
CNeuralEngine::ThreadSafeQueue< Element > | |
CNeuralEngine::Timer | |
CNeuralEngine::MachineLearning::Trans | |
CNeuralEngine::TypeName< T > | |
CNeuralEngine::TypeName< bool > | |
CNeuralEngine::TypeName< char > | |
CNeuralEngine::TypeName< cv::Mat > | |
CNeuralEngine::TypeName< double > | |
CNeuralEngine::TypeName< float > | |
CNeuralEngine::TypeName< int16_t > | |
CNeuralEngine::TypeName< int32_t > | |
CNeuralEngine::TypeName< int64_t > | |
CNeuralEngine::TypeName< int8_t > | |
CNeuralEngine::TypeName< MatlabIOContainer > | |
CNeuralEngine::TypeName< std::vector< cv::Mat > > | |
CNeuralEngine::TypeName< std::vector< MatlabIOContainer > > | |
CNeuralEngine::TypeName< std::vector< std::vector< MatlabIOContainer > > > | |
CNeuralEngine::TypeName< uint16_t > | |
CNeuralEngine::TypeName< uint32_t > | |
CNeuralEngine::TypeName< uint64_t > | |
CNeuralEngine::TypeName< uint8_t > | |
CNeuralEngine::TypeName< void > | |
CNeuralEngine::MachineLearning::Util | |
CNeuralEngine::Util | A Utility class. |
CNeuralEngine::MachineLearning::View | |
CNeuralEngine::MachineLearning::Window | |
CNeuralEngine::MachineLearning::Isomap::Xstruct | |
Cint | |
CScalar | |
Cvector< DiscreteFactorNode * > | |
Cvector< DiscreteVariableNode * > | |