NeuralEngine A Game Engine with embeded Machine Learning algorithms based on Gaussian Processes. |

Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 1 2 3 4 5]

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