NeuralEngine
A Game Engine with embeded Machine Learning algorithms based on Gaussian Processes.
NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar > Member List

This is the complete list of members for NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >, including all inherited members.

AddData(const af::array Ytrain)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >virtual
afBiasNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >protected
afIndexesNeuralEngine::MachineLearning::IModel< Scalar >protected
afSegmentsNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >protected
afXNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >protected
afYNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >protected
bInitNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >protected
boost::serialization::access (defined in NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >)NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >friend
FixInducing(bool isfixed)NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
FixKernelParameters(bool isfixed)NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
FixLikelihoodParameters(bool isfixed)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >virtual
Function(const af::array &x, af::array &outGradient)NeuralEngine::MachineLearning::IModel< Scalar >virtual
GetBatchSize()NeuralEngine::MachineLearning::IModel< Scalar >
GetDataDimensionality()NeuralEngine::MachineLearning::IModel< Scalar >
GetDataLenght()NeuralEngine::MachineLearning::IModel< Scalar >
GetGPLayer()NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
GetModelType()NeuralEngine::MachineLearning::IModel< Scalar >
GetNumParameters() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
GetParameters() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
GetPseudoInputs()NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
GetSegments()NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
GetTrainingData()NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
GetTrainingInputs()NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
GPBaseModel(const af::array &Y, LogLikType lType=LogLikType::Gaussian, ModelType mtype=ModelType::GPR)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
GPBaseModel()NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
gpLayerNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >protected
iBatchSizeNeuralEngine::MachineLearning::IModel< Scalar >protected
iDNeuralEngine::MachineLearning::IModel< Scalar >protected
ikNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >protected
IModel(int numData, int numDimension, ModelType type)NeuralEngine::MachineLearning::IModel< Scalar >protected
iNNeuralEngine::MachineLearning::IModel< Scalar >protected
Inference(Scalar alpha=1.0, int numIter=10, bool parallelUpdate=false, Scalar decay=0.5)NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >
Init() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
iqNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >protected
likLayerNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >protected
m_dTypeNeuralEngine::MachineLearning::IModel< Scalar >protected
mType (defined in NeuralEngine::MachineLearning::IModel< Scalar >)NeuralEngine::MachineLearning::IModel< Scalar >protected
Optimise(OptimizerType method=L_BFGS, Scalar tol=0.0, bool reinit_hypers=true, int maxiter=1000, int mb_size=0, LineSearchType lsType=MoreThuente, bool disp=true, int *cycle=nullptr)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >virtual
PredictF(const af::array &testInputs, af::array &mf, af::array &vf) overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
PredictY(const af::array &testInputs, af::array &my, af::array &vy)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >virtual
SampleY(const af::array inputs, int numSamples, af::array &outFunctions) overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
serialize(Archive &ar, unsigned int version) (defined in NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >)NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >inlineprivate
SetBatchSize(int size)NeuralEngine::MachineLearning::IModel< Scalar >virtual
SetIndexes(af::array &indexes)NeuralEngine::MachineLearning::IModel< Scalar >
SetParameters(const af::array &param) overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
SetSegments(af::array segments)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
SetTrainingData(af::array &data)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
SetTrainingInputs(af::array &inputs)NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
SGPR2nd(const af::array &Y, const af::array &X, int numInducing=20, LogLikType lType=LogLikType::Gaussian)NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >
SGPR2nd() (defined in NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >)NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR2nd< Scalar >protected
SparseGPBaseModel(const af::array &Y, const af::array &X, int numInducing=200, LogLikType lType=LogLikType::Gaussian)NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
SparseGPBaseModel()NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
UpdateParameters() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual
~GPBaseModel()NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >virtual
~SparseGPBaseModel()NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >virtual