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

NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > Member List

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

AddData(const af::array Ytrain) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | virtual
afBias | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | protected
afIndexes | NeuralEngine::MachineLearning::IModel< Scalar > | protected
afSegments | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | protected
afX | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | protected
afY | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | protected
bInit | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | protected
boost::serialization::access (defined in NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar >) | NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > | friend
dAlpha | NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > | private
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) override | NeuralEngine::MachineLearning::GPModels::AEP::SGPR< 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() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | virtual
GetParameters() override | NeuralEngine::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 > |
gpLayer | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | protected
iBatchSize | NeuralEngine::MachineLearning::IModel< Scalar > | protected
iD | NeuralEngine::MachineLearning::IModel< Scalar > | protected
ik | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | protected
IModel(int numData, int numDimension, ModelType type) | NeuralEngine::MachineLearning::IModel< Scalar > | protected
iN | NeuralEngine::MachineLearning::IModel< Scalar > | protected
Init() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | virtual
iq | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | protected
likLayer | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | protected
m_dType | NeuralEngine::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) override | NeuralEngine::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) override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | virtual
serialize(Archive &ar, unsigned int version) (defined in NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar >) | NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > | inline private
SetBatchSize(int size) | NeuralEngine::MachineLearning::IModel< Scalar > | virtual
SetIndexes(af::array &indexes) | NeuralEngine::MachineLearning::IModel< Scalar > |
SetParameters(const af::array &param) override | NeuralEngine::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 > |
SGPR(const af::array &Y, const af::array &X, int numInducing=20, Scalar alpha=1.0, LogLikType lType=LogLikType::Gaussian) | NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > |
SGPR() | NeuralEngine::MachineLearning::GPModels::AEP::SGPR< Scalar > |
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() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | virtual
~GPBaseModel() | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | virtual
~SparseGPBaseModel() | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | virtual