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

NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar > Member List

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

AddData(const af::array Ytrain) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private virtual
afBias | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
afIndexes | NeuralEngine::MachineLearning::IModel< Scalar > | private
afSegments | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
afX | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
afY | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
bInit | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
boost::serialization::access (defined in NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar >) | NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar > | friend
FixInducing(bool isfixed) | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
FixKernelParameters(bool isfixed) | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
FixLikelihoodParameters(bool isfixed) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private virtual
Function(const af::array &x, af::array &outGradient) | NeuralEngine::MachineLearning::IModel< Scalar > | private virtual
GetBatchSize() | NeuralEngine::MachineLearning::IModel< Scalar > | private
GetDataDimensionality() | NeuralEngine::MachineLearning::IModel< Scalar > | private
GetDataLenght() | NeuralEngine::MachineLearning::IModel< Scalar > | private
GetGPLayer() | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
GetModelType() | NeuralEngine::MachineLearning::IModel< Scalar > | private
GetNumParameters() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
GetParameters() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
GetPseudoInputs() | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
GetSegments() | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
GetTrainingData() | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
GetTrainingInputs() | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
GPBaseModel(const af::array &Y, LogLikType lType=LogLikType::Gaussian, ModelType mtype=ModelType::GPR) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
GPBaseModel() | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
gpLayer | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
iBatchSize | NeuralEngine::MachineLearning::IModel< Scalar > | private
iD | NeuralEngine::MachineLearning::IModel< Scalar > | private
ik | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
IModel(int numData, int numDimension, ModelType type) | NeuralEngine::MachineLearning::IModel< Scalar > | private
iN | NeuralEngine::MachineLearning::IModel< Scalar > | private
Init() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
iq | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
likLayer | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
m_dType | NeuralEngine::MachineLearning::IModel< Scalar > | private
mType (defined in NeuralEngine::MachineLearning::IModel< Scalar >) | NeuralEngine::MachineLearning::IModel< Scalar > | private
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 > | private virtual
PredictF(const af::array &testInputs, af::array &mf, af::array &vf) override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
PredictY(const af::array &testInputs, af::array &my, af::array &vy) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private virtual
SampleY(const af::array inputs, int numSamples, af::array &outFunctions) override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
serialize(Archive &ar, unsigned int version) (defined in NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar >) | NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar > | inline private
SetBatchSize(int size) | NeuralEngine::MachineLearning::IModel< Scalar > | private virtual
SetIndexes(af::array &indexes) | NeuralEngine::MachineLearning::IModel< Scalar > | private
SetParameters(const af::array &param) override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
SetSegments(af::array segments) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
SetTrainingData(af::array &data) | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private
SetTrainingInputs(af::array &inputs) | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
SparseGPBaseModel(const af::array &Y, const af::array &X, int numInducing=200, LogLikType lType=LogLikType::Gaussian) | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
SparseGPBaseModel() | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private
UpdateParameters() override | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual
~GPBaseModel() | NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar > | private virtual
~SparseGPBaseModel() | NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar > | private virtual