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 >privatevirtual
afBiasNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >private
afIndexesNeuralEngine::MachineLearning::IModel< Scalar >private
afSegmentsNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >private
afXNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >private
afYNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >private
bInitNeuralEngine::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 >privatevirtual
FixKernelParameters(bool isfixed)NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
FixLikelihoodParameters(bool isfixed)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >privatevirtual
Function(const af::array &x, af::array &outGradient)NeuralEngine::MachineLearning::IModel< Scalar >privatevirtual
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() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
GetParameters() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
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
gpLayerNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >private
iBatchSizeNeuralEngine::MachineLearning::IModel< Scalar >private
iDNeuralEngine::MachineLearning::IModel< Scalar >private
ikNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >private
IModel(int numData, int numDimension, ModelType type)NeuralEngine::MachineLearning::IModel< Scalar >private
iNNeuralEngine::MachineLearning::IModel< Scalar >private
Init() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
iqNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >private
likLayerNeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >private
m_dTypeNeuralEngine::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 >privatevirtual
PredictF(const af::array &testInputs, af::array &mf, af::array &vf) overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
PredictY(const af::array &testInputs, af::array &my, af::array &vy)NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >privatevirtual
SampleY(const af::array inputs, int numSamples, af::array &outFunctions) overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
serialize(Archive &ar, unsigned int version) (defined in NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar >)NeuralEngine::MachineLearning::GPModels::PowerEP::SGPLVM< Scalar >inlineprivate
SetBatchSize(int size)NeuralEngine::MachineLearning::IModel< Scalar >privatevirtual
SetIndexes(af::array &indexes)NeuralEngine::MachineLearning::IModel< Scalar >private
SetParameters(const af::array &param) overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
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() overrideNeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual
~GPBaseModel()NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >privatevirtual
~SparseGPBaseModel()NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >privatevirtual