13#include <MachineLearning/FgGPLVMBaseModel.h>
14#include <MachineLearning/FgSparseGPBaseLayer.h>
18 namespace MachineLearning
24 template<
typename Scalar>
30 template<
typename Scalar>
77 template<
typename Scalar>
96 int numInducing = 200, LogLikType lType = LogLikType::Gaussian, XInit emethod = XInit::pca);
121 virtual void PredictF(
const af::array& testInputs, af::array& mf, af::array& vf)
override;
132 virtual void SampleY(
const af::array inputs,
int numSamples, af::array& outFunctions)
override;
205 friend class boost::serialization::access;
207 template<
class Archive>
208 void serialize(Archive& ar,
unsigned int version)
210 ar& boost::serialization::base_object<GPLVMBaseModel<Scalar>>(*this);
218 ar& BOOST_SERIALIZATION_NVP(ik);
219 ar& BOOST_SERIALIZATION_NVP(gpLayer);
Base class with abstract and basic function definitions. All deep GP models will be derived from this...
Abstract class for different GP likelihood layers.
Base class for all sparse GPLVM models.
virtual void SetParameters(const af::array ¶m) override
Sets the parameters for each optimization iteration.
virtual void PredictF(const af::array &testInputs, af::array &mf, af::array &vf) override
Predict noise free functions values .
SparseGPBaseLayer< Scalar > * gpLayer
sparse Gaussian Process layer
virtual ~SparseGPLVMBaseModel()
Destructor.
virtual void FixKernelParameters(bool isfixed) override
Sets fixation for hyperparameters.
SparseGPLVMBaseModel()
Default constructor.
virtual bool Init() override
Initializes the model.
virtual void FixInducing(bool isfixed) override
Set fixation for inducing inputs.
virtual void SampleY(const af::array inputs, int numSamples, af::array &outFunctions) override
Generate function samples from posterior.
int ik
number of inducing inputs
virtual int GetNumParameters() override
Gets number of parameters.
virtual void UpdateParameters() override
Updates the parameters.
SparseGPLVMBaseModel(const af::array &Y, int latentDimension, Scalar priorMean=0.0, Scalar priorVariance=1.0, int numInducing=200, LogLikType lType=LogLikType::Gaussian, XInit emethod=XInit::pca)
Constructor.
virtual af::array GetParameters() override
Gets the parameters for each optimization iteration.