13#include <MachineLearning/FgGPBaseModel.h>
14#include <MachineLearning/FgSparseGPBaseLayer.h>
18 namespace MachineLearning
24 template<
typename Scalar>
30 template<
typename Scalar>
57 template<
typename Scalar>
72 SparseGPBaseModel(
const af::array& Y,
const af::array& X,
int numInducing = 200, LogLikType lType = LogLikType::Gaussian);
97 virtual void PredictF(
const af::array& testInputs, af::array& mf, af::array& vf)
override;
108 virtual void SampleY(
const af::array inputs,
int numSamples, af::array& outFunctions)
override;
219 friend class boost::serialization::access;
221 template<
class Archive>
222 void serialize(Archive& ar,
unsigned int version)
224 ar& boost::serialization::base_object<GPBaseModel<Scalar>>(*this);
232 ar& BOOST_SERIALIZATION_NVP(ik);
233 ar& BOOST_SERIALIZATION_NVP(iq);
234 ar& BOOST_SERIALIZATION_NVP(afX);
235 ar& BOOST_SERIALIZATION_NVP(gpLayer);
Base class with abstract and basic function definitions. All GP models will be derived from this clas...
Abstract class for different GP likelihood layers.
Base class for all sparse GP models.
af::array afX
training inputs
SparseGPBaseModel(const af::array &Y, const af::array &X, int numInducing=200, LogLikType lType=LogLikType::Gaussian)
Constructor.
af::array GetTrainingInputs()
Gets training inputs X.
af::array GetPseudoInputs()
Gets pseudo inputs.
virtual ~SparseGPBaseModel()
Destructor.
SparseGPBaseModel()
Default constructor.
virtual void UpdateParameters() override
Updates the parameters.
SparseGPBaseLayer< Scalar > * gpLayer
gp layer
virtual void FixInducing(bool isfixed)
Set fixation for inducing inputs.
virtual void SetParameters(const af::array ¶m) override
Sets the parameters for each optimization iteration.
virtual bool Init() override
Initializes the model.
virtual void PredictF(const af::array &testInputs, af::array &mf, af::array &vf) override
Predict noise free functions values .
virtual int GetNumParameters() override
Gets number of parameters.
int ik
number of inducing inputs
SparseGPBaseLayer< Scalar > * GetGPLayer()
Gets the gp layer.
virtual void FixKernelParameters(bool isfixed)
Sets fixation for hyperparameters.
virtual void SampleY(const af::array inputs, int numSamples, af::array &outFunctions) override
Generate function samples from posterior.
virtual af::array GetParameters() override
Gets the parameters for each optimization iteration.
void SetTrainingInputs(af::array &inputs)
Gets training inputs X.