13#include <MachineLearning/FgSparseDeepGPLVMBaseModel.h>
17 namespace MachineLearning
65 template<
typename Scalar>
81 Scalar priorMean = 0.0,
Scalar priorVariance = 1.0, PropagationMode probMode = PropagationMode::MomentMatching,
82 LogLikType lType = LogLikType::Gaussian, XInit emethod = XInit::pca);
94 SDGPLVM(
const af::array& Y,
int latentDimension, std::vector<HiddenLayerDescription> descriptions,
Scalar alpha = 1.0,
95 Scalar priorMean = 0.0,
Scalar priorVariance = 1.0, PropagationMode probMode = PropagationMode::MomentMatching,
96 LogLikType lType = LogLikType::Gaussian, XInit emethod = XInit::pca);
149 af::array
CavityGradientLatents(
const af::array& dmx,
const af::array& dvx,
const af::array& m,
const af::array& v);
167 PropagationMode pMode;
169 friend class boost::serialization::access;
171 template<
class Archive>
172 void serialize(Archive& ar,
unsigned int version)
174 ar& boost::serialization::base_object<SparseDeepGPLVMBaseModel<Scalar>>(*this);
176 ar& BOOST_SERIALIZATION_NVP(dAlpha);
177 ar& BOOST_SERIALIZATION_NVP(pMode);
Sparse deep GPLVM via Approximated Expectation Propagation (AEP).
virtual ~SDGPLVM()
Destructor.
SDGPLVM(const af::array &Y, int latentDimension, std::vector< HiddenLayerDescription > descriptions, Scalar alpha=1.0, Scalar priorMean=0.0, Scalar priorVariance=1.0, PropagationMode probMode=PropagationMode::MomentMatching, LogLikType lType=LogLikType::Gaussian, XInit emethod=XInit::pca)
Constructor.
af::array CavityGradientLatents(const af::array &dmx, const af::array &dvx, const af::array &m, const af::array &v)
Gradient of the cavity distribution w.r.t. latent points.
Scalar dAlpha
fraction parameter
virtual Scalar Function(const af::array &x, af::array &outGradient) override
Cost function the given parameter inputs.
SDGPLVM()
Default Constructor.
void CavityLatents(af::array &mx, af::array &vx)
Computes the cavity distribution of the latent points.
Scalar ComputePhiLatents(const af::array &mx, const af::array &vx, af::array *dmx=nullptr, af::array *dvx=nullptr)
Calculates the likelihood contribution of the latent points and its gradients.
SDGPLVM(const af::array &Y, int latentDimension, HiddenLayerDescription description, Scalar alpha=1.0, Scalar priorMean=0.0, Scalar priorVariance=1.0, PropagationMode probMode=PropagationMode::MomentMatching, LogLikType lType=LogLikType::Gaussian, XInit emethod=XInit::pca)
Constructor.
Base class with abstract and basic function definitions. All deep GP models will be derived from this...
Description of the layer.