NeuralEngine
A Game Engine with embeded Machine Learning algorithms based on Gaussian Processes.
NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar > Class Template Reference

Sparse GP layer. More...

#include <FgAEPSparseGPLayer.h>

Inheritance diagram for NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >:
Collaboration diagram for NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >:

Public Member Functions

 SGPLayer (int numPoints, int numPseudos, int outputDim, int inputDim)
 Constructor. More...
 
virtual ~SGPLayer ()
 Destructor. More...
 
void ForwardPredictionCavity (af::array &outMout, af::array &outVout, af::array *psi1out, af::array *psi2out, const af::array &mx, const af::array *vx=nullptr, Scalar alpha=1.0, PropagationMode mode=PropagationMode::MomentMatching)
 Forward prediction through cavity. More...
 
void ForwardPredictionRandomCavityMC (af::array &mout, af::array &vout, af::array &xout, af::array &eps, const af::array &mx, const af::array &vx, Scalar alpha=1.0)
 Forward prediction random cavity MCMC. More...
 
af::array BackpropGradientsReg (const af::array &m, const af::array &v, const af::array &dlogZ_dm, const af::array &dlogZ_dv, const af::array &x, std::map< std::string, af::array > *outGrad_cav=nullptr, Scalar alpha=1.0)
 Back propagation gradients through usual GP regression task. More...
 
af::array BackpropGradientsMM (const af::array &m, const af::array &v, const af::array &dlogZ_dm, const af::array &dlogZ_dv, const af::array &psi1, const af::array &psi2, const af::array &mx, const af::array &vx, std::map< std::string, af::array > *outGrad_cav, Scalar alpha=1.0)
 Back propagation gradients through moment matching. More...
 
af::array BackpropGradientsMC (const af::array &m, const af::array &v, const af::array &eps, const af::array &dlogZ_dm, const af::array &dlogZ_dv, const af::array &x, std::map< std::string, af::array > *outGradInput, Scalar alpha=1.0)
 Back propagation gradients through MCMC. More...
 
Scalar ComputePhi (Scalar alpha)
 Computes the weighted sum of the log-partitions of prior, post and cav. More...
 
void UpdateFactor (af::array &n, std::map< std::string, af::array > grad_cav, Scalar alpha, Scalar decay=0)
 Update step of PowerEP. More...
 
virtual void UpdateParameters () override
 Updates the parameters. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar >
 SparseGPBaseLayer (int numPoints, int numPseudos, int outputDim, int inputDim)
 Constructor. More...
 
virtual ~SparseGPBaseLayer ()
 Destructor. More...
 
virtual void ForwardPredictionPost (const af::array *mx, const af::array *vx, af::array &mout, af::array &vout) override
 Forward prediction of posterior function values. More...
 
virtual void SampleFromPost (const af::array &inX, af::array &outfsample) override
 Samples from posterior. More...
 
void ComputeKuu ()
 Calculates the kernel matrix of pseudo inputs. More...
 
void ComputeKfu (const af::array &inX)
 Calculates the kernel matrix of inputes and pseudo inputs. More...
 
af::array GetPseudoInputs ()
 Gets pseudo inputs. More...
 
virtual int GetNumParameters () override
 Gets number of parameters to be optimized. More...
 
virtual void SetParameters (const af::array &param) override
 Sets the parameters for each optimization iteration. More...
 
virtual af::array GetParameters () override
 Gets the parameters for each optimization iteration. More...
 
virtual void FixInducing (bool isfixed)
 Set fixation for inducing inputs. More...
 
virtual void UpdateParameters () override
 Updates the parameters. More...
 
virtual void InitParameters (af::array *X=nullptr) override
 Initializes the parameters. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::GPModels::GPBaseLayer< Scalar >
 GPBaseLayer (int numPoints, int outputDim, int inputDim)
 Constructor. More...
 
virtual ~GPBaseLayer ()
 Destructor. More...
 
IKernel< Scalar > * GetKernel ()
 Gets the kernel function. More...
 
void SetKernel (IKernel< Scalar > *kern)
 Sets a kernel function. More...
 
virtual void InitParameters (af::array *X=nullptr)
 
virtual int GetNumParameters ()
 Gets number of parameters to be optimized. More...
 
virtual void SetParameters (const af::array &param)
 Sets the parameters for each optimization iteration. More...
 
virtual af::array GetParameters ()
 Gets the parameters for each optimization iteration. More...
 
virtual void ForwardPredictionPost (const af::array *mx, const af::array *vx, af::array &mout, af::array &vout)
 Forward prediction of posterior function values. More...
 
virtual void SampleFromPost (const af::array &inX, af::array &outfsample)
 Samples from posterior. More...
 
virtual void FixKernelParameters (bool isfixed)
 Sets fixation for hyperparameters. More...
 
virtual void UpdateParameters ()
 Updates the parameters. More...
 
virtual void SetDataSize (int length, int dimension) override
 Sets data size. More...
 
void SetStyles (std::map< std::string, Style< Scalar > > *styles)
 Sets the syles. More...
 
void SetLatentDimension (int q)
 Sets latent dimension. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::ILayer< Scalar >
 ILayer (LayerType type, int numPoints, int outputDim)
 Constructor. More...
 
virtual ~ILayer ()=default
 Destructor. More...
 
LayerType GetType ()
 Gets the layer type. More...
 
virtual int GetNumParameters ()=0
 Gets number of parameters to be optimized. More...
 
virtual void SetParameters (const af::array &param)=0
 Sets the parameters for each optimization iteration. More...
 
virtual af::array GetParameters ()=0
 Gets the parameters for each optimization iteration. More...
 
virtual void UpdateParameters ()=0
 Updates the parameters. More...
 
virtual void SetDataSize (int length, int dimension)
 Sets data size. More...
 

Protected Member Functions

 SGPLayer ()
 Default constructor. More...
 
void ForwardPredictionDeterministicPost (const af::array &mx, af::array *mout, af::array *vout) override
 Deterministic forward propagation through posterior. More...
 
void ForwardPredictionDeterministicCavity (af::array &outMout, af::array &outVout, af::array *kfuOut, const af::array &mx, Scalar alpha=1.0)
 Forward prediction through deterministic cavity. More...
 
void ForwardPredictionRandomCavity (af::array &mout, af::array &vout, af::array *psi1out, af::array *psi2out, const af::array &mx, const af::array &vx, PropagationMode mode, Scalar alpha=1.0)
 Forward prediction through random cavity. More...
 
void ForwardPredictionRandomCavityMM (af::array &mout, af::array &vout, af::array *psi1out, af::array *psi2out, const af::array &mx, const af::array &vx, Scalar alpha=1.0)
 Forward prediction random cavity moment matching. More...
 
virtual void ForwardPredictionRandomPost (const af::array &mx, const af::array &vx, af::array &mout, af::array &vout, PropagationMode mode=PropagationMode::MomentMatching) override
 Forward prediction through random posterior. More...
 
void ForwardPredictionRandomPostMM (const af::array &mx, const af::array &vx, af::array &mout, af::array &vout)
 Forward prediction through posterior via Moment Matching. More...
 
void ComputeCavity (Scalar alpha=1.0f)
 Calculates the cavity. More...
 
void ComputeCavityGradientU (af::array &dMucav, af::array &dSucav, af::array &out_dT1, af::array &out_dT2, af::array &out_dInvKuu, Scalar alpha=1.0f)
 Calculates gradient contributions of cavity distribution. More...
 
void ComputePosteriorGradientU (af::array &dMu, af::array &dSu, af::array &out_dT1, af::array &out_dT2, af::array &out_dInvKuu)
 Calculates the gradient contributions of posterior. More...
 
Scalar ComputePhiPrior ()
 Calculates energy contribution phi prior. More...
 
Scalar ComputePhiPosterior ()
 Calculates energy contribution phi posterior. More...
 
Scalar ComputePhiCavity ()
 Calculates energy contribution phi cavity. More...
 
- Protected Member Functions inherited from NeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar >
 SparseGPBaseLayer ()
 Default constructor. More...
 
virtual void ReinitParameters () override
 Reinitializes the parameters. More...
 
virtual void ForwardPredictionDeterministicPost (const af::array &mx, af::array *mout, af::array *vout)
 Deterministic forward propagation through posterior. More...
 
virtual void ForwardPredictionRandomPost (const af::array &mx, const af::array &vx, af::array &mout, af::array &vout, PropagationMode mode=PropagationMode::MomentMatching)
 Forward prediction through random posterior. More...
 
- Protected Member Functions inherited from NeuralEngine::MachineLearning::GPModels::GPBaseLayer< Scalar >
 GPBaseLayer ()
 Default constructor. More...
 
virtual void ReinitParameters ()
 Reinitializes the parameters. More...
 
- Protected Member Functions inherited from NeuralEngine::MachineLearning::ILayer< Scalar >
 ILayer ()
 Default constructor. More...
 

Protected Attributes

af::array afGamma
 
af::array afBeta
 
af::array afGammaHat
 
af::array afBetaHat
 
af::array afSuMuMu
 
af::array afBetaStochastic
 
af::array afBetaHatStochastic
 
af::array afMuHat
 
af::array afSuHat
 
af::array afInvSuHat
 
af::array afInvSuMuHat
 
af::array afSuMuMuHat
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar >
int ik
 
af::array afMu
 
af::array afSu
 
af::array afInvSu
 
af::array afInvSuMu
 
af::array T1
 
af::array T2
 
af::array T2_R
 
af::array afXu
 
af::array afKuu
 
af::array afInvKuu
 
af::array afKfu
 
bool isFixedInducing
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::GPModels::GPBaseLayer< Scalar >
int iq
 Latent dimension. More...
 
bool isFixedHypers
 
IKernel< Scalar > * kernel
 kernel function More...
 
std::map< std::string, Style< Scalar > > * mStyles
 style variable More...
 
Scalar JITTER
 for kernel matrix stability (positive definiteness) More...
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::ILayer< Scalar >
int iD
 data dimension More...
 
int iN
 data size More...
 
LayerType lType
 liklihood or gp layer More...
 
af::dtype m_dType
 floating point precision flag for af::array More...
 

Private Member Functions

template<class Archive >
void serialize (Archive &ar, unsigned int version)
 

Friends

class boost::serialization::access
 
template<typename >
class SDGPR
 

Detailed Description

template<typename Scalar>
class NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >

Sparse GP layer.

Holds all variables for FITC approximation and PEP. Defines a subset of \(\mathbf{X}\). Selects \(k\) inducing inputs \(\mathbf{X_u}\), computes the subset kernel matrix \(\mathbf{K_{uu}}\) and its inverse.

For more information see, Hmetal T, 05/05/2018.

Definition at line 39 of file FgAEPSparseGPLayer.h.

Constructor & Destructor Documentation

◆ SGPLayer() [1/2]

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::SGPLayer ( int  numPoints,
int  numPseudos,
int  outputDim,
int  inputDim 
)

Constructor.

, 15.05.2018.

Parameters
numPointsNumber of points.
numPseudosNumber of pseudo inputs.
outputDimThe output dimension.
inputDimThe input dimension.

◆ ~SGPLayer()

template<typename Scalar >
virtual NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::~SGPLayer ( )
virtual

Destructor.

, 15.05.2018.

◆ SGPLayer() [2/2]

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::SGPLayer ( )
inlineprotected

Default constructor.

Hmetal T, 02/07/2018.

Definition at line 191 of file FgAEPSparseGPLayer.h.

Member Function Documentation

◆ ForwardPredictionCavity()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionCavity ( af::array &  outMout,
af::array &  outVout,
af::array *  psi1out,
af::array *  psi2out,
const af::array &  mx,
const af::array *  vx = nullptr,
Scalar  alpha = 1.0,
PropagationMode  mode = PropagationMode::MomentMatching 
)

Forward prediction through cavity.


Computes new mean m^{
}_{f} and covariance V^{
}_{ff} function. From cavity distribution.

, 16.05.2018.

Parameters
mout[in,out] The m^{
}_{f}.
vout[in,out] The V^{
}_{ff}.
mx[in,out] The inputs mx.
vx[in,out] (Optional) If non-null, the variances vx.
alpha(Optional) the alpha, weighning for alpha-divergence.

◆ ForwardPredictionRandomCavityMC()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionRandomCavityMC ( af::array &  mout,
af::array &  vout,
af::array &  xout,
af::array &  eps,
const af::array &  mx,
const af::array &  vx,
Scalar  alpha = 1.0 
)

Forward prediction random cavity MCMC.

Hmetal T, 10/07/2019.

Parameters
mout[in,out] The mout.
vout[in,out] The vout.
mxThe mx.
vxThe vx.
alpha(Optional) the alpha.

◆ BackpropGradientsReg()

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::BackpropGradientsReg ( const af::array &  m,
const af::array &  v,
const af::array &  dlogZ_dm,
const af::array &  dlogZ_dv,
const af::array &  x,
std::map< std::string, af::array > *  outGrad_cav = nullptr,
Scalar  alpha = 1.0 
)

Back propagation gradients through usual GP regression task.

Hmetal T, 11/07/2019.

Parameters
outGrad_hyper[in,out] Gradient of hypers.
Parameters
mCavity prediction mean from current layer.
vCavity prediction variance from current layer.
dlogZ_dmCavity prediction mean gradient.
dlogZ_dvCavity prediction variance gradient.
xThe training inputs.
outGrad_cav(Optional) [in,out] Gradient of cavity parameters.
alpha(Optional) Ratio.
Returns
Gradients of hyper parameters.

◆ BackpropGradientsMM()

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::BackpropGradientsMM ( const af::array &  m,
const af::array &  v,
const af::array &  dlogZ_dm,
const af::array &  dlogZ_dv,
const af::array &  psi1,
const af::array &  psi2,
const af::array &  mx,
const af::array &  vx,
std::map< std::string, af::array > *  outGrad_cav,
Scalar  alpha = 1.0 
)

Back propagation gradients through moment matching.

Hmetal T, 11/07/2019.

Parameters
mCavity prediction mean from current layer.
vCavity prediction variance from current layer.
dlogZ_dmCavity prediction mean gradient.
dlogZ_dvCavity prediction variance gradient.
psi1Cavity Psi1 statistics.
psi2Cavity Psi2 statistics.
mxCavity prediction mean from lower layer.
vxCavity prediction variance from lower layer.
outGrad_hyper[in,out] Gradient of hypers.
outGrad_cav[in,out] Gradient of cavity parameters.
alpha(Optional) Ratio.

◆ BackpropGradientsMC()

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::BackpropGradientsMC ( const af::array &  m,
const af::array &  v,
const af::array &  eps,
const af::array &  dlogZ_dm,
const af::array &  dlogZ_dv,
const af::array &  x,
std::map< std::string, af::array > *  outGradInput,
Scalar  alpha = 1.0 
)

Back propagation gradients through MCMC.

Hmetal T, 11/07/2019.

Parameters
mCavity prediction mean from current layer.
vCavity prediction variance from current layer.
dlogZ_dmCavity prediction mean gradient.
dlogZ_dvCavity prediction variance gradient.
xThe training inputs.
outGrad_cav[in,out] (Optional) Gradient of cavity parameters.
alpha(Optional) Ratio.
Returns
Gradients of hyper parameters.

◆ ComputePhi()

template<typename Scalar >
Scalar NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputePhi ( Scalar  alpha)

Computes the weighted sum of the log-partitions of prior, post and cav.

, 25.06.2018.

Parameters
alphaThe alpha.
Returns
The calculated phi.

◆ UpdateFactor()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::UpdateFactor ( af::array &  n,
std::map< std::string, af::array >  grad_cav,
Scalar  alpha,
Scalar  decay = 0 
)

Update step of PowerEP.


Hmetal T, 08/06/2018. 
Parameters
n[in,out] The indexes to compute.
grad_cav[in,out] The gradient of the cavity functions m_f^{
} and V_{ff}^{\m}.
alphaThe alpha.
decay(Optional) the decay.

◆ UpdateParameters()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::UpdateParameters ( )
overridevirtual

Updates the parameters.

, 26.06.2018.

Reimplemented from NeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar >.

◆ ForwardPredictionDeterministicPost()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionDeterministicPost ( const af::array &  mx,
af::array *  mout,
af::array *  vout 
)
overrideprotectedvirtual

Deterministic forward propagation through posterior.

Hmetal T, 01.04.2019.

Parameters
mx[in,out] The inputs mx.
mout[in,out] The m^{
}_{f}.
vout[in,out] The V^{
}_{ff}.

Reimplemented from NeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar >.

◆ ForwardPredictionDeterministicCavity()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionDeterministicCavity ( af::array &  outMout,
af::array &  outVout,
af::array *  kfuOut,
const af::array &  mx,
Scalar  alpha = 1.0 
)
protected

Forward prediction through deterministic cavity.


Projection step. Computation of cavity posterior mean and covariance function,

$$m^{
}_{f} = K_{fu}K_{uu}^{−1}T^{
, −1}_{2, u}T^{
}_{1, u},$$

$$V{
}_{ff} = K_{ff} − Q_{ff} + K_{fu}K^{−1}_{uu}T^{
, −1}_{2, u}K^{−1}_{uu}K_{uf}.$$

, 16.05.2018.

Parameters
mout[in,out] The m^{
}_{f}.
vout[in,out] The V^{
}_{ff}.
idx[in,out] indexes of points to be removed from distribution.
mx[in,out] The inputs mx.
alpha(Optional) the alpha, weighning for alpha-divergence.

◆ ForwardPredictionRandomCavity()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionRandomCavity ( af::array &  mout,
af::array &  vout,
af::array *  psi1out,
af::array *  psi2out,
const af::array &  mx,
const af::array &  vx,
PropagationMode  mode,
Scalar  alpha = 1.0 
)
protected

Forward prediction through random cavity.

, 16.05.2018.

Parameters
idx[in,out] indexes of points to be removed from distribution.
mout[in,out] The m^{
}_{f}.
vout[in,out] The V^{
}_{ff}.
mxThe inputs mx.
vxIf non-null, the variances vx.
modePropagation mode.
parameter6(Optional) the alpha, ratio for alpha-divergence.

◆ ForwardPredictionRandomCavityMM()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionRandomCavityMM ( af::array &  mout,
af::array &  vout,
af::array *  psi1out,
af::array *  psi2out,
const af::array &  mx,
const af::array &  vx,
Scalar  alpha = 1.0 
)
protected

Forward prediction random cavity moment matching.

Hmetal T, 10/07/2019.

Parameters
mout[in,out] The mout.
vout[in,out] The vout.
mxThe mx.
vxThe vx.
alpha(Optional) the alpha.

◆ ForwardPredictionRandomPost()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionRandomPost ( const af::array &  mx,
const af::array &  vx,
af::array &  mout,
af::array &  vout,
PropagationMode  mode = PropagationMode::MomentMatching 
)
overrideprotectedvirtual

Forward prediction through random posterior.

, 16.05.2018.

Parameters
mxThe inputs mx.
vxIf non-null, the variances vx.
mout[in,out] The m^{
}_{f}.
vout[in,out] The V^{
}_{ff}.
mode(Optional) Propagation mode.

Reimplemented from NeuralEngine::MachineLearning::GPModels::SparseGPBaseLayer< Scalar >.

◆ ForwardPredictionRandomPostMM()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ForwardPredictionRandomPostMM ( const af::array &  mx,
const af::array &  vx,
af::array &  mout,
af::array &  vout 
)
protected

Forward prediction through posterior via Moment Matching.

Hmetal T, 17/09/2019.

Parameters
mxThe inputs mx.
vxThe input variances vx.
mout[in,out] The mout.
vout[in,out] The vout.

◆ ComputeCavity()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputeCavity ( Scalar  alpha = 1.0f)
protected

Calculates the cavity.

Deletion step: The cavity for data point n, q^{
}(f) ∝ q^∗(f)/t^α_n(u), has a similar form to the posterior, but the natural parameters are modified by deletion,

T^\n_{1, u} = T_{1, u} − αT_{1, n}

    and

T^\n_{2, u} = T_{2, u} − αT_{2, n},

for yielding new mean \^{\mathbf{m_u}} and covariance function \^{\mathbf{S_u} for cavity distribution.

, 16.05.2018.

Parameters
alpha(Optional) the alpha, weighning for alpha-divergence.

◆ ComputeCavityGradientU()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputeCavityGradientU ( af::array &  dMucav,
af::array &  dSucav,
af::array &  out_dT1,
af::array &  out_dT2,
af::array &  out_dInvKuu,
Scalar  alpha = 1.0f 
)
protected

Calculates gradient contributions of cavity distribution.

, 22.06.2018.

Parameters
dMucav[in,out] The gradient mucav.
dSucav[in,out] The gradient sucav.
out_dT1[in,out] The gradient of natural parameter 1.
out_dT2[in,out] The gradient of natural parameter 2.
out_dInvKuu[in,out] The gradient of inverse Kuu.
alpha(Optional) the alpha.

◆ ComputePosteriorGradientU()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputePosteriorGradientU ( af::array &  dMu,
af::array &  dSu,
af::array &  out_dT1,
af::array &  out_dT2,
af::array &  out_dInvKuu 
)
protected

Calculates the gradient contributions of posterior.

, 25.06.2018.

Parameters
dMu[in,out] The gradient mu.
dSu[in,out] The gradient su.
out_dT1[in,out] The gradient of natural parameter 1.
out_dT2[in,out] The gradient of natural parameter 2.

◆ ComputePhiPrior()

template<typename Scalar >
Scalar NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputePhiPrior ( )
protected

Calculates energy contribution phi prior.

, 26.06.2018.

Returns
The calculated phi prior.

◆ ComputePhiPosterior()

template<typename Scalar >
Scalar NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputePhiPosterior ( )
protected

Calculates energy contribution phi posterior.

, 26.06.2018.

Returns
The calculated phi posterior.

◆ ComputePhiCavity()

template<typename Scalar >
Scalar NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::ComputePhiCavity ( )
protected

Calculates energy contribution phi cavity.

, 26.06.2018.

Returns
The calculated phi cavity.

◆ serialize()

template<typename Scalar >
template<class Archive >
void NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::serialize ( Archive &  ar,
unsigned int  version 
)
inlineprivate

Definition at line 366 of file FgAEPSparseGPLayer.h.

Friends And Related Function Documentation

◆ boost::serialization::access

template<typename Scalar >
friend class boost::serialization::access
friend

Definition at line 362 of file FgAEPSparseGPLayer.h.

◆ SDGPR

template<typename Scalar >
template<typename >
friend class SDGPR
friend

Definition at line 363 of file FgAEPSparseGPLayer.h.

Member Data Documentation

◆ afGamma

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afGamma
protected

Definition at line 346 of file FgAEPSparseGPLayer.h.

◆ afBeta

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afBeta
protected

Definition at line 347 of file FgAEPSparseGPLayer.h.

◆ afGammaHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afGammaHat
protected

Definition at line 348 of file FgAEPSparseGPLayer.h.

◆ afBetaHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afBetaHat
protected

Definition at line 349 of file FgAEPSparseGPLayer.h.

◆ afSuMuMu

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afSuMuMu
protected

Definition at line 350 of file FgAEPSparseGPLayer.h.

◆ afBetaStochastic

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afBetaStochastic
protected

Definition at line 351 of file FgAEPSparseGPLayer.h.

◆ afBetaHatStochastic

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afBetaHatStochastic
protected

Definition at line 352 of file FgAEPSparseGPLayer.h.

◆ afMuHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afMuHat
protected

Definition at line 355 of file FgAEPSparseGPLayer.h.

◆ afSuHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afSuHat
protected

Definition at line 356 of file FgAEPSparseGPLayer.h.

◆ afInvSuHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afInvSuHat
protected

Definition at line 357 of file FgAEPSparseGPLayer.h.

◆ afInvSuMuHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afInvSuMuHat
protected

Definition at line 358 of file FgAEPSparseGPLayer.h.

◆ afSuMuMuHat

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::AEP::SGPLayer< Scalar >::afSuMuMuHat
protected

Definition at line 359 of file FgAEPSparseGPLayer.h.


The documentation for this class was generated from the following file: