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


Base class with abstract and basic function definitions. All deep GP models will be derived from this class.
More...

#include <FgGPLVMBaseModel.h>

Inheritance diagram for NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >:
Collaboration diagram for NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >:

Public Member Functions

 GPLVMBaseModel (const af::array &Y, int latentDimension, Scalar priorMean=0.0, Scalar priorVariance=1.0, LogLikType lType=LogLikType::Gaussian, XInit emethod=XInit::pca)
 Constructor. More...
 
 GPLVMBaseModel ()
 Default constructor. More...
 
virtual ~GPLVMBaseModel ()
 Destructor. More...
 
virtual void 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) override
 Optimizes the model parameters for best fit. More...
 
virtual bool Init (af::array &mx)
 Initializes the model. More...
 
virtual bool Init () override
 Initializes the model. More...
 
virtual void PosteriorLatents (af::array &mx, af::array &vx)
 Get posterior distribution of latent variables /f$\mathbf{X}/f$. More...
 
virtual int GetNumParameters () override
 Gets number of parameters. 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 UpdateParameters () override
 Updates the parameters. More...
 
virtual void FixKernelParameters (bool isfixed)
 Sets fixation for hyperparameters. More...
 
virtual void FixInducing (bool isfixed)
 Set fixation for inducing inputs. More...
 
void FixLatents (bool isFixed)
 
af::array GetMeanGradient ()
 Gets prior mean gradient. More...
 
af::array GetVarGradient ()
 Gets prior variance gradient. More...
 
void SetPrior (const af::array mean, const af::array var)
 Sets the prior. More...
 
void SetPriorCavity (const af::array meanCav, const af::array varCav)
 Sets the cavity prior. More...
 
void SetLatentGradient (const af::array &dmParent, const af::array &dvParent)
 Sets latent gradient. More...
 
void SetLatentGradientCavity (const af::array &dmParent, const af::array &dvParent)
 Sets the latent cavity gradient. More...
 
int GetLatentDimension ()
 Gets latent dimension. More...
 
void SetBackConstraint (IBackconstraint< Scalar > *constraint)
 Sets a back-constraint. More...
 
IBackconstraint< Scalar > * GetBackConstraint ()
 Gets the back-constraint. More...
 
void SetStyles (std::map< std::string, Style< Scalar > > *styles)
 Sets the syles. More...
 
void AddStyle (Style< Scalar > style)
 Adds a style. More...
 
std::map< std::string, Style< Scalar > > * GetStyles ()
 Gets the styles. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
 GPBaseModel (const af::array &Y, LogLikType lType=LogLikType::Gaussian, ModelType mtype=ModelType::GPR)
 Constructor. More...
 
 GPBaseModel ()
 Default Constructor. More...
 
virtual ~GPBaseModel ()
 Destructor. More...
 
virtual void 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)
 Optimizes the model parameters for best fit. More...
 
virtual bool Init ()
 Initializes the model. More...
 
virtual void PredictF (const af::array &testInputs, af::array &mf, af::array &vf)
 Predict noise free functions values \(\mathbf{F}_*\). More...
 
virtual void PredictY (const af::array &testInputs, af::array &my, af::array &vy)
 Prediction of test outputs \(\mathbf{Y}_*\). More...
 
virtual void SampleY (const af::array inputs, int numSamples, af::array &outFunctions)
 Generate function samples from posterior. More...
 
virtual void AddData (const af::array Ytrain)
 Adds training data to the model. More...
 
af::array GetTrainingData ()
 Gets the training data set Y. More...
 
void SetTrainingData (af::array &data)
 Sets training data Y. More...
 
virtual int GetNumParameters ()
 Gets number of parameters. 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 UpdateParameters ()
 Updates the parameters. More...
 
virtual void FixLikelihoodParameters (bool isfixed)
 Sets the likelihood parameters to be fixed or not for optimization. More...
 
void SetSegments (af::array segments)
 Sets fixation for hyperparameters. More...
 
af::array GetSegments ()
 Gets the start index array for the sequences. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::IModel< Scalar >
virtual Scalar Function (const af::array &x, af::array &outGradient)
 Cost function the given x inputs. 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...
 
int GetDataLenght ()
 Gets data lenght. More...
 
int GetDataDimensionality ()
 Gets data dimensionality. More...
 
ModelType GetModelType ()
 Gets model type. More...
 
virtual void SetBatchSize (int size)
 Sets batch size. More...
 
int GetBatchSize ()
 Gets batch size. More...
 
void SetIndexes (af::array &indexes)
 Sets the batch indexes. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::GPNode< Scalar >
 GPNode ()
 Default constructor. More...
 
virtual ~GPNode ()
 Destructor. More...
 
int GetNumChildren () const
 Gets the number of children of this item. More...
 
int AttachChild (std::shared_ptr< GPNode< Scalar > > const &child)
 Attaches a child. More...
 
int DetachChild (std::shared_ptr< GPNode< Scalar > > const &child)
 Detaches a child. More...
 
std::shared_ptr< GPNode< Scalar > > DetachChildAt (int i)
 Detaches a child at index. More...
 
void DetachAllChildren ()
 Detach all children from this node. More...
 
std::shared_ptr< GPNode< Scalar > > SetChild (int i, std::shared_ptr< GPNode< Scalar > > const &child)
 Sets a child. More...
 
std::shared_ptr< GPNode< Scalar > > GetChild (int i)
 Gets a child at index. More...
 
GPNode< Scalar > * GetParent ()
 Access to the parent object, which is null for the root of the hierarchy. More...
 
void SetParent (GPNode< Scalar > *parent)
 
Access to the parent object. Node calls this during attach/detach of children. More...
 

Protected Member Functions

virtual af::array PosteriorGradientLatents (const af::array &dmx, const af::array &dvx)
 Posterior gradient of latent inputs /f$\mathbf{X}/f$. More...
 
virtual void UpdateParametersInternal ()
 Updates the internal parameters. More...
 
virtual af::array LatentGradient (const af::array &dm, const af::array &dv)
 Latent gradient. More...
 
- Protected Member Functions inherited from NeuralEngine::MachineLearning::IModel< Scalar >
 IModel (int numData, int numDimension, ModelType type)
 Constructor. More...
 

Protected Attributes

int iq
 latent dimension More...
 
Scalar dPriorMean
 prior mean More...
 
Scalar dPriorVariance
 prior variance More...
 
Scalar dPriorX1
 prior /f$x_1/f$ More...
 
Scalar dPriorX2
 prior /f$x_2/f$ More...
 
af::array afFactorX1
 natural parameter factor 1 for latent variable More...
 
af::array afFactorX2
 natural parameter factor 2 for latent variable More...
 
af::array afPosteriorX1
 posterior natural parameter 1 for latent variable More...
 
af::array afPosteriorX2
 posterior natural parameter 2 for latent variable More...
 
af::array afPriorMean
 prior mean for hiersrchy mode More...
 
af::array afPriorVariance
 prior variance for hiersrchy mode More...
 
af::array afPriorMeanCav
 prior mean for hiersrchy mode More...
 
af::array afPriorVarianceCav
 prior variance for hiersrchy mode More...
 
af::array afPriorX1
 prior /f$x_1/f$ More...
 
af::array afPriorX2
 prior /f$x_2/f$ More...
 
af::array afPriorX1Cav
 prior /f$x_1/f$ More...
 
af::array afPriorX2Cav
 prior /f$x_2/f$ More...
 
af::array afGradMean
 prior mean gradient for hiersrchy mode More...
 
af::array afGradVariance
 prior variance gradient for hiersrchy mode More...
 
af::array afGradMeanCav
 prior mean gradient for hiersrchy mode More...
 
af::array afGradVarianceCav
 prior variance gradient for hiersrchy mode More...
 
af::array afLatentGradientX
 top down gradient More...
 
XInit eEmMethod
 
IBackconstraint< Scalar > * backConst
 back-constraint More...
 
std::map< std::string, Style< Scalar > > * mStyles
 style variable More...
 
bool bIsLatetsFixed
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >
bool bInit
 check if model is initialized More...
 
af::array afY
 training dataset, mean substracted More...
 
af::array afBias
 the bias More...
 
af::array afSegments
 Index of starting positions for all trials. More...
 
LikelihoodBaseLayer< Scalar > * likLayer
 liklihood layer More...
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::IModel< Scalar >
ModelType mType
 
int iN
 dataset length More...
 
int iD
 dataset dimension More...
 
int iBatchSize
 size of the batch More...
 
af::array afIndexes
 indexes of /f$\mathbf{X}/f$ for batch learning More...
 
af::dtype m_dType
 floating point precision flag for af::array More...
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::GPNode< Scalar >
std::vector< std::shared_ptr< GPNode< Scalar > > > mChild
 
GPNode< Scalar > * mParent
 

Private Member Functions

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

Friends

class boost::serialization::access
 
class KBR< Scalar >
 
class PTC< Scalar >
 

Detailed Description

template<typename Scalar>
class NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >


Base class with abstract and basic function definitions. All deep GP models will be derived from this class.

HmetalT, 26.10.2017.

Definition at line 294 of file FgGPLVMBaseModel.h.

Constructor & Destructor Documentation

◆ GPLVMBaseModel() [1/2]

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GPLVMBaseModel ( const af::array &  Y,
int  latentDimension,
Scalar  priorMean = 0.0,
Scalar  priorVariance = 1.0,
LogLikType  lType = LogLikType::Gaussian,
XInit  emethod = XInit::pca 
)

Constructor.

, 26.03.2018.

Parameters
YThe data af::array to process.
latentDimensionThe training inputs.
priorMean(Optional) The description for one hidden layer.
priorVariance(Optional) The prior variance.
numInducing(Optional) Number of inducings.
lType(Optional) the loglik type.
emethod(Optional) The emethod.

◆ GPLVMBaseModel() [2/2]

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GPLVMBaseModel ( )

Default constructor.

Hmetal T, 31/03/2020.

◆ ~GPLVMBaseModel()

Destructor.

, 15.05.2018.

Member Function Documentation

◆ Optimise()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::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 
)
overridevirtual

Optimizes the model parameters for best fit.

Hmetal T, 29.11.2017.

Parameters
method(Optional) the optimization method.
tol(Optional) the tolerance.
reinit_hypers(Optional) true to re hypers.
maxiter(Optional) max iterations.
mb_size(Optional) batch size.
LSType(Optional) linesearch type.
disp(Optional) true to disp.

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

◆ Init() [1/2]

template<typename Scalar >
virtual bool NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::Init ( af::array &  mx)
virtual

Initializes the model.

Hmetal T, 29.11.2017.

Returns
true if it succeeds, false if it fails.

◆ Init() [2/2]

template<typename Scalar >
virtual bool NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::Init ( )
overridevirtual

Initializes the model.

Hmetal T, 29.11.2017.

Returns
true if it succeeds, false if it fails.

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

Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >.

◆ PosteriorLatents()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::PosteriorLatents ( af::array &  mx,
af::array &  vx 
)
virtual

Get posterior distribution of latent variables /f$\mathbf{X}/f$.

Hmetal T, 09/12/2019.

Parameters
indexIndex of selected inputs.
mx[in,out] The mean.
vx[in,out] The variance.

◆ GetNumParameters()

template<typename Scalar >
virtual int NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GetNumParameters ( )
overridevirtual

◆ SetParameters()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetParameters ( const af::array &  param)
overridevirtual

Sets the parameters for each optimization iteration.

, 26.06.2018.

Parameters
paramThe parameter.

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

Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >.

◆ GetParameters()

template<typename Scalar >
virtual af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GetParameters ( )
overridevirtual

Gets the parameters for each optimization iteration.

, 26.06.2018.

Parameters
paramThe parameter.

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

Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >.

◆ UpdateParameters()

◆ FixKernelParameters()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::FixKernelParameters ( bool  isfixed)
virtual

Sets fixation for hyperparameters.

Hmetal T, 16/12/2019.

Parameters
isfixedTrue if isfixed.

Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >.

◆ FixInducing()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::FixInducing ( bool  isfixed)
virtual

Set fixation for inducing inputs.

Hmetal T, 16/12/2019.

Parameters
isfixedTrue if isfixed.

Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >.

◆ GetMeanGradient()

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GetMeanGradient ( )

Gets prior mean gradient.

Hmetal T, 31/08/2020.

Returns
The mean gradient.

◆ GetVarGradient()

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GetVarGradient ( )

Gets prior variance gradient.

Hmetal T, 31/08/2020.

Returns
The variable gradient.

◆ SetPrior()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetPrior ( const af::array  mean,
const af::array  var 
)

Sets the prior.

Hmetal T, 01/09/2020.

Parameters
meanThe mean.
varThe variable.

◆ SetPriorCavity()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetPriorCavity ( const af::array  meanCav,
const af::array  varCav 
)

Sets the cavity prior.

Hmetal T, 20/04/2021.

Parameters
meanCavThe mean cav.
varCavThe variable cav.

◆ SetLatentGradient()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetLatentGradient ( const af::array &  dmParent,
const af::array &  dvParent 
)

Sets latent gradient.

Hmetal T, 20/04/2021.

Parameters
dmParentThe dm parent.
dvParentThe dv parent.

◆ SetLatentGradientCavity()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetLatentGradientCavity ( const af::array &  dmParent,
const af::array &  dvParent 
)

Sets the latent cavity gradient.

Hmetal T, 20/04/2021.

Parameters
dmParentThe dm parent.
dvParentThe dv parent.

◆ GetLatentDimension()

template<typename Scalar >
int NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GetLatentDimension ( )

Gets latent dimension.

Hmetal T, 01/09/2020.

Returns
The latent dimension.

◆ SetBackConstraint()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetBackConstraint ( IBackconstraint< Scalar > *  constraint)

Sets a back-constraint.

Hmetal T, 17/09/2020.

Parameters
constraintThe constraint.

◆ GetBackConstraint()

Gets the back-constraint.

Hmetal T, 17/09/2020.

Returns
Null if it fails, else the back constraint.

◆ SetStyles()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::SetStyles ( std::map< std::string, Style< Scalar > > *  styles)

Sets the syles.

Hmetal T, 25/09/2020.

Parameters
styles[in,out] If non-null, the styles.

◆ AddStyle()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::AddStyle ( Style< Scalar style)

Adds a style.

Hmetal T, 02/11/2020.

Parameters
stylesThe styles.

◆ GetStyles()

template<typename Scalar >
std::map< std::string, Style< Scalar > > * NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::GetStyles ( )

Gets the styles.

Hmetal T, 25/09/2020.

Returns
Null if it fails, else the styles.

◆ PosteriorGradientLatents()

template<typename Scalar >
virtual af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::PosteriorGradientLatents ( const af::array &  dmx,
const af::array &  dvx 
)
protectedvirtual

Posterior gradient of latent inputs /f$\mathbf{X}/f$.

Hmetal T, 09/12/2019.

Parameters
dmx[in,out] The gradient of the mean.
dvx[in,out] The the gradient of the variance.

◆ UpdateParametersInternal()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::UpdateParametersInternal ( )
protectedvirtual

Updates the internal parameters.

Hmetal T, 23/03/2020.

◆ LatentGradient()

template<typename Scalar >
virtual af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::LatentGradient ( const af::array &  dm,
const af::array &  dv 
)
protectedvirtual

Latent gradient.

Hmetal T, 20/04/2021.

Returns
An af::array.

◆ serialize()

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

Definition at line 615 of file FgGPLVMBaseModel.h.

Friends And Related Function Documentation

◆ boost::serialization::access

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

Definition at line 610 of file FgGPLVMBaseModel.h.

◆ KBR< Scalar >

template<typename Scalar >
friend class KBR< Scalar >
friend

Definition at line 610 of file FgGPLVMBaseModel.h.

◆ PTC< Scalar >

template<typename Scalar >
friend class PTC< Scalar >
friend

Definition at line 610 of file FgGPLVMBaseModel.h.

Member Data Documentation

◆ iq

latent dimension

Definition at line 575 of file FgGPLVMBaseModel.h.

◆ dPriorMean

template<typename Scalar >
Scalar NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::dPriorMean
protected

prior mean

Definition at line 577 of file FgGPLVMBaseModel.h.

◆ dPriorVariance

template<typename Scalar >
Scalar NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::dPriorVariance
protected

prior variance

Definition at line 578 of file FgGPLVMBaseModel.h.

◆ dPriorX1

prior /f$x_1/f$

Definition at line 579 of file FgGPLVMBaseModel.h.

◆ dPriorX2

prior /f$x_2/f$

Definition at line 580 of file FgGPLVMBaseModel.h.

◆ afFactorX1

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afFactorX1
protected

natural parameter factor 1 for latent variable

Definition at line 582 of file FgGPLVMBaseModel.h.

◆ afFactorX2

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afFactorX2
protected

natural parameter factor 2 for latent variable

Definition at line 583 of file FgGPLVMBaseModel.h.

◆ afPosteriorX1

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPosteriorX1
protected

posterior natural parameter 1 for latent variable

Definition at line 584 of file FgGPLVMBaseModel.h.

◆ afPosteriorX2

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPosteriorX2
protected

posterior natural parameter 2 for latent variable

Definition at line 585 of file FgGPLVMBaseModel.h.

◆ afPriorMean

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorMean
protected

prior mean for hiersrchy mode

Definition at line 587 of file FgGPLVMBaseModel.h.

◆ afPriorVariance

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorVariance
protected

prior variance for hiersrchy mode

Definition at line 588 of file FgGPLVMBaseModel.h.

◆ afPriorMeanCav

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorMeanCav
protected

prior mean for hiersrchy mode

Definition at line 589 of file FgGPLVMBaseModel.h.

◆ afPriorVarianceCav

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorVarianceCav
protected

prior variance for hiersrchy mode

Definition at line 590 of file FgGPLVMBaseModel.h.

◆ afPriorX1

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorX1
protected

prior /f$x_1/f$

Definition at line 591 of file FgGPLVMBaseModel.h.

◆ afPriorX2

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorX2
protected

prior /f$x_2/f$

Definition at line 592 of file FgGPLVMBaseModel.h.

◆ afPriorX1Cav

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorX1Cav
protected

prior /f$x_1/f$

Definition at line 593 of file FgGPLVMBaseModel.h.

◆ afPriorX2Cav

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afPriorX2Cav
protected

prior /f$x_2/f$

Definition at line 594 of file FgGPLVMBaseModel.h.

◆ afGradMean

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afGradMean
protected

prior mean gradient for hiersrchy mode

Definition at line 595 of file FgGPLVMBaseModel.h.

◆ afGradVariance

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afGradVariance
protected

prior variance gradient for hiersrchy mode

Definition at line 596 of file FgGPLVMBaseModel.h.

◆ afGradMeanCav

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afGradMeanCav
protected

prior mean gradient for hiersrchy mode

Definition at line 597 of file FgGPLVMBaseModel.h.

◆ afGradVarianceCav

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afGradVarianceCav
protected

prior variance gradient for hiersrchy mode

Definition at line 598 of file FgGPLVMBaseModel.h.

◆ afLatentGradientX

template<typename Scalar >
af::array NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::afLatentGradientX
protected

top down gradient

Definition at line 600 of file FgGPLVMBaseModel.h.

◆ eEmMethod

template<typename Scalar >
XInit NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::eEmMethod
protected

Definition at line 602 of file FgGPLVMBaseModel.h.

◆ backConst

back-constraint

Definition at line 604 of file FgGPLVMBaseModel.h.

◆ mStyles

template<typename Scalar >
std::map<std::string, Style<Scalar> >* NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::mStyles
protected

style variable

Definition at line 605 of file FgGPLVMBaseModel.h.

◆ bIsLatetsFixed

template<typename Scalar >
bool NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >::bIsLatetsFixed
protected

Definition at line 607 of file FgGPLVMBaseModel.h.


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