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


Sparse Gaussian Process Regression (SGPR) with optimization through Power Expectation Propagation (PEP). More...

#include <FgPEPSparseGPR.h>

Inheritance diagram for NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR< Scalar >:
Collaboration diagram for NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR< Scalar >:

Public Member Functions

 SGPR (const af::array &Y, const af::array &X, int numInducing=20, LogLikType lType=LogLikType::Gaussian)
 Constructor. More...
 
void Inference (Scalar alpha=1.0, int numIter=10, bool parallelUpdate=false, Scalar decay=0.5)
 Inference. More...
 
- Public Member Functions inherited from NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
 SparseGPBaseModel (const af::array &Y, const af::array &X, int numInducing=200, LogLikType lType=LogLikType::Gaussian)
 Constructor. More...
 
 SparseGPBaseModel ()
 Default constructor. More...
 
virtual ~SparseGPBaseModel ()
 Destructor. More...
 
virtual void PredictF (const af::array &testInputs, af::array &mf, af::array &vf) override
 Predict noise free functions values \(\mathbf{F}_*\). More...
 
virtual void SampleY (const af::array inputs, int numSamples, af::array &outFunctions) override
 Generate function samples from posterior. More...
 
af::array GetTrainingInputs ()
 Gets training inputs X. More...
 
void SetTrainingInputs (af::array &inputs)
 Gets training inputs X. More...
 
af::array GetPseudoInputs ()
 Gets pseudo inputs. More...
 
virtual bool Init () override
 Initializes the model. 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...
 
SparseGPBaseLayer< Scalar > * GetGPLayer ()
 Gets the gp layer. 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...
 

Private Member Functions

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

Friends

class boost::serialization::access
 

Additional Inherited Members

- Protected Member Functions inherited from NeuralEngine::MachineLearning::IModel< Scalar >
 IModel (int numData, int numDimension, ModelType type)
 Constructor. More...
 
- Protected Attributes inherited from NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >
int ik
 number of inducing inputs More...
 
int iq
 latent dimension More...
 
af::array afX
 training inputs More...
 
SparseGPBaseLayer< Scalar > * gpLayer
 gp layer More...
 
- 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...
 

Detailed Description

template<typename Scalar>
class NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR< Scalar >


Sparse Gaussian Process Regression (SGPR) with optimization through Power Expectation Propagation (PEP).

PEP, as an extention of EP, minimizes a α-divergence. It is equivalent to minimizing KL-divergence with the exact distribution raised to a power. PEP can be seen as a hybrid between the regular EP (α = 1) and variational inference (Variational Free Energy (VFE)) (α = 0).

For more information see, https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2004-149.pdf https://arxiv.org/pdf/1605.07066.pdf

, 28.02.2018.

Definition at line 44 of file FgPEPSparseGPR.h.

Constructor & Destructor Documentation

◆ SGPR()

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR< Scalar >::SGPR ( const af::array &  Y,
const af::array &  X,
int  numInducing = 20,
LogLikType  lType = LogLikType::Gaussian 
)

Constructor.

, 12.06.2018.

Parameters
YThe training data.
XThe training inputs.
numInducing(Optional) number of inducing inputs.
lType(Optional) likelihood type.

Member Function Documentation

◆ Inference()

template<typename Scalar >
void NeuralEngine::MachineLearning::GPModels::PowerEP::SGPR< Scalar >::Inference ( Scalar  alpha = 1.0,
int  numIter = 10,
bool  parallelUpdate = false,
Scalar  decay = 0.5 
)

Inference.


Running the PowerEP algorithm to optimize posterior.

, 12.06.2018.

Parameters
alpha(Optional) the alpha.
numIter(Optional) number of iterators.
parallelUpdate(Optional) true to parallel update.
decay(Optional) the decay.

◆ serialize()

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

Definition at line 83 of file FgPEPSparseGPR.h.

Friends And Related Function Documentation

◆ boost::serialization::access

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

Definition at line 80 of file FgPEPSparseGPR.h.


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