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


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

#include <FgDeepGPBaseModel.h>

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

Public Member Functions

 DeepGPBaseModel (const af::array &Y, HiddenLayerDescription hiddenLayerdescription, LogLikType lType=LogLikType::Gaussian)
 Constructor. More...
 
 DeepGPBaseModel (const af::array &Y, std::vector< HiddenLayerDescription > descriptions, LogLikType lType=LogLikType::Gaussian)
 Constructor. More...
 
 DeepGPBaseModel ()
 Default Constructor. More...
 
virtual ~DeepGPBaseModel ()
 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...
 
virtual int GetNumParameters () override
 Gets number of parameters. More...
 
virtual int GetNumLayers ()
 Gets number of GP layers. 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 std::vector< GPBaseLayer< Scalar > * > GetGPLayers ()
 Gets vector of GP layers. More...
 
virtual void FixKernelParameters (bool isfixed)
 Sets fixation for hyperparameters. 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...
 

Protected Attributes

int iNumLayer
 
std::vector< intvNumPseudosPerLayer
 
std::vector< intvSize
 
std::vector< GPBaseLayer< Scalar > * > gpLayer
 
- 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...
 

Private Member Functions

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

Friends

class boost::serialization::access
 
class AEP::SGPLayer< Scalar >
 
class PowerEP::SGPLayer< Scalar >
 

Additional Inherited Members

- Protected Member Functions inherited from NeuralEngine::MachineLearning::IModel< Scalar >
 IModel (int numData, int numDimension, ModelType type)
 Constructor. More...
 

Detailed Description

template<typename Scalar>
class NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< 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 50 of file FgDeepGPBaseModel.h.

Constructor & Destructor Documentation

◆ DeepGPBaseModel() [1/3]

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::DeepGPBaseModel ( const af::array &  Y,
HiddenLayerDescription  hiddenLayerdescription,
LogLikType  lType = LogLikType::Gaussian 
)

Constructor.

, 26.03.2018.

Parameters
YThe data af::array to process.
XThe training inputs.
hiddenLayerdescriptionThe description for one hidden layer.
lType(Optional) the loglik type.

◆ DeepGPBaseModel() [2/3]

template<typename Scalar >
NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::DeepGPBaseModel ( const af::array &  Y,
std::vector< HiddenLayerDescription descriptions,
LogLikType  lType = LogLikType::Gaussian 
)

Constructor.

, 26.03.2018.

Parameters
YThe data af::array to process.
XThe training inputs.
hiddenLayerdescriptionsThe hidden layer descriptions.
lType(Optional) the loglik type.

◆ DeepGPBaseModel() [3/3]

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

Default Constructor.

Hmetal T, 29.11.2017.

◆ ~DeepGPBaseModel()

Destructor.

, 23.04.2018.

Member Function Documentation

◆ PredictF()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::PredictF ( const af::array &  testInputs,
af::array &  mf,
af::array &  vf 
)
overridevirtual

Predict noise free functions values \(\mathbf{F}_*\).

Hmetal T, 05/05/2020.

Parameters
testInputsThe test inputs.
mf[in,out] mean of function values.
vf[in,out] The variance of function values.

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

◆ SampleY()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::SampleY ( const af::array  inputs,
int  numSamples,
af::array &  outFunctions 
)
overridevirtual

Generate function samples from posterior.

Hmetal T, 18/06/2019.

Parameters
outFunctions[in,out] The out functions.
inputsThe inputs.
numSamplesNumber of samples.

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

◆ GetNumParameters()

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

Gets number of parameters.

, 26.06.2018.

Returns
The number parameters.

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

◆ GetNumLayers()

template<typename Scalar >
virtual int NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::GetNumLayers ( )
virtual

Gets number of GP layers.

Hmetal T, 09/07/2019.

Returns
The number layers.

◆ SetParameters()

template<typename Scalar >
virtual void NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< 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 >.

◆ GetParameters()

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

Gets the parameters for each optimization iteration.

, 26.06.2018.

Parameters
paramThe parameter.

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

◆ UpdateParameters()

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

Updates the parameters.

, 26.06.2018.

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

◆ GetGPLayers()

template<typename Scalar >
virtual std::vector< GPBaseLayer< Scalar > * > NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::GetGPLayers ( )
virtual

Gets vector of GP layers.

HmetalT, 09/07/2019.

Returns
null if it fails, else the gp layers.

◆ FixKernelParameters()

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

Sets fixation for hyperparameters.

Hmetal T, 16/12/2019.

Parameters
isfixedTrue if isfixed.

◆ serialize()

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

Definition at line 192 of file FgDeepGPBaseModel.h.

Friends And Related Function Documentation

◆ boost::serialization::access

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

Definition at line 185 of file FgDeepGPBaseModel.h.

◆ AEP::SGPLayer< Scalar >

template<typename Scalar >
friend class AEP::SGPLayer< Scalar >
friend

Definition at line 185 of file FgDeepGPBaseModel.h.

◆ PowerEP::SGPLayer< Scalar >

template<typename Scalar >
friend class PowerEP::SGPLayer< Scalar >
friend

Definition at line 185 of file FgDeepGPBaseModel.h.

Member Data Documentation

◆ iNumLayer

template<typename Scalar >
int NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::iNumLayer
protected

Definition at line 177 of file FgDeepGPBaseModel.h.

◆ vNumPseudosPerLayer

template<typename Scalar >
std::vector<int> NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::vNumPseudosPerLayer
protected

Definition at line 179 of file FgDeepGPBaseModel.h.

◆ vSize

template<typename Scalar >
std::vector<int> NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::vSize
protected

Definition at line 180 of file FgDeepGPBaseModel.h.

◆ gpLayer

template<typename Scalar >
std::vector<GPBaseLayer<Scalar>*> NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >::gpLayer
protected

Definition at line 182 of file FgDeepGPBaseModel.h.


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