Base class with abstract and basic function definitions. All GP models will be derived from this class.
More...
#include <FgGPBaseModel.h>
Public Member Functions | |
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 ¶m) |
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... | |
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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 ¶m)=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 | |
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... | |
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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 | GaussLikLayer< Scalar > |
class | ProbitLikLayer< Scalar > |
Additional Inherited Members | |
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IModel (int numData, int numDimension, ModelType type) | |
Constructor. More... | |
Base class with abstract and basic function definitions. All GP models will be derived from this class.
HmetalT, 26.10.2017.
Definition at line 39 of file FgGPBaseModel.h.
NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >::GPBaseModel | ( | const af::array & | Y, |
LogLikType | lType = LogLikType::Gaussian , |
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ModelType | mtype = ModelType::GPR |
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) |
Constructor.
, 26.03.2018.
Y | The data af::array to process. |
NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >::GPBaseModel | ( | ) |
Default Constructor.
Hmetal T, 29.11.2017.
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virtual |
Destructor.
, 23.04.2018.
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virtual |
Optimizes the model parameters for best fit.
Hmetal T, 29.11.2017.
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 in NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >.
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virtual |
Initializes the model.
Hmetal T, 29.11.2017.
Reimplemented in NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Predict noise free functions values \(\mathbf{F}_*\).
Hmetal T, 05/05/2020.
testInputs | The test inputs. |
mf | [in,out] mean of function values. |
vf | [in,out] The variance of function values. |
Reimplemented in NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Prediction of test outputs \(\mathbf{Y}_*\).
, 12.06.2018.
my | [in,out] The posterior mean function. |
vy | [in,out] The posterior covariance function. |
testX | [in,out] The test inputs. |
Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Generate function samples from posterior.
Hmetal T, 18/06/2019.
outFunctions | [in,out] The out functions. |
inputs | The inputs. |
numSamples | Number of samples. |
Reimplemented in NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >.
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virtual |
Adds training data to the model.
Hmetal T, 29.11.2017.
Ytrain | [in,out] The training data. |
af::array NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >::GetTrainingData | ( | ) |
Gets the training data set Y.
, 26.03.2018.
void NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >::SetTrainingData | ( | af::array & | data | ) |
Sets training data Y.
, 27.03.2018.
data | [in,out] The data. |
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virtual |
Gets number of parameters.
, 26.06.2018.
Implements NeuralEngine::MachineLearning::IModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Sets the parameters for each optimization iteration.
, 26.06.2018.
param | The parameter. |
Implements NeuralEngine::MachineLearning::IModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Gets the parameters for each optimization iteration.
, 26.06.2018.
param | The parameter. |
Implements NeuralEngine::MachineLearning::IModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Updates the parameters.
, 26.06.2018.
Implements NeuralEngine::MachineLearning::IModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::DeepGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::GPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPLVMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPBaseModel< Scalar >, NeuralEngine::MachineLearning::GPModels::SparseGPLVMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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virtual |
Sets the likelihood parameters to be fixed or not for optimization.
Hmetal T, 17/12/2019.
isfixed | True if is fixed. |
void NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >::SetSegments | ( | af::array | segments | ) |
Sets fixation for hyperparameters.
Hmetal T, 16/12/2019.
isfixed | True if isfixed. |
Sets the start indexes array of each sequence.
Hmetal T, 18/07/2022.
segments | The segments. |
af::array NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >::GetSegments | ( | ) |
Gets the start index array for the sequences.
Hmetal T, 18/07/2022.
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inlineprivate |
Definition at line 252 of file FgGPBaseModel.h.
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friend |
Definition at line 246 of file FgGPBaseModel.h.
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friend |
Definition at line 246 of file FgGPBaseModel.h.
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friend |
Definition at line 246 of file FgGPBaseModel.h.
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protected |
check if model is initialized
Definition at line 233 of file FgGPBaseModel.h.
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protected |
training dataset, mean substracted
Definition at line 234 of file FgGPBaseModel.h.
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protected |
the bias
Definition at line 235 of file FgGPBaseModel.h.
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protected |
Index of starting positions for all trials.
Definition at line 236 of file FgGPBaseModel.h.
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protected |
liklihood layer
Definition at line 239 of file FgGPBaseModel.h.