Base class with abstract and basic function definitions. All GP state space models will be derived from this class.
More...
#include <FgGPStateSpaceBaseModel.h>


Public Member Functions | |
| GPSSBaseModel (const af::array &Y, int latentDimension, Scalar priorMean=0.0, Scalar priorVariance=1.0, af::array &xControl=af::array(), PropagationMode probMode=PropagationMode::MomentMatching, LogLikType lType=LogLikType::Gaussian, bool GPemission=true, bool controlToEmiss=true, XInit emethod=XInit::pca) | |
| Constructor. More... | |
| GPSSBaseModel () | |
| Default constructor. More... | |
| virtual | ~GPSSBaseModel () |
| 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 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 ¶m) override |
| Sets the parameters for each optimization iteration. More... | |
| virtual af::array | GetParameters () override |
| Gets the parameters for each optimization iteration. More... | |
| void | UpdateDynamicIndexes () |
| Updates the dynamic indexes. More... | |
| void | GetLatents (af::array &mx, af::array &vx) |
| virtual void | AddWindowData (af::array data) |
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 ¶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... | |
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 ¶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... | |
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 parameters. 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... | |
| int | iDControlEmiss |
| int | iDControlDyn |
| Scalar | dSn |
| bool | bGPemission |
| bool | bControlToEmiss |
| 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 | afXControl |
| af::array | afDynIndexes |
| 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 | afPriorMean |
| prior mean for hierarchy mode More... | |
| af::array | afPriorVariance |
| prior variance for hierarchy mode More... | |
| af::array | afPriorMeanCav |
| prior mean for hierarchy mode More... | |
| af::array | afPriorVarianceCav |
| prior variance for hierarchy mode 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 |
| PropagationMode | pMode |
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 |
Base class with abstract and basic function definitions. All GP state space models will be derived from this class.
HmetalT, 26.10.2017.
Definition at line 31 of file FgGPStateSpaceBaseModel.h.
| NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >::GPSSBaseModel | ( | const af::array & | Y, |
| int | latentDimension, | ||
| Scalar | priorMean = 0.0, |
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| Scalar | priorVariance = 1.0, |
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| af::array & | xControl = af::array(), |
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| PropagationMode | probMode = PropagationMode::MomentMatching, |
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| LogLikType | lType = LogLikType::Gaussian, |
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| bool | GPemission = true, |
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| bool | controlToEmiss = true, |
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| XInit | emethod = XInit::pca |
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| ) |
Constructor.
Hmetal T, 04/05/2020.
| Y | Observation data. |
| latentDimension | The latent dimension. |
| priorMean | (Optional) The prior mean. |
| priorVariance | (Optional) The prior variance. |
| numInducing | (Optional) Number of inducings inputs. |
| lType | (Optional) The likelihood type. |
| GPemission | (Optional) True to non-linear emission function. |
| emethod | (Optional) The embed method. |
| NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >::GPSSBaseModel | ( | ) |
Default constructor.
Hmetal T, 09/05/2022.
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virtual |
Destructor.
HmetalT, 05/05/2020.
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overridevirtual |
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 from NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >.
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virtual |
Initializes the model.
Hmetal T, 29.11.2017.
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virtual |
Get posterior distribution of latent variables /f$\mathbf{X}/f$.
Hmetal T, 09/12/2019.
| index | Index of selected inputs. |
| mx | [in,out] The mean. |
| vx | [in,out] The variance. |
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overridevirtual |
Gets number of parameters.
, 26.06.2018.
Reimplemented from NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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overridevirtual |
Sets the parameters for each optimization iteration.
, 26.06.2018.
| param | The parameter. |
Reimplemented from NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
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overridevirtual |
Gets the parameters for each optimization iteration.
, 26.06.2018.
| param | The parameter. |
Reimplemented from NeuralEngine::MachineLearning::GPModels::GPBaseModel< Scalar >.
Reimplemented in NeuralEngine::MachineLearning::GPModels::SparseDeepGPSSMBaseModel< Scalar >, and NeuralEngine::MachineLearning::GPModels::SparseGPSSMBaseModel< Scalar >.
| void NeuralEngine::MachineLearning::GPModels::GPSSBaseModel< Scalar >::UpdateDynamicIndexes | ( | ) |
Updates the dynamic indexes.
Hmetal T, 18/05/2022.
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protectedvirtual |
Posterior gradient of latent inputs /f$\mathbf{X}/f$.
Hmetal T, 09/12/2019.
| dmx | [in,out] The gradient of the mean. |
| dvx | [in,out] The the gradient of the variance. |
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protectedvirtual |
Updates the parameters.
, 26.06.2018.
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Definition at line 212 of file FgGPStateSpaceBaseModel.h.
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Definition at line 209 of file FgGPStateSpaceBaseModel.h.
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latent dimension
Definition at line 166 of file FgGPStateSpaceBaseModel.h.
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Definition at line 167 of file FgGPStateSpaceBaseModel.h.
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Definition at line 168 of file FgGPStateSpaceBaseModel.h.
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Definition at line 170 of file FgGPStateSpaceBaseModel.h.
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Definition at line 172 of file FgGPStateSpaceBaseModel.h.
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Definition at line 173 of file FgGPStateSpaceBaseModel.h.
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prior mean
Definition at line 175 of file FgGPStateSpaceBaseModel.h.
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prior variance
Definition at line 176 of file FgGPStateSpaceBaseModel.h.
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prior /f$x_1/f$
Definition at line 177 of file FgGPStateSpaceBaseModel.h.
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prior /f$x_2/f$
Definition at line 178 of file FgGPStateSpaceBaseModel.h.
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natural parameter factor 1 for latent variable
Definition at line 180 of file FgGPStateSpaceBaseModel.h.
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natural parameter factor 2 for latent variable
Definition at line 181 of file FgGPStateSpaceBaseModel.h.
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posterior natural parameter 1 for latent variable
Definition at line 182 of file FgGPStateSpaceBaseModel.h.
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posterior natural parameter 2 for latent variable
Definition at line 183 of file FgGPStateSpaceBaseModel.h.
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Definition at line 184 of file FgGPStateSpaceBaseModel.h.
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Definition at line 185 of file FgGPStateSpaceBaseModel.h.
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prior /f$x_1/f$
Definition at line 187 of file FgGPStateSpaceBaseModel.h.
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prior /f$x_2/f$
Definition at line 188 of file FgGPStateSpaceBaseModel.h.
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prior /f$x_1/f$
Definition at line 189 of file FgGPStateSpaceBaseModel.h.
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prior /f$x_2/f$
Definition at line 190 of file FgGPStateSpaceBaseModel.h.
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prior mean for hierarchy mode
Definition at line 191 of file FgGPStateSpaceBaseModel.h.
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prior variance for hierarchy mode
Definition at line 192 of file FgGPStateSpaceBaseModel.h.
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prior mean for hierarchy mode
Definition at line 193 of file FgGPStateSpaceBaseModel.h.
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prior variance for hierarchy mode
Definition at line 194 of file FgGPStateSpaceBaseModel.h.
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prior mean gradient for hiersrchy mode
Definition at line 195 of file FgGPStateSpaceBaseModel.h.
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prior variance gradient for hiersrchy mode
Definition at line 196 of file FgGPStateSpaceBaseModel.h.
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prior mean gradient for hiersrchy mode
Definition at line 197 of file FgGPStateSpaceBaseModel.h.
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prior variance gradient for hiersrchy mode
Definition at line 198 of file FgGPStateSpaceBaseModel.h.
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top down gradient
Definition at line 200 of file FgGPStateSpaceBaseModel.h.
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Definition at line 205 of file FgGPStateSpaceBaseModel.h.
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Definition at line 207 of file FgGPStateSpaceBaseModel.h.