Sparse Gaussian Process Latent Variable Model (SGPLVM) with optimization through Power Expectation Propagation (PEP).
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#include <FgPEPSparseGPLVM.h>


Private Member Functions | |
| template<class Archive > | |
| void | serialize (Archive &ar, unsigned int version) |
Private 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 ¶m) 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... | |
Private 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... | |
Private 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... | |
| IModel (int numData, int numDimension, ModelType type) | |
| Constructor. More... | |
Friends | |
| class | boost::serialization::access |
Additional Inherited Members | |
Private 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... | |
Private 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... | |
Private 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... | |
Sparse Gaussian Process Latent Variable Model (SGPLVM) 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
, 28.02.2018.
Definition at line 42 of file FgPEPSparseGPLVM.h.
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inlineprivate |
Definition at line 52 of file FgPEPSparseGPLVM.h.
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friend |
Definition at line 49 of file FgPEPSparseGPLVM.h.