bayesopt::GaussianProcessML Class Reference
Gaussian process with ML parameters. More...
#include <gaussian_process_ml.hpp>
Inheritance diagram for bayesopt::GaussianProcessML:
Collaboration diagram for bayesopt::GaussianProcessML:
Public Member Functions | |
| GaussianProcessML (size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng) | |
| ProbabilityDistribution * | prediction (const vectord &query) |
| Function that returns the prediction of the GP for a query point in the hypercube [0,1]. More... | |
| Public Member Functions inherited from bayesopt::HierarchicalGaussianProcess | |
| HierarchicalGaussianProcess (size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng) | |
| Public Member Functions inherited from bayesopt::ConditionalBayesProcess | |
| ConditionalBayesProcess (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) | |
| double | evaluate (const vectord &x) |
| Computes the score (eg:likelihood) of the kernel parameters. More... | |
| double | evaluateKernelParams () |
| Computes the score (eg:likelihood) of the current kernel parameters. More... | |
| Public Member Functions inherited from bayesopt::KernelRegressor | |
| KernelRegressor (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) | |
| void | fitSurrogateModel () |
| Computes the initial surrogate model and updates the kernel parameters estimation. More... | |
| void | updateSurrogateModel () |
| Sequential update of the surrogate model by adding a new row to the Kernel matrix, more precisely, to its Cholesky decomposition. More... | |
| double | getSignalVariance () |
| size_t | nHyperParameters () |
| vectord | getHyperParameters () |
| void | setHyperParameters (const vectord &theta) |
| Public Member Functions inherited from bayesopt::NonParametricProcess | |
| NonParametricProcess (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) | |
| double | getValueAtMinimum () |
| const Dataset * | getData () |
| double | getSignalVariance () |
Private Member Functions | |
| double | negativeLogLikelihood () |
| Computes the negative log likelihood and its gradient of the data. More... | |
| void | precomputePrediction () |
| Precompute some values of the prediction that do not depends on the query. | |
Private Attributes | |
| vectord | mWML |
| GP ML parameters. | |
| vectord | mAlphaF |
| Precomputed GP prediction operations. | |
| matrixd | mKF |
| matrixd | mL2 |
| GaussianDistribution * | d_ |
| Predictive distributions. | |
Additional Inherited Members | |
| Static Public Member Functions inherited from bayesopt::NonParametricProcess | |
| static NonParametricProcess * | create (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) |
| Factory model generator for surrogate models. More... | |
| Protected Member Functions inherited from bayesopt::HierarchicalGaussianProcess | |
| double | negativeTotalLogLikelihood () |
| Computes the negative log likelihood of the data for all the parameters. More... | |
| Protected Member Functions inherited from bayesopt::KernelRegressor | |
| void | computeCorrMatrix (matrixd &corrMatrix) |
| Computes the Correlation (Kernel or Gram) matrix. | |
| matrixd | computeCorrMatrix () |
| Computes the Correlation (Kernel or Gram) matrix. | |
| matrixd | computeDerivativeCorrMatrix (int dth_index) |
| Computes the derivative of the correlation matrix with respect to the dth hyperparameter. | |
| vectord | computeCrossCorrelation (const vectord &query) |
| double | computeSelfCorrelation (const vectord &query) |
| void | computeCholeskyCorrelation () |
| Computes the Cholesky decomposition of the Correlation matrix. | |
| Protected Attributes inherited from bayesopt::KernelRegressor | |
| matrixd | mL |
| Cholesky decomposition of the Correlation matrix. | |
| score_type | mScoreType |
| learning_type | mLearnType |
| bool | mLearnAll |
| KernelModel | mKernel |
| Protected Attributes inherited from bayesopt::NonParametricProcess | |
| const Dataset & | mData |
| double | mSigma |
| Signal variance. | |
| size_t | dim_ |
| MeanModel & | mMean |
Detailed Description
Gaussian process with ML parameters.
Definition at line 42 of file gaussian_process_ml.hpp.
Member Function Documentation
◆ negativeLogLikelihood()
|
privatevirtual |
Computes the negative log likelihood and its gradient of the data.
In this case, it is equivalent to the negativeTotalLogLikelihood
- Returns
- value negative log likelihood
Implements bayesopt::ConditionalBayesProcess.
Definition at line 49 of file gaussian_process_ml.cpp.
References bayesopt::HierarchicalGaussianProcess::negativeTotalLogLikelihood().
◆ prediction()
|
virtual |
Function that returns the prediction of the GP for a query point in the hypercube [0,1].
- Parameters
-
query in the hypercube [0,1] to evaluate the Gaussian process
- Returns
- pointer to the probability distribution.
Implements bayesopt::ConditionalBayesProcess.
Definition at line 56 of file gaussian_process_ml.cpp.
References bayesopt::KernelRegressor::mL, and bayesopt::NonParametricProcess::mSigma.
The documentation for this class was generated from the following files:
