computeCholeskyCorrelation() | bayesopt::KernelRegressor | protected |
computeCorrMatrix(matrixd &corrMatrix) | bayesopt::KernelRegressor | inlineprotected |
computeCorrMatrix() | bayesopt::KernelRegressor | inlineprotected |
computeCrossCorrelation(const vectord &query) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlineprotected |
computeDerivativeCorrMatrix(int dth_index) | bayesopt::KernelRegressor | protected |
computeSelfCorrelation(const vectord &query) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlineprotected |
ConditionalBayesProcess(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::ConditionalBayesProcess) | bayesopt::ConditionalBayesProcess | |
create(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) | bayesopt::NonParametricProcess | static |
d_ | bayesopt::GaussianProcessNormal | private |
dim_ (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected |
evaluate(const vectord &x) | bayesopt::ConditionalBayesProcess | inlinevirtual |
evaluateKernelParams() | bayesopt::ConditionalBayesProcess | |
fitSurrogateModel() | bayesopt::KernelRegressor | inlinevirtual |
GaussianProcessNormal(size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | |
getData() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | inline |
getHyperParameters() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlinevirtual |
getSignalVariance() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | |
getValueAtMinimum() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | inline |
HierarchicalGaussianProcess(size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::HierarchicalGaussianProcess) | bayesopt::HierarchicalGaussianProcess | |
KernelRegressor(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | |
mD (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private |
mData (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected |
mInvVarW (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private |
mKernel (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected |
mKF (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private |
mL | bayesopt::KernelRegressor | protected |
mLearnAll (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected |
mLearnType (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected |
mMean (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected |
mScoreType (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected |
mSigma | bayesopt::NonParametricProcess | protected |
mVf | bayesopt::GaussianProcessNormal | private |
mW0 (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private |
mWMap | bayesopt::GaussianProcessNormal | private |
negativeLogLikelihood() | bayesopt::GaussianProcessNormal | privatevirtual |
negativeTotalLogLikelihood() | bayesopt::HierarchicalGaussianProcess | protectedvirtual |
nHyperParameters() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlinevirtual |
NonParametricProcess(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | |
precomputePrediction() | bayesopt::GaussianProcessNormal | privatevirtual |
prediction(const vectord &query) | bayesopt::GaussianProcessNormal | virtual |
RBOptimizable() (defined in bayesopt::RBOptimizable) | bayesopt::RBOptimizable | inline |
setHyperParameters(const vectord &theta) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlinevirtual |
updateSurrogateModel() | bayesopt::KernelRegressor | virtual |
~ConditionalBayesProcess() (defined in bayesopt::ConditionalBayesProcess) | bayesopt::ConditionalBayesProcess | virtual |
~GaussianProcessNormal() (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | virtual |
~HierarchicalGaussianProcess() (defined in bayesopt::HierarchicalGaussianProcess) | bayesopt::HierarchicalGaussianProcess | inlinevirtual |
~KernelRegressor() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | virtual |
~NonParametricProcess() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | virtual |
~RBOptimizable() (defined in bayesopt::RBOptimizable) | bayesopt::RBOptimizable | inlinevirtual |