| 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 |
| dim_ (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected |
| evaluate(const vectord &x) | bayesopt::ConditionalBayesProcess | inlinevirtual |
| evaluateKernelParams() | bayesopt::ConditionalBayesProcess | |
| fitSurrogateModel() | bayesopt::KernelRegressor | inlinevirtual |
| 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 |
| KernelRegressor(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | |
| mData (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected |
| mKernel (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected |
| 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 |
| negativeCrossValidation() | bayesopt::ConditionalBayesProcess | private |
| negativeLogLikelihood()=0 | bayesopt::ConditionalBayesProcess | protectedpure virtual |
| negativeTotalLogLikelihood()=0 | bayesopt::ConditionalBayesProcess | protectedpure virtual |
| 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()=0 | bayesopt::KernelRegressor | protectedpure virtual |
| prediction(const vectord &query)=0 | bayesopt::ConditionalBayesProcess | pure 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 |
| ~KernelRegressor() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | virtual |
| ~NonParametricProcess() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | virtual |
| ~RBOptimizable() (defined in bayesopt::RBOptimizable) | bayesopt::RBOptimizable | inlinevirtual |
1.8.13