| addObservations(const MatrixType &x, VectorType &y, VectorType &error) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| addObservations(const MatrixType &x, const VectorType &y) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| computePosterior(const MatrixType &input) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| GaussianProcess(Kernel kernel, double constantPriorMean=0) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| GaussianProcess(Kernel kernel, std::function< double(VectorType)> priorMeanFunction) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getKernel() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getObservedCovariance() const | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getObservedInputs() const | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getObservedValueErrors() const | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getObservedValues() const | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getPosteriorCopy() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getPosteriorCovariance() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getPosteriorMean() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getPriorCopy() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getPriorMeanFunction() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| getSqrtPosteriorCovariance() | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| sample(const MatrixType &input, hops::RandomNumberGenerator &randomNumberGenerator, size_t &maxElement) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| sample(hops::RandomNumberGenerator &randomNumberGenerator, size_t &maxElement) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| sample(const MatrixType &x, hops::RandomNumberGenerator &randomNumberGenerator) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| sample(hops::RandomNumberGenerator &randomNumberGenerator) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| setKernelSigma(double m_sigma) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| updateObservations(const MatrixType &x, const VectorType &y, const VectorType &error, bool isUnique=false) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
| updateObservations(const MatrixType &x, const VectorType &y) | hops::GaussianProcess< MatrixType, VectorType, Kernel > | inline |
