24 const bool is_self_covariance)
const override;
29 const std::vector<Real> & length_factor,
30 const Real sigma_f_squared,
31 const Real sigma_n_squared,
32 const bool is_self_covariance);
37 const std::string & hyper_param_name,
38 unsigned int ind)
const override;
43 const std::vector<Real> & length_factor,
44 const Real sigma_f_squared,
const std::vector< Real > & _length_factor
lengh factor () for the kernel, in vector form for multiple parameters
SquaredExponentialCovariance(const InputParameters ¶meters)
static const std::string K
static void computedKdlf(RealEigenMatrix &K, const RealEigenMatrix &x, const std::vector< Real > &length_factor, const Real sigma_f_squared, const int ind)
Computes dK/dlf for individual length factors.
Base class for covariance functions that are used in Gaussian Processes.
void computeCovarianceMatrix(RealEigenMatrix &K, const RealEigenMatrix &x, const RealEigenMatrix &xp, const bool is_self_covariance) const override
Generates the Covariance Matrix given two points in the parameter space.
const Real & _sigma_f_squared
signal variance (^2)
const std::vector< double > x
const Real & _sigma_n_squared
noise variance (^2)
Eigen::Matrix< Real, Eigen::Dynamic, Eigen::Dynamic > RealEigenMatrix
bool computedKdhyper(RealEigenMatrix &dKdhp, const RealEigenMatrix &x, const std::string &hyper_param_name, unsigned int ind) const override
Redirect dK/dhp for hyperparameter "hp".
DIE A HORRIBLE DEATH HERE typedef LIBMESH_DEFAULT_SCALAR_TYPE Real
const InputParameters & parameters() const
static void SquaredExponentialFunction(RealEigenMatrix &K, const RealEigenMatrix &x, const RealEigenMatrix &xp, const std::vector< Real > &length_factor, const Real sigma_f_squared, const Real sigma_n_squared, const bool is_self_covariance)
static InputParameters validParams()