9 #ifdef MOOSE_LIBTORCH_ENABLED 23 const torch::Tensor &
x,
24 const torch::Tensor & xp,
25 const bool is_self_covariance)
const override;
28 const torch::Tensor &
x,
29 const torch::Tensor & xp,
30 const torch::Tensor & length_factor,
31 const torch::Tensor & sigma_f_squared,
32 const torch::Tensor & sigma_n_squared,
33 const bool is_self_covariance);
37 const torch::Tensor &
x,
38 const std::string & hyper_param_name,
39 unsigned int ind)
const override;
43 const torch::Tensor &
x,
44 const torch::Tensor & length_factor,
45 const torch::Tensor & sigma_f_squared,
SquaredExponentialCovariance(const InputParameters ¶meters)
static const std::string K
const InputParameters & parameters() const
Base class for covariance functions that are used in Gaussian Processes.
static void computedKdlf(torch::Tensor &K, const torch::Tensor &x, const torch::Tensor &length_factor, const torch::Tensor &sigma_f_squared, const int ind)
Computes dK/dlf for individual length factors.
const torch::Tensor & _sigma_n_squared
noise variance (^2)
const std::vector< double > x
static void SquaredExponentialFunction(torch::Tensor &K, const torch::Tensor &x, const torch::Tensor &xp, const torch::Tensor &length_factor, const torch::Tensor &sigma_f_squared, const torch::Tensor &sigma_n_squared, const bool is_self_covariance)
void computeCovarianceMatrix(torch::Tensor &K, const torch::Tensor &x, const torch::Tensor &xp, const bool is_self_covariance) const override
Generates the Covariance Matrix given two points in the parameter space.
bool computedKdhyper(torch::Tensor &dKdhp, const torch::Tensor &x, const std::string &hyper_param_name, unsigned int ind) const override
Redirect dK/dhp for hyperparameter "hp".
const torch::Tensor & _length_factor
lengh factor () for the kernel, in vector form for multiple parameters
const torch::Tensor & _sigma_f_squared
signal variance (^2)
static InputParameters validParams()