9#ifdef MOOSE_LIBTORCH_ENABLED
26 const torch::Tensor &
x,
27 const torch::Tensor & xp,
28 const bool is_self_covariance)
const override;
31 const torch::Tensor &
x,
32 const std::string & hyper_param_name,
33 unsigned int ind)
const override;
43 void computeBMatrix(torch::Tensor & Bmat,
const unsigned int exp_i)
const;
53 computeAGradient(torch::Tensor & grad,
const unsigned int exp_i,
const unsigned int index)
const;
63 const unsigned int exp_i,
64 const unsigned int index)
const;
const std::vector< double > x
Base class for covariance functions that are used in Gaussian Processes.
Covariance function for multi-output Gaussian Processes based on the linear model of coregionalizatio...
void computeAGradient(torch::Tensor &grad, const unsigned int exp_i, const unsigned int index) const
Computes the gradient of $B$ with respect to the entries in $a_i$ in the following expression: $B = \...
std::vector< const torch::Tensor * > _a_coeffs
The vectors in the $B = \sum_i a_i a_i^T + diag(lambda_i)$ expansion.
void computeBMatrix(torch::Tensor &Bmat, const unsigned int exp_i) const
Computes the covariance matrix for the outputs (using the latent coefficients) We use a $B = \sum_i a...
static InputParameters validParams()
std::vector< const torch::Tensor * > _lambdas
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 unsigned int _num_expansion_terms
The number of expansion terms in the output ovariance matrix.
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 sets of points in the parameter space.
void computeLambdaGradient(torch::Tensor &grad, const unsigned int exp_i, const unsigned int index) const
Computes the gradient of $B$ with respect to the entries in $lambda_i$ in the following expression: $...
const InputParameters & parameters() const