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ExponentialCovariance.h
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2 //* https://mooseframework.inl.gov
3 //*
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5 //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
6 //*
7 //* Licensed under LGPL 2.1, please see LICENSE for details
8 //* https://www.gnu.org/licenses/lgpl-2.1.html
9 #ifdef MOOSE_LIBTORCH_ENABLED
10 
11 #pragma once
12 
13 #include "CovarianceFunctionBase.h"
14 
16 {
17 public:
20 
22  void computeCovarianceMatrix(torch::Tensor & K,
23  const torch::Tensor & x,
24  const torch::Tensor & xp,
25  const bool is_self_covariance) const override;
26 
27  static void ExponentialFunction(torch::Tensor & K,
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 torch::Tensor & gamma,
34  const bool is_self_covariance);
35 
37  bool computedKdhyper(torch::Tensor & dKdhp,
38  const torch::Tensor & x,
39  const std::string & hyper_param_name,
40  unsigned int ind) const override;
41 
43  static void computedKdlf(torch::Tensor & K,
44  const torch::Tensor & x,
45  const torch::Tensor & length_factor,
46  const torch::Tensor & sigma_f_squared,
47  const torch::Tensor & gamma,
48  const int ind);
49 
50 private:
52  const torch::Tensor & _length_factor;
53 
55  const torch::Tensor & _sigma_f_squared;
56 
58  const torch::Tensor & _sigma_n_squared;
59 
61  const torch::Tensor & _gamma;
62 };
63 
64 #endif
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".
static const std::string K
Definition: NS.h:174
const InputParameters & parameters() const
Base class for covariance functions that are used in Gaussian Processes.
const torch::Tensor & _gamma
gamma exponential factor for use in kernel
static InputParameters validParams()
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.
const std::vector< double > x
ExponentialCovariance(const InputParameters &parameters)
const torch::Tensor & _sigma_n_squared
noise variance (^2)
static void ExponentialFunction(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 torch::Tensor &gamma, const bool is_self_covariance)
const torch::Tensor & _sigma_f_squared
signal variance (^2)
static void computedKdlf(torch::Tensor &K, const torch::Tensor &x, const torch::Tensor &length_factor, const torch::Tensor &sigma_f_squared, const torch::Tensor &gamma, const int ind)
Computes dK/dlf for individual length factors.
const torch::Tensor & _length_factor
lengh factor () for the kernel, in vector form for multiple parameters