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SquaredExponentialCovariance.h
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3 //*
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7 //* Licensed under LGPL 2.1, please see LICENSE for details
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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 SquaredExponentialFunction(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 bool is_self_covariance);
34 
36  bool computedKdhyper(torch::Tensor & dKdhp,
37  const torch::Tensor & x,
38  const std::string & hyper_param_name,
39  unsigned int ind) const override;
40 
42  static void computedKdlf(torch::Tensor & K,
43  const torch::Tensor & x,
44  const torch::Tensor & length_factor,
45  const torch::Tensor & sigma_f_squared,
46  const int ind);
47 
48 protected:
50  const torch::Tensor & _length_factor;
51 
53  const torch::Tensor & _sigma_f_squared;
54 
56  const torch::Tensor & _sigma_n_squared;
57 };
58 
59 #endif
SquaredExponentialCovariance(const InputParameters &parameters)
static const std::string K
Definition: NS.h:174
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)