LSST Applications  21.0.0-172-gfb10e10a+18fedfabac,22.0.0+297cba6710,22.0.0+80564b0ff1,22.0.0+8d77f4f51a,22.0.0+a28f4c53b1,22.0.0+dcf3732eb2,22.0.1-1-g7d6de66+2a20fdde0d,22.0.1-1-g8e32f31+297cba6710,22.0.1-1-geca5380+7fa3b7d9b6,22.0.1-12-g44dc1dc+2a20fdde0d,22.0.1-15-g6a90155+515f58c32b,22.0.1-16-g9282f48+790f5f2caa,22.0.1-2-g92698f7+dcf3732eb2,22.0.1-2-ga9b0f51+7fa3b7d9b6,22.0.1-2-gd1925c9+bf4f0e694f,22.0.1-24-g1ad7a390+a9625a72a8,22.0.1-25-g5bf6245+3ad8ecd50b,22.0.1-25-gb120d7b+8b5510f75f,22.0.1-27-g97737f7+2a20fdde0d,22.0.1-32-gf62ce7b1+aa4237961e,22.0.1-4-g0b3f228+2a20fdde0d,22.0.1-4-g243d05b+871c1b8305,22.0.1-4-g3a563be+32dcf1063f,22.0.1-4-g44f2e3d+9e4ab0f4fa,22.0.1-42-gca6935d93+ba5e5ca3eb,22.0.1-5-g15c806e+85460ae5f3,22.0.1-5-g58711c4+611d128589,22.0.1-5-g75bb458+99c117b92f,22.0.1-6-g1c63a23+7fa3b7d9b6,22.0.1-6-g50866e6+84ff5a128b,22.0.1-6-g8d3140d+720564cf76,22.0.1-6-gd805d02+cc5644f571,22.0.1-8-ge5750ce+85460ae5f3,master-g6e05de7fdc+babf819c66,master-g99da0e417a+8d77f4f51a,w.2021.48
LSST Data Management Base Package
Public Member Functions | List of all members
lsst::afw::math::NeuralNetCovariogram< T > Class Template Reference

a Covariogram that recreates a neural network with one hidden layer and infinite units in that layer More...

#include <GaussianProcess.h>

Inheritance diagram for lsst::afw::math::NeuralNetCovariogram< T >:
lsst::afw::math::Covariogram< T >

Public Member Functions

 ~NeuralNetCovariogram () override
 
 NeuralNetCovariogram ()
 
void setSigma0 (double sigma0)
 set the _sigma0 hyper parameter More...
 
void setSigma1 (double sigma1)
 set the _sigma1 hyper parameter More...
 
operator() (ndarray::Array< const T, 1, 1 > const &, ndarray::Array< const T, 1, 1 > const &) const override
 Actually evaluate the covariogram function relating two points you want to interpolate from. More...
 

Detailed Description

template<typename T>
class lsst::afw::math::NeuralNetCovariogram< T >

a Covariogram that recreates a neural network with one hidden layer and infinite units in that layer

Contains two hyper parameters (_sigma0 and _sigma1) that characterize the expected variance of the function being interpolated

see Rasmussen and Williams (2006) http://www.gaussianprocess.org/gpml/ equation 4.29

Definition at line 193 of file GaussianProcess.h.

Constructor & Destructor Documentation

◆ ~NeuralNetCovariogram()

template<typename T >
lsst::afw::math::NeuralNetCovariogram< T >::~NeuralNetCovariogram ( )
overridedefault

◆ NeuralNetCovariogram()

template<typename T >
lsst::afw::math::NeuralNetCovariogram< T >::NeuralNetCovariogram
explicit

Definition at line 2025 of file GaussianProcess.cc.

2025  {
2026  _sigma0 = 1.0;
2027  _sigma1 = 1.0;
2028 }

Member Function Documentation

◆ operator()()

template<typename T >
T lsst::afw::math::NeuralNetCovariogram< T >::operator() ( ndarray::Array< const T, 1, 1 > const &  p1,
ndarray::Array< const T, 1, 1 > const &  p2 
) const
overridevirtual

Actually evaluate the covariogram function relating two points you want to interpolate from.

Parameters
[in]p1the first point
[in]p2the second point

Reimplemented from lsst::afw::math::Covariogram< T >.

Definition at line 2031 of file GaussianProcess.cc.

2032  {
2033  int i, dim;
2034  double num, denom1, denom2, arg;
2035 
2036  dim = p1.template getSize<0>();
2037 
2038  num = 2.0 * _sigma0;
2039  denom1 = 1.0 + 2.0 * _sigma0;
2040  denom2 = 1.0 + 2.0 * _sigma0;
2041 
2042  for (i = 0; i < dim; i++) {
2043  num += 2.0 * p1[i] * p2[i] * _sigma1;
2044  denom1 += 2.0 * p1[i] * p1[i] * _sigma1;
2045  denom2 += 2.0 * p2[i] * p2[i] * _sigma1;
2046  }
2047 
2048  arg = num / ::sqrt(denom1 * denom2);
2049  return T(2.0 * (::asin(arg)) / 3.141592654);
2050 }
T asin(T... args)

◆ setSigma0()

template<typename T >
void lsst::afw::math::NeuralNetCovariogram< T >::setSigma0 ( double  sigma0)

set the _sigma0 hyper parameter

Definition at line 2053 of file GaussianProcess.cc.

2053  {
2054  _sigma0 = sigma0;
2055 }

◆ setSigma1()

template<typename T >
void lsst::afw::math::NeuralNetCovariogram< T >::setSigma1 ( double  sigma1)

set the _sigma1 hyper parameter

Definition at line 2058 of file GaussianProcess.cc.

2058  {
2059  _sigma1 = sigma1;
2060 }
table::Key< double > sigma1

The documentation for this class was generated from the following files: