Template Class NeuralNetCovariogram

Inheritance Relationships

Base Type

Class Documentation

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

Public Functions

~NeuralNetCovariogram()
NeuralNetCovariogram()
void setSigma0(double sigma0)

set the _sigma0 hyper parameter

void setSigma1(double sigma1)

set the _sigma1 hyper parameter

T operator()(ndarray::Array<const T, 1, 1> const &p1, ndarray::Array<const T, 1, 1> const &p2) const

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

Parameters

  • [in] p1: the first point

  • [in] p2: the second point