covseiso.m

来自「高斯过程在回归和分类问题中的应用」· M 代码 · 共 37 行

function [A, B] = covSEiso(loghyper, x, z);

% Squared Exponential covariance function with isotropic distance measure. The 
% covariance function is parameterized as:
%
% k(x^p,x^q) = sf2 * exp(-(x^p - x^q)'*inv(P)*(x^p - x^q)/2) 
%
% where the P matrix is ell^2 times the unit matrix and sf2 is the signal
% variance. The hyperparameters are:
%
% loghyper = [ log(ell)
%              log(sqrt(sf2)) ]
%
% For more help on design of covariance functions, try "help covFunctions".
%
% (C) Copyright 2006 by Carl Edward Rasmussen (2007-06-25)

if nargin == 0, A = '2'; return; end              % report number of parameters

[n D] = size(x);
ell = exp(loghyper(1));                           % characteristic length scale
sf2 = exp(2*loghyper(2));                                     % signal variance

if nargin == 2
  A = sf2*exp(-sq_dist(x'/ell)/2);
elseif nargout == 2                              % compute test set covariances
  A = sf2*ones(size(z,1),1);
  B = sf2*exp(-sq_dist(x'/ell,z'/ell)/2);
else                                                % compute derivative matrix
  if z == 1                                                   % first parameter
    A = sf2*exp(-sq_dist(x'/ell)/2).*sq_dist(x'/ell);  
  else                                                       % second parameter
    A = 2*sf2*exp(-sq_dist(x'/ell)/2);
  end
end