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% STK_DISCRETECOV computes a covariance matrix for a discrete model
%
% CALL: K = stk_discretecov (PARAM, X, Y)
%
% computes the covariance matrix K between the sets of locations X and
% Y, using the discrete model (a kriging model on a discrete space)
% parameters PARAM. The inputs arguments X and Y are expected to be
% vectors of indices. The output matrix K has size NX x NY, where NX is
% the length of X and NY the length of Y. PARAM must contain a field
% PARAM.K which is the full covariance matrix on the discrete input
% space.
%
% CALL: K = stk_discretecov (PARAM, X, Y, DIFF, PAIRWISE)
%
% computes the covariance *vector* between the sets of locations X and
% Y if PAIRWISE is TRUE, and the covariance *matrix* otherwise. In the
% first case, the output K is a vector of length N, where N is the
% common number of rows of X and Y. In the second, it is an NX x NY
% matrix as above. The argument DIFF has no effect, but must be equal
% to -1 since this is not a parametric model.
% Copyright Notice
%
% Copyright (C) 2015, 2016 CentraleSupelec
% Copyright (C) 2013, 2014 SUPELEC
%
% Authors: Julien Bect <julien.bect@centralesupelec.fr>
% Emmanuel Vazquez <emmanuel.vazquez@centralesupelec.fr>
% Copying Permission Statement
%
% This file is part of
%
% STK: a Small (Matlab/Octave) Toolbox for Kriging
% (http://sourceforge.net/projects/kriging)
%
% STK is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free
% Software Foundation, either version 3 of the License, or (at your
% option) any later version.
%
% STK is distributed in the hope that it will be useful, but WITHOUT
% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
% or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
% License for more details.
%
% You should have received a copy of the GNU General Public License
% along with STK. If not, see <http://www.gnu.org/licenses/>.
function K = stk_discretecov (param, x, y, diff, pairwise)
%--- process input arguments ---------------------------------------------------
x = double (x);
y = double (y);
if (nargin >= 4) && (diff ~= -1),
stk_error ('diff should be equal to -1', 'InvalidArgument');
end
if nargin < 5,
pairwise = false;
end
%--- compute covariance matrix -------------------------------------------------
if ~pairwise,
K = param.K(x, y);
else
idx = sub2ind (size (param.K), x, y);
K = param.K(idx);
end
end % function
%!shared model, model2, x0
%! n0 = 20; n1 = 10; dim = 4;
%! x0 = stk_sampling_randunif (n0, dim);
%! x1 = stk_sampling_randunif (n1, dim);
%! model = stk_model ('stk_materncov52_aniso', dim);
%! model.lm = stk_lm_affine ();
%! model.param = log ([1.0; 2.1; 2.2; 2.3; 2.4]);
%!test % without noise, pairwise = false
%! model.lognoisevariance = - inf;
%! model2 = stk_model ('stk_discretecov', model, x0);
%! idx = [1 4 9];
%! [K1, P1] = stk_make_matcov (model, x0(idx, :));
%! [K2, P2] = stk_make_matcov (model2, idx');
%! assert (stk_isequal_tolrel (K1, K2));
%! assert (stk_isequal_tolrel (P1, P2));
%!test % without noise, pairwise = true
%! K1 = stk_make_matcov (model, x0([2 5 6], :), [], true);
%! K2 = stk_make_matcov (model2, [2 5 6]', [], true);
%! assert (stk_isequal_tolrel (K1, K2));
%!test % with noise, pairwise = false
%! model.lognoisevariance = log (0.01);
%! model2 = stk_model ('stk_discretecov', model, x0);
%! idx = [1 4 9];
%! [K1, P1] = stk_make_matcov (model, x0(idx, :));
%! [K2, P2] = stk_make_matcov (model2, idx');
%! assert (stk_isequal_tolrel (K1, K2));
%! assert (stk_isequal_tolrel (P1, P2));
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