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    Metadata details:
    filename: covDiscrete.m
    path of file: ./repos/bads/gpml-matlab-v3.6-2015-07-07/cov
    
    the code of the file until where you have to start completion: function K = covDiscrete(s, hyp, x, z, i)

% Covariance function for discrete inputs. Given a function defined on the
% integers 1,2,3,..,s, the covariance function is parameterized as:
%
% k(x,z) = K_{xz},
%
% where K is a matrix of size (s x s).
%
% This implementation assumes that the inputs x and z are given as integers
% between 1 and s, which simply index the matrix K.

    
function K = covDiscrete(s, hyp, x, z, i)

% Covariance function for discrete inputs. Given a function defined on the
% integers 1,2,3,..,s, the covariance function is parameterized as:
%
% k(x,z) = K_{xz},
%
% where K is a matrix of size (s x s).
%
% This implementation assumes that the inputs x and z are given as integers
% between 1 and s, which simply index the matrix K.
%
% The hyperparameters specify the upper-triangular part of the Cholesky factor
% of K, where the (positive) diagonal elements are specified by their logarithm.
% If L = chol(K), so K = L'*L, then the hyperparameters are:
%
% hyp = [ log(L_11)
%             L_21
%         log(L_22)
%             L_31
%             L_32
%         ..
%         log(L_ss) ]
%
% The hyperparameters hyp can be generated from K using:
% L = chol(K); L(1:(s+1):end