function [K, C] = gp_trvar(gp, x1, predcf)
%GP_TRVAR  Evaluate training variance vector. 
%
%  Description
%    K = GP_TRVAR(GP, TX, PREDCF) takes in Gaussian process GP and
%    matrix TX that contains training input vectors to GP. Returns
%    (noiseless) variance vector K for latent values (diagonal of
%    the covariance matrix returned by gp_trcov), which is formed
%    as a sum of the variances from covariance functions in GP.cf
%    array. Every element ij of K contains covariance between
%    inputs i and j in TX. PREDCF is an array specifying the
%    indexes of covariance functions, which are used for forming
%    the matrix. If not given, the matrix is formed with all
%    functions.
%
%    [K, C] = GP_TRCOV(GP, TX, PREDCF) returns also the (noisy)
%    variance vector C, which is sum of K and the variance term
%    for example, from Gaussian noise.
%
%  See also
%    GP_SET, GPCF_*
%
% Copyright (c) 2006 Jarno Vanhatalo
% Copyright (c) 2010 Tuomas Nikoskinen

% This software is distributed under the GNU General Public 
% License (version 3 or later); please refer to the file 
% License.txt, included with the software, for details.

[n,m]=size(x1);
n1 = n+1;
ncf = length(gp.cf);

if ~(isfield(gp,'derivobs') && gp.derivobs)
  % Evaluate the covariance without noise
  K = 0;
  if nargin < 3 || isempty(predcf)
    predcf = 1:ncf;
  end      
  for i=1:length(predcf)
    gpcf = gp.cf{predcf(i)};
    K = K + gpcf.fh.trvar(gpcf, x1);
  end

  if ~isempty(gp.jitterSigma2)
    K = K + gp.jitterSigma2;
  end

  if nargout >1
    C=K;
    if isfield(gp.lik.fh,'trvar')
      % Add Gaussian noise to the covariance
      C = C + gp.lik.fh.trvar(gp.lik, x1);
    end
  end
else
  % Derivative observations
  
  % Evaluate the covariance without noise
  K = 0;
  gpcf = gp.cf{1};
  %right sized vector for the tr_var
  x2=zeros(m*n+n,1);
  K = K + gpcf.fh.trvar(gpcf, x2);

  if ~isempty(gp.jitterSigma2)
    K = K + gp.jitterSigma2;
  end

  if nargout >1
    C=K;
    if isfield(gp.lik.fh,'trvar')
      % Add Gaussian noise to the covariance
      C = C + gp.lik.fh.trvar(gp.lik, x2);
    end
    C(C<eps)=0;
  end
  K(K<eps)=0;
  
end