Experiments using periodic LF kernel.

% modelType = 'sim';  % First order differential equation kernel (see simMeanCreate.m)

modelType = 'gg';   % Gaussian kernel (for convolution) and Gaussian covariance

% modelType = 'lmc';   % Linear model of corregionalization

% modelType = 'gg-periodic'; % A custom combo we created. We had to modify

                             % multigpCreate and multigpKernComposer to add this one.

% modelType = 'lmc-periodic'; % Another custom kernel

nlf = 2;                  % number of latent forces

nlf = 1;                  % number of latent forces

missing_rate = 0;         % set to 0.9 for the 90% missing rate experiment

gf_experiment = true;     % set to true for the gap-filling experiment

gf_range = [2009,2014];   % setup range (from 2019 to 2010-2014)

% Filename for model

mdate = datestr(now, 'yyyymmdd_hhMM');

switch lower(modelType)

    case {'sim', 'gg'}

    case {'sim', 'gg', 'gg-periodic'}

        fname = sprintf('SPARC_model_rev1_%s_%s_nlf_%d_mr_%0.2f_%s.mat', ...

            var, upper(modelType), nlf, missing_rate, mdate);

    case 'lmc'

    case {'lmc', 'lmc-periodic'}

        fname = sprintf('SPARC_model_rev1_%s_%s_nlf_%d_rank_%d_mr_%0.2f_%s.mat', ...

            var, upper(modelType), nlf, size(Ytrain,2), missing_rate, mdate);

    otherwise

        error(['Error unknown modelType ', modelType])

end

if gf_experiment

    % fname = strrep(fname, '_SPARC_', '_SPARC_gapfilling_');

switch lower(modelType)

    case 'sim'

        options.nlf = nlf;

    case 'gg'

    case {'gg', 'gg-periodic'}

        % Options for GG Full Cov. Latent Force model

        options.nlf = nlf; % size(Ytrain, 2); % <= 3 latent forces in this case

    case 'lmc'

    case {'lmc', 'lmc-periodic'}

        % Options for linear model of coregionalization (LMC).

        % Here the number of effective latent forces are given by

        % nlf * rankCorregMatrix. As far as I understand, we should set