s2_fit_all_local.m

%% Parse the hyperparameters
if ~exist('doCross',      'var'), doCross = false;       end
if ~exist('target',       'var'), target  = 'target';    end % 'target' or 'All';
if ~exist('optimizer',    'var'), optimizer = 'classic'; end % 'classic' or 'reparam';
if ~exist('start_idx',    'var'), start_idx = 1;         end % cache 
if ~exist('choose_model', 'var'), choose_model = 'more'; end  

switch doCross
    case false
        cross_valid = 'one';           % 'one': not cross validate; 'cross_valid': cross validate
        data_folder = 'noCross';       % save in which folder. value space: 'noCross', 'Cross'
    case true
        cross_valid = 'cross_valid';   % choose what kind of cross , value space: 'one', 'cross_valid'. 'one' is no cross validation.
        data_folder = 'Cross';         % save in which folder. value space: 'noCross', .....
end

fittime             = 40;        % how many initialization. value space: Integer
verbose             = 'off';     % show the fit details?

%% save address and create a table for all jobs

% save address
save_address = fullfile(stdnormRootPath, 'Data', data_folder, target,  optimizer);
if ~exist(save_address, 'dir'), mkdir(save_address); end

% create jobs
T = chooseData(choose_model, optimizer, fittime);

%% start Fit
% get the job using loop
for hpc_job_number = 1:size(T, 1)
    
    dataset   = T.dataset(hpc_job_number);
    roi       = T.roiNum(hpc_job_number);
    model_idx = T.modelNum(hpc_job_number);
    model     = T.modelLoader{hpc_job_number};
    
    % set the save info, this helps in
    % continuing job in the broken pipe
    save_temp = fullfile( save_address, 'temp');
    if ~exist(save_temp, 'dir'), mkdir(save_temp); end
    save_info.dir       = save_temp;
    save_info.roi       = roi;
    save_info.model_idx = model_idx;
    save_info.dataset   = dataset;
    save_info.start_idx = start_idx;
    
    % display information to keep track of fitting
    display = ['dataset: ' num2str(dataset), ' roi: ',num2str( roi), ' model: ', num2str(model_idx) ];
    disp(display)
    
    % load training label
    BOLD_target = dataloader(stdnormRootPath, 'BOLD_target', target, dataset, roi);
    
    % load the input E
    switch model.model_type
        case 'orientation'; which_obj = 'E_ori'; % CE, NOA
        case 'space';       which_obj = 'E_xy';  % SOC, OTS
    end
    % load contrast energy
    E = dataloader(stdnormRootPath, which_obj, target, dataset, roi);
    x = {E};
    
    disp(model.legend)
    % get pre-calc normalized energy
    switch model.legend
        case 'OTS'
            Z = dataloader(stdnormRootPath, 'Z1', target, dataset, roi);
            x{end + 1} = Z;
        case 'DN'
            Z = dataloader(stdnormRootPath, 'Z2', target, dataset, roi);
            x{end + 1} = Z;
    end
    
    % fit the data without cross validation: knock-1-out
    [BOLD_pred, params, Rsquare, model] = ...
        model.fit(model, x, BOLD_target, verbose, cross_valid, save_info);
    
    
    if strcmp( cross_valid, 'one')
        loss_log = model.loss_log;
    end
    
    % save data
    save(fullfile(save_address , sprintf('parameters_data-%d_roi-%d_model-%d.mat',dataset, roi, model_idx )) , 'params');
    save(fullfile(save_address , sprintf('prediction_data-%d_roi-%d_model-%d.mat',dataset, roi, model_idx )) , 'BOLD_pred');
    save(fullfile(save_address , sprintf('Rsquare_data-%d_roi-%d_model-%d.mat',dataset, roi, model_idx ))    , 'Rsquare');
    if strcmp( cross_valid, 'one')
        save(fullfile(save_address , sprintf('loss_log_data-%d_roi-%d_model-%d.mat',dataset, roi, model_idx )) , 'loss_log');
    end
end