function varargout = exp_llado2022(varargin)
%EXP_LLADO2022 Experiments of Llado et al. (2022)
%
%   Usage: [] = exp_llado2022(flag) 
%
%   EXP_LLADO2022(flag) reproduces figures and results of the study  
%   from LLado et al. (2022).
%
%
%   To display Fig.5 use :
%
%     exp_llado2022('fig5');
%
%   To display Fig.6 use :
%
%     exp_llado2022('fig6');
%
%
%   See also: llado2022
%
%   Url: http://amtoolbox.org/amt-1.4.0/doc/experiments/exp_llado2022.php


%   #Author: Pedro Lladó (2021)
%   #Author: Petteri Hyvärinen (2021)
%   #Author: Ville Pulkki (2021)
%   #Author: Clara Hollomey (2022): adaptations for AMT

% This file is licensed unter the GNU General Public License (GPL) either 
% version 3 of the license, or any later version as published by the Free Software 
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and 
% at <https://www.gnu.org/licenses/gpl-3.0.html>. 
% You can redistribute this file and/or modify it under the terms of the GPLv3. 
% This file is distributed without any warranty; without even the implied warranty 
% of merchantability or fitness for a particular purpose. 


definput.flags.type = {'missingflag', 'fig5', 'fig6'};

[flags,~]  = ltfatarghelper({},definput,varargin);

if flags.do_missingflag
  flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
             sprintf('%s or %s',definput.flags.type{end-1},...
             definput.flags.type{end})];
  error('%s: You must specify one of the following flags: %s.', ...
      upper(mfilename),flagnames);
end

%% Load precomputed binaural estimates

    % Load pretrained model
    x = amt_load('llado2022', 'NN_pretrained.mat');
    NN_pretrained = x.NN_pretrained;
    
if flags.do_fig5
    % Load extracted binaural features itd and ild features
    x_input = [NN_pretrained.x_itd;NN_pretrained.x_ild];
    
    %% Training set: all devices but the test device
    testDevice = 'F-Gecko';
    
    % Getting the test subset
    angle_id = NN_pretrained.angle_id;
    nAngles = NN_pretrained.nAngles;
    device_id = NN_pretrained.device_id;
    nDevices = NN_pretrained.nDevices;
    y_output = NN_pretrained.y';
    testDevice_id = find(device_id == testDevice);

    testDevicePos = nAngles*(testDevice_id-1)+1:nAngles*(testDevice_id);

    x_test = x_input(:,testDevicePos);
    y_test = y_output(testDevicePos,:);
    
    %% evaluate pretrained model
    y_hat = llado2022_evaluatenn(x_test,NN_pretrained);    
    y_est_dir = y_hat(:,1);
    y_est_uncertainty = y_hat(:,2);


    if (isvector(y_est_dir) == 1 )
        y_est_dir = y_est_dir;
        y_est_uncertainty = y_est_uncertainty;
    else
        y_est_dir = mean(y_est_dir);
        y_est_uncertainty = mean(y_est_uncertainty);
    end

    plot_llado2022(y_est_dir,y_est_uncertainty,angle_id,y_test);
end

if flags.do_fig6
    llado2022_weightsanalysis(NN_pretrained);
end