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tapas_hgf_plotTraj.m
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function tapas_hgf_plotTraj(r)
% Plots the estimated or generated trajectories for the HGF perceptual model
% Usage example: est = tapas_fitModel(responses, inputs); tapas_hgf_plotTraj(est);
%
% --------------------------------------------------------------------------------------------------
% Copyright (C) 2012-2013 Christoph Mathys, TNU, UZH & ETHZ
%
% This file is part of the HGF toolbox, which is released under the terms of the GNU General Public
% Licence (GPL), version 3. You can redistribute it and/or modify it under the terms of the GPL
% (either version 3 or, at your option, any later version). For further details, see the file
% COPYING or <http://www.gnu.org/licenses/>.
% Optional plotting of standard deviations (true or false)
plotsd = true;
% Set up display
scrsz = get(0,'screenSize');
outerpos = [0.2*scrsz(3),0.2*scrsz(4),0.8*scrsz(3),0.8*scrsz(4)];
figure(...
'OuterPosition', outerpos,...
'Name', 'HGF trajectories');
% Time axis
if size(r.u,2) > 1 && ~isempty(find(strcmp(fieldnames(r.c_prc),'irregular_intervals'))) && r.c_prc.irregular_intervals
t = r.u(:,end)';
else
t = ones(1,size(r.u,1));
end
ts = cumsum(t);
ts = [0, ts];
% Number of levels
l = length(r.p_prc.p)/5;
% Upper levels
for j = 1:l-1
% Subplots
subplot(l,1,j);
if plotsd == true
upperprior = r.p_prc.mu_0(l-j+1) +sqrt(r.p_prc.sa_0(l-j+1));
lowerprior = r.p_prc.mu_0(l-j+1) -sqrt(r.p_prc.sa_0(l-j+1));
upper = [upperprior; r.traj.mu(:,l-j+1)+sqrt(r.traj.sa(:,l-j+1))];
lower = [lowerprior; r.traj.mu(:,l-j+1)-sqrt(r.traj.sa(:,l-j+1))];
plot(0, upperprior, 'ob', 'LineWidth', 1);
hold all;
plot(0, lowerprior, 'ob', 'LineWidth', 1);
fill([ts, fliplr(ts)], [(upper)', fliplr((lower)')], ...
'b', 'EdgeAlpha', 0, 'FaceAlpha', 0.15);
end
plot(ts, [r.p_prc.mu_0(l-j+1); r.traj.mu(:,l-j+1)], 'b', 'LineWidth', 2);
hold all;
plot(0, r.p_prc.mu_0(l-j+1), 'ob', 'LineWidth', 2); % prior
xlim([0 ts(end)]);
title(['Posterior expectation of x_' num2str(l-j+1)], 'FontWeight', 'bold');
ylabel(['\mu_', num2str(l-j+1)]);
end
% Input level
subplot(l,1,l);
if plotsd == true
upperprior = r.p_prc.mu_0(1) +sqrt(r.p_prc.sa_0(1));
lowerprior = r.p_prc.mu_0(1) -sqrt(r.p_prc.sa_0(1));
upper = [upperprior; r.traj.mu(:,1)+sqrt(r.traj.sa(:,1))];
lower = [lowerprior; r.traj.mu(:,1)-sqrt(r.traj.sa(:,1))];
plot(0, upperprior, 'or', 'LineWidth', 1);
hold all;
plot(0, lowerprior, 'or', 'LineWidth', 1);
fill([ts, fliplr(ts)], [(upper)', fliplr((lower)')], ...
'r', 'EdgeAlpha', 0, 'FaceAlpha', 0.15);
end
plot(ts, [r.p_prc.mu_0(1); r.traj.mu(:,1)], 'r', 'LineWidth', 2);
hold all;
plot(0, r.p_prc.mu_0(1), 'or', 'LineWidth', 2); % prior
plot(ts(2:end), r.u(:,1), '.', 'Color', [0 0.6 0]); % inputs
if ~isempty(find(strcmp(fieldnames(r),'y'))) && ~isempty(r.y)
plot(ts(2:end), r.y(:,1), '.', 'Color', [1 0.7 0]); % responses
title(['Response y (orange), input u (green), and posterior expectation of x_1 ', ...
'(red) for \rho=', num2str(r.p_prc.rho), ', \kappa=', ...
num2str(r.p_prc.ka), ', \omega=', num2str(r.p_prc.om), ...
', \pi_u=', num2str(r.p_prc.pi_u)], ...
'FontWeight', 'bold');
ylabel('y, u, \mu_1');
else
title(['Input u (green) and posterior expectation of x_1 ', ...
'(red) for \rho=', num2str(r.p_prc.rho), ', \kappa=', ...
num2str(r.p_prc.ka), ', \omega=', num2str(r.p_prc.om), ...
', \pi_u=', num2str(r.p_prc.pi_u)], ...
'FontWeight', 'bold');
ylabel('u, \mu_1');
end
xlim([0 ts(end)]);
xlabel({'Trial number', ' '}); % A hack to get the relative subplot sizes right
hold off;