function [logp, yhat, res] = tapas_bayes_optimal_whatworld(r, infStates, ptrans)
% Calculates the log-probability of the inputs given the current predictions
%
% --------------------------------------------------------------------------------------------------
% Copyright (C) 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 .
% Initialize returned log-probabilities as NaNs so that NaN is
% returned for all irregualar trials
n = size(infStates,1);
logp = NaN(n,1);
yhat = NaN(n,1);
res = NaN(n,1);
% Number of states whose contingencies have to be learned
ns = r.c_prc.n_states;
% Number of elements of the transition matrix
ntr = ns^2;
% Weed irregular trials out from predictions and inputs
% States
pred = squeeze(infStates(:,1,:,:,1,1));
pred(r.irr,:) = [];
% Inputs
u = r.u(:,1);
% Transitions: first column - to; second column - from;
ufrom = [1; u];
ufrom(end) = [];
tr = [u ufrom];
% Weed transitions from irregular trials out
tr(r.irr,:) = [];
% Calculate probabilities of transitions
for k = 1:length(u)
p(k) = pred(k,tr(k,1),tr(k,2));
end
% Calculate log-probabilities for non-irregular trials
reg = ~ismember(1:n,r.irr);
logp(reg) = log(p);
yhat(reg) = p;
res(reg) = -log(p);
return;