function c = tapas_softmax_config %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Contains the configuration for the softmax observation model for multinomial responses % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % -------------------------------------------------------------------------------------------------- % 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 . % Config structure c = struct; % Is the decision based on predictions or posteriors? Comment as appropriate. c.predorpost = 1; % Predictions %c.predorpost = 2; % Posteriors % Model name c.model = 'softmax'; % Sufficient statistics of Gaussian parameter priors % Beta c.logbemu = log(1); c.logbesa = 4^2; % Gather prior settings in vectors c.priormus = [ c.logbemu,... ]; c.priorsas = [ c.logbesa,... ]; % Model filehandle c.obs_fun = @tapas_softmax; % Handle to function that transforms observation parameters to their native space % from the space they are estimated in c.transp_obs_fun = @tapas_softmax_transp; return;