You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: DEM_demo_MDP_rule.m
    path of file: ./repos/bspm/thirdparty/spm12/toolbox/DEM
    
    the code of the file until where you have to start completion: function MDP = DEM_demo_MDP_rule
% Demo of active inference for visual salience
%__________________________________________________________________________
%
% This routine simulates a crude form of consciousness using active
% inference and structure learning to vitiate ignorance or nescience. This
% entails learning the hyper parameters of causal structure generating
% outcomes and then using Bayesian model reduction (during sleep) to
% minimise complexity.
%
% We first set up an abstract problem in which an agent has to respond
% according to rules (identify the correct colour depending upon one of
% three rules that are specified by the colour of a cue in the centre of
% vision). If the rule is centre, the colour is always green; however, if
% the colour of the Centre cue is red, the correct colour is on the left
% (and on the right if the queue is blue). Simulations are provided when
% the agent knows the rules. This is then repeated in the absence
% (nescience) of any knowledge about the rules to see if the agent can learn
% causal structure through Bayesian belief updating of the likelihood array
% (A).
%
% We then consider the improvement in performance (in terms of variational
% free energy, its constituent parts and performance) following Bayesian
% model reduction of the likelihood model (heuristically, like slow wave
% sleep), followed by a restitution of posterior beliefs during fictive
% active inference (as in REM sleep). Finally, we address the communication
% of the implicit  structure learning to a conspecific or child to
% demonstrate the improvement under instruction.
%
% see also: DEM_demo_MDP_habits.m and spm_MPD_VB_X.m
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: DEM_demo_MDP_rule.m 6814 2016-06-19 10:24:46Z karl $

% set up and preliminaries
%==========================================================================

    
function MDP = DEM_demo_MDP_rule
% Demo of active inference for visual salience
%__________________________________________________________________________
%
% This routine simulates a crude form of consciousness using active
% inference and structure learning to vitiate ignorance or nescience. This
% entails learning the hyper parameters of causal structure generating
% outcomes and then using Bayesian model reduction (during sleep) to
% minimise complexity.
%
% We first set up an abstract problem in which an agent has to respond
% according to rules (identify the correct colour depending upon one of
% three rules that are specified by the colour of a cue in the centre of
% vision). If the rule is centre, the colour is always green; however, if
% the colour of the Centre cue is red, the correct colour is on the left
% (and on the right if the queue is blue). Simulations are provided when
% the agent knows the rules. This is then repeated in the absence
% (nescience) of any knowledge about the rules to see if the agent can learn
% causal structure through Bayesian belief updating of the likelihood array
% (A).
%
% We then consider the improvement in performance (in terms of variational
% free energy, its constituent parts and performance) following Bayesian
% model reduction of the likelihood model (heuristically, like slow wave
% sleep), followed by a restitution of posterior beliefs during fictive
% active inference (as in REM sleep). Finally, we address the communication
% of the implicit  structure learning to a conspecific or child to
% demonstrate the improvement under instruction.
%
% see also: DEM_demo_MDP_habits.m and spm_MPD_VB_X.m
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: DEM_demo_MDP_rule.m 6814 2016-06-19 10:24:46Z karl $

% set up and preliminaries
%==========================================================================

% specify the (rule-based) generative process (environment)
%==========================================================================
rng('default')

% prior beliefs about initial states (in terms of counts_: D
%--------------------------------------------------------------------------
D{1} = [1 1 1]';        % rule:   {'left','centre','right'}
D{2} = [1 1 1]';        % what:   {'red','green','blue'}
D{3} = [0 0 0 1]';      % where:  {'left','centre','right','null'}
D{4} = [0 0 0 1]';      % report: {'red','green','blue','undecided'}

% probabilistic mapping from hidden states to outcomes: A
%--------------------------------------------------------------------------
Nf    = numel(D);
Ns    = zeros(1,Nf);
for f = 1:Nf
    Ns(f) = numel(D{f});
end