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_fMRI_HMM.m
    path of file: ./repos/spm12/toolbox/DEM
    
    the code of the file until where you have to start completion: function DEM_demo_fMRI_HMM
% Demonstration of Hidden Markov models for fMRI
%__________________________________________________________________________
%  This demonstration routine illustrates the modelling of state
%  transitions generating resting state fMRI timeseries. The hidden states
%  are modelled as a hidden Markov model, where each state corresponds to a
%  particular  point in the parameter space of effective connectivity. This
%  effective connectivity then generates complex cross spectral data
%  features  of the observed timeseries. Model specification requires prior
%  constraints on the probability transition matrix among hidden states,
%  which implicitly specifies the number of hidden states. The user also
%  has to specify the number of windows for epochs to apply to the
%  timeseries, where each epoch  places a lower bound on the duration of
%  each (discrete) state.
%     We first generate synthetic data using regular transitions among
%  three hidden states  (C.F., a discrete version of a heteroclinic
%  cycle  for orbit). The data are then converted by a routine that
%  combines a parametric empirical Bayesian model and a hidden Markov model
%  (as implemented as a special case of a Markov decision process). This
%  inversion is repeated for each model specified in terms of the
%  transition matrices (as prior Dirichlet concentration parameters).
%  Setting a prior transition parameter to 0 precludes that transition. In
%  this way, several different models of transitions and number of hidden
%  states can be  scored in terms of  the variational free energy.

    
function DEM_demo_fMRI_HMM
% Demonstration of Hidden Markov models for fMRI
%__________________________________________________________________________
%  This demonstration routine illustrates the modelling of state
%  transitions generating resting state fMRI timeseries. The hidden states
%  are modelled as a hidden Markov model, where each state corresponds to a
%  particular  point in the parameter space of effective connectivity. This
%  effective connectivity then generates complex cross spectral data
%  features  of the observed timeseries. Model specification requires prior
%  constraints on the probability transition matrix among hidden states,
%  which implicitly specifies the number of hidden states. The user also
%  has to specify the number of windows for epochs to apply to the
%  timeseries, where each epoch  places a lower bound on the duration of
%  each (discrete) state.
%     We first generate synthetic data using regular transitions among
%  three hidden states  (C.F., a discrete version of a heteroclinic
%  cycle  for orbit). The data are then converted by a routine that
%  combines a parametric empirical Bayesian model and a hidden Markov model
%  (as implemented as a special case of a Markov decision process). This
%  inversion is repeated for each model specified in terms of the
%  transition matrices (as prior Dirichlet concentration parameters).
%  Setting a prior transition parameter to 0 precludes that transition. In
%  this way, several different models of transitions and number of hidden
%  states can be  scored in terms of  the variational free energy.
%     Following inversion, the results are plotted in terms of expected
%  state transitions, fluctuations in connections that are allowed to
%  change (specified in the usual way by DCM.b), the deviations in
%  connectivity associated with each hidden state and the expected
%  probability transition matrix.
%     Finally, we consider Bayesian model comparison in terms of group
%  differences (here, simply the difference between the first and second
%  simulated subject).  Bayesian model comparison is simple to do in this
%  context  by comparing the free energy of a hidden Markov model in which
%  both groups share the same state dependent connections and transition
%  probabilities, with two independent models. These can be evaluated
%  efficiently using Bayesian model reduction implicit in PEB. in this
%  example, we did not introduce any differences between the two groups
%  (i.e., subjects) and therefore expected to infer no group effect.
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: DEM_demo_fMRI_HMM.m 7679 2019-10-24 15:54:07Z spm $



% (I) Simulate fMRI timeseries
%==========================================================================
rng('default')

%  Assume we have P sessions with N epochs of T scans with a TR of 2 secs:
% -------------------------------------------------------------------------
%  These epochs could be from a single subject or result from the
%  concatenation of multiple sessions (under the assumption that they share
%  the same modes of connectivity).
% -------------------------------------------------------------------------
P  = 2;                               % number of sessions (e.g., subjects)
S  = 3;                               % number of latent (hidden) states
N  = 9;                               % number of epochs or windows
T  = 128;                             % number of observations (per epoch)
TR = 2;                               % repetition time or timing
t  = (1:(T*N))*TR;                    % observation times (seconds)
n  = 3;                               % number of regions or nodes

% setup model for generating timeseries
% -------------------------------------------------------------------------
options.nonlinear  = 0;
options.two_state  = 0;
options.stochastic = 0;
options.induced    = 1;

% get priors to generate simulated data
% -------------------------------------------------------------------------
a   = ones(n,n);
b   = zeros(n,n,0);
c   = zeros(n,n);
d   = zeros(n,n,0);
pP  = spm_dcm_fmri_priors(a,b,c,d,options);

% average parameters - a simple hierarchy of three nodes
% -------------------------------------------------------------------------
pP.A = [  0  -.3    0;
         .4    0  -.1;
          0   .3    0];
pP.C = eye(n,n);
pP.transit = randn(n,1)/64;

% generate spectral density of neuronal fluctuations and observation noise
% -------------------------------------------------------------------------
[Gu,Gn,Hz,dt] = spm_csd_fmri_gu(pP,TR);
Gu    = Gu(:,1,1)*ones(1,n);
Gn    = Gn(:,1,1)*ones(1,n);


% specify and generate Markovian succession of hidden states
%==========================================================================

% connections associated with hidden states: here, intrinsic connectivity
% -------------------------------------------------------------------------
B        = zeros(n,n,S);
B(1,1,1) = 1/2;
B(2,2,2) = 1/2;
B(3,3,3) = 1/2;
pP.B     = any(B,3);                     % state-depend