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: ADEM_mountaincar_loss_5.m
    path of file: ./repos/spm12/toolbox/DEM
    
    the code of the file until where you have to start completion: % This demo re-visits the mountain car problem to show that adaptive
% (desired) behaviour can be prescribed in terms of loss-functions (i.e.
% reward functions of state-space).
% It exploits the fact that under the free-energy formulation, loss is
% divergence. This means that priors can be used to make certain parts of
% state-space costly (i.e. with high divergence) and others rewarding (low
% divergence). Active inference under these priors will lead to sampling of
% low cost states and (apparent) attractiveness of those states.
%
% This is version five; where loss is a function of and only of drive
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: ADEM_mountaincar_loss_5.m 4626 2012-01-24 20:55:59Z karl $
 
% generative process (mountain car terrain)
%==========================================================================                        % switch for demo
 
% parameters of generative process
%--------------------------------------------------------------------------
P.a     = 0;
P.b     = [0 0];
P.c     = [0 0 0 0];
P.d     = 1;                                % action on
 
fx      = inline('spm_mc_fxa_5(x,v,a,P)','x','v','a','P');
gx      = inline('[x.x; x.v; exp(-(x.x - 1)^2*32); x.p]','x','v','a','P');
x0.x    = 0;
x0.v    = 0;
x0.p    = 0;
x0.q    = 0;


% level 1
%--------------------------------------------------------------------------
G(1).x  = x0;
G(1).f  = fx;
G(1).g  = gx;
G(1).pE = P;
G(1).V  = exp(16);                          % error precision
G(1).W  = exp(16);                          % error precision
 
% level 2
%--------------------------------------------------------------------------
G(2).a  = 0;                                % action
G(2).v  = 0;                                % inputs
G(2).V  = exp(16);
G       = spm_ADEM_M_set(G);
 
 
% generative model
%==========================================================================
clear P x0
 
% parameters (previously learned) and equations of motion
%--------------------------------------------------------------------------
P       = [2.7 1.7 0.74 -0.51 -0.85 0.08 -0.23 -1.15];
np      = length(P);
fx      = inline('spm_mc_fx_5(x,v,P)','x','v','P');
gx      = inline('[x.x; x.v; x.q; x.p]','x','v','P');
x0.x    = 0;
x0.v    = 0;
x0.p    = 0;
x0.q    = 0;
x0.c    = 0;

 
% level 1
%--------------------------------------------------------------------------
M(1).x  = x0;
M(1).f  = fx;
M(1).g  = gx;
M(1).pE = P;
M(1).V  = diag(exp([8 8 16 8]));             % error precision
M(1).W  = diag(exp([4 4 8 16]));             % error precision
 
% level 2
%--------------------------------------------------------------------------
M(2).v  = 0;                                % inputs
M(2).V  = exp(16);
M       = spm_DEM_M_set(M);
 
 
% learn gradients with a flat loss-functions (priors on divergence)
%==========================================================================
N       = 1600;
U       = sparse(N,M(1).m);
DEM.U   = U;
DEM.C   = U;
DEM.G   = G;
DEM.M   = M;
DEM     = spm_ADEM(DEM);
 

% show dynamics
%======================================
    
function of and only of drive
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: ADEM_mountaincar_loss_5.m 4626 2012-01-24 20:55:59Z karl $
 
% generative process (mountain car terrain)
%==========================================================================                        % switch for demo
 
% parameters of generative process
%--------------------------------------------------------------------------
P.a     = 0;
P.b     = [0 0];
P.c     = [0 0 0 0];
P.d     = 1;                                % action on
 
fx      = inline('spm_mc_fxa_5(x,v,a,P)','x','v','a','P');
gx      = inline('[x.x; x.v; exp(-(x.x - 1)^2*32); x.p]','x','v','a','P');
x0.x    = 0;
x0.v    = 0;
x0.p    = 0;
x0.q    = 0;


% level 1
%--------------------------------------------------------------------------
G(1).x  = x0;
G(1).f  = fx;
G(1).g  = gx;
G(1).pE = P;
G(1).V  = exp(16);                          % error precision
G(1).W  = exp(16);                          % error precision
 
% level 2
%--------------------------------------------------------------------------
G(2).a  = 0;                                % action
G(2).v  = 0;                                % inputs
G(2).V  = exp(16);
G       = spm_ADEM_M_set(G);
 
 
% generative model
%==========================================================================
clear P x0
 
% parameters (previously learned) and equations of motion
%--------------------------------------------------------------------------
P       = [2.7 1.7 0.74 -0.51 -0.85 0.08 -0.23 -1.15];
np      = length(P);
fx      = inline('spm_mc_fx_5(x,v,P)','x','v','P');
gx      = inline('[x.x; x.v; x.q; x.p]','x','v','P');
x0.x    = 0;
x0.v    = 0;
x0.p    = 0;
x0.q    = 0;
x0.c    = 0;

 
% level 1
%--------------------------------------------------------------------------
M(1).x  = x0;
M(1).f  = fx;
M(1).g  = gx;
M(1).pE = P;
M(1).V  = diag(exp([8 8 16 8]));             % error precision
M(1).W  = diag(exp([4 4 8 16]));             % error precision
 
% level 2
%--------------------------------------------------------------------------
M(2).v  = 0;                                % inputs
M(2).V  = exp(16);
M       = spm_DEM_M_set(M);
 
 
% learn gradients with a flat loss-functions (priors on divergence)
%==========================================================================
N       = 1600;
U       = sparse(N,M(1).m);
DEM.U   = U;
DEM.C   = U;
DEM.G   = G;
DEM.M   = M;
DEM     = spm_ADEM(DEM);
 

% show dynamics
%==========================================================================
 
% inference
%--------------------------------------------------------------------------
spm_figure('GetWin','Figure 1');
spm_DEM_qU(DEM.qU)
 
% true and inferred position
%--------------------------------------------------------------------------
subplot(2,2,3)
plot(DEM.pU.x{1}(1,:),DEM.pU.x{1}(2,:)),hold on
plot(   1,0,'r.','Markersize',32),      hold on
plot(-1/2,0,'g.','Markersize',16),      hold off
xlabel('position','Fontsize',14)
ylabel('velcitiy','Fontsize',14)
title('trajectories','Fontsize',16)
axis([-1 1 -1 1]*3)
axis square

% true position
%--------------------------------------------------------------------------
subplot(2,2,3)
plot3(DEM.pU.x{1}(1,:),DEM.pU.x{1}(2,:),1:N), hold on
plot3(   1,0,1:64:N,'r.','Markersize',8),     hold on
plot3(-1/2,0,1:64:N,'g.','Markersize',8),     hold off
xlabel('position','Fontsize',14)
ylabel('velocity','Fontsize',14)
zlabel('time','Fontsize',14)
title('trajectories','Fontsize',16)
axis([-2 2 -2 2 0 N])
axis square


% real states
%==========================================================================
spm_figure('GetWin','DEM');
spm_DEM_qU(DEM.pU)
 

subplot(2,2,1)
plot3(   1,0,1:1/8:2,'r.'),                                       hold on
plot3(-1/2,0,1:1/8:2,'g.'),                                       hold on
plot3(DEM.pU.x{1}(1,:),DEM.pU.x{1}(2,:),DEM.pU.x{1}(4,:)),        hold off
xlabel('position','Fontsize',14)
ylabel('velocity','Fontsize',14)
zlabel('satiety','Fontsize',14)
title('trajectories','Fontsize',16)
axis([-2 2 -2 2 0 8])



% cost function (see spm_mc_fx_4.m)
%--------------------------------------------------------------------------
subplot(2,1,2)
x     = -2:1/64:2;
d     =  0:1/64:2;
for i = 1:length(x)
    for j = 1:length(d)
        D      = spm_phi((1 - d(j))*8);
        A      = 2 - 32*exp(-(x(i) - 1).^2*32);
        C(i,j) = A*D - 1;
    end