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_surveillance.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function MDP = DEM_surveillance
% Demo of active (visual) scene-construction
%__________________________________________________________________________
%
% This routine uses a Markov decision process formulation of active
% inference (with belief propagation) to model active scene construction.
% It focuses on a discrete state space representation of a dynamic scene;
% generating visual snapshots at about the same frequency of saccadic eye
% movements. The generative model starts with latent states that correspond
% to natural kinds (e.g., objects) subject to natural laws (e.g., object
% invariance, classical mechanics, occlusion, and so on). A second latent
% factor (e.g., a 'where' stream) generates the fixation points in visual
% space for saccadic eye movements. The factors corresponding to multiple
% objects are themselves a Kronecker tensor product of attributes that
% depend upon each other; for example, position, velocity, pose, and
% non-spatial attributes that depend on spatial attributes. This
% interdependence means that object-specific attributes cannot be
% factorised; hence their combination as a tensor product (e.g., a 'what'
% stream).
%
% In what follows, we build a generative model, starting from state
% transitions that entail natural laws. Position refers to radial
% coordinates in egocentric space, implying a distinction between angular
% and radial (depth) states - and likewise for motion. This allows us to
% incorporate head orientation; in the form of head movements that
% reorientate the direction of gaze - that also depends upon the deployment
% of saccades in a head-centred frame of reference. Head movements are
% implemented, in the generative model, as moving objects in the egocentric
% frame of reference. This means that head movement is implemented via
% action-dependent transitions in location, while saccades are implemented
% via transitions among the latent states representing where gaze is
% deployed (in a head-centred frame of reference).
%
% Equipped with all of these hidden states, one can then complete a
% relatively simple generative model by specifying the likelihood mapping
% from hidden states to observations. This likelihood mapping is a high
% dimensional tensor - encoding all the high order dependencies generating
% visual input for the epoch in question. High order here refers to
% dependencies such as the interaction between two objects in the same line
% of sight that depends upon their relative depth to model occlusions.
%
% These outcomes are themselves discrete and multimodal. a high acuity
% modality models the parvocellular stream, with a restricted (central)
% field of view. This is complemented by two other modalities with a more
% peripheral field of view reporting contrast and motion energy, that is
% not spatially resolved (cf, the magnocellular stream). Note that in this
% construction (designed to generate the outputs of a computer vision
% scheme) motion is converted into a categorical (present versus absent)
% variable over discrete epochs of time. Note that the kind of scene
% construction and representation is implemented in egocentric and head
% centric frames of reference throughout. There is no part of the
% generative model that requires an allocentric representation - and yet,
% the agent can skilfully navigate a relatively complicated moving
% environment. in the example here, there are two inanimate objects (that
% play the role of landmarks) and an inanimate object (namely, a person who
% occasionally engages the agent with eye contact). This setup allows the
% simulation of reciprocal gaze and a primitive form of dyadic interaction.

    
function MDP = DEM_surveillance
% Demo of active (visual) scene-construction
%__________________________________________________________________________
%
% This routine uses a Markov decision process formulation of active
% inference (with belief propagation) to model active scene construction.
% It focuses on a discrete state space representation of a dynamic scene;
% generating visual snapshots at about the same frequency of saccadic eye
% movements. The generative model starts with latent states that correspond
% to natural kinds (e.g., objects) subject to natural laws (e.g., object
% invariance, classical mechanics, occlusion, and so on). A second latent
% factor (e.g., a 'where' stream) generates the fixation points in visual
% space for saccadic eye movements. The factors corresponding to multiple
% objects are themselves a Kronecker tensor product of attributes that
% depend upon each other; for example, position, velocity, pose, and
% non-spatial attributes that depend on spatial attributes. This
% interdependence means that object-specific attributes cannot be
% factorised; hence their combination as a tensor product (e.g., a 'what'
% stream).
%
% In what follows, we build a generative model, starting from state
% transitions that entail natural laws. Position refers to radial
% coordinates in egocentric space, implying a distinction between angular
% and radial (depth) states - and likewise for motion. This allows us to
% incorporate head orientation; in the form of head movements that
% reorientate the direction of gaze - that also depends upon the deployment
% of saccades in a head-centred frame of reference. Head movements are
% implemented, in the generative model, as moving objects in the egocentric
% frame of reference. This means that head movement is implemented via
% action-dependent transitions in location, while saccades are implemented
% via transitions among the latent states representing where gaze is
% deployed (in a head-centred frame of reference).
%
% Equipped with all of these hidden states, one can then complete a
% relatively simple generative model by specifying the likelihood mapping
% from hidden states to observations. This likelihood mapping is a high
% dimensional tensor - encoding all the high order dependencies generating
% visual input for the epoch in question. High order here refers to
% dependencies such as the interaction between two objects in the same line
% of sight that depends upon their relative depth to model occlusions.
%
% These outcomes are themselves discrete and multimodal. a high acuity
% modality models the parvocellular stream, with a restricted (central)
% field of view. This is complemented by two other modalities with a more
% peripheral field of view reporting contrast and motion energy, that is
% not spatially resolved (cf, the magnocellular stream). Note that in this
% construction (designed to generate the outputs of a computer vision
% scheme) motion is converted into a categorical (present versus absent)
% variable over discrete epochs of time. Note that the kind of scene
% construction and representation is implemented in egocentric and head
% centric frames of reference throughout. There is no part of the
% generative model that requires an allocentric representation - and yet,
% the agent can skilfully navigate a relatively complicated moving
% environment. in the example here, there are two inanimate objects (that
% play the role of landmarks) and an inanimate object (namely, a person who
% occasionally engages the agent with eye contact). This setup allows the
% simulation of reciprocal gaze and a primitive form of dyadic interaction.
% In other words, the prior preferences of this agent are to position
% itself and its direction of gaze to find someone who is looking at her.
%
% The code below is briefly annotated to illustrate how to build a
% generative model and then simulate active inference under that model, to
% produce relatively realistic sampling of a visual scene; namely, active
% scene construction. This inversion uses a sophisticated active inference
% scheme based upon a recursive estimation of expected free energy. This
% finesses the numerics because it uses belief propagation into the future
% - as opposed to marginal (variational) message passing. The numerical
% complexity of these models is a nontrivial issue: this is because most of
% the heavy lifting in the generative model is in the connectivity encoding
% dependencies that corresponds to high-dimensional tensors. In these
% simulations, the connectivity tensors are represented in working memory;
% whereas, in the brain or analogue (neuromorphic) implementations they
% would be simpler to instantiate.
%_________________________________________________________________________

% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


rng('default')

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

% Latent states
%--------------------------------------------------------------------------
% First we specify the hidden states for each factor (i.e., object). Each
% object has a number of attributes: line of sight, depth (foreground,
% background and distant), movement (none, right, left, retreat, approach),
% and disposition (e.g., looking in my direction or not).
%
% Note that there are several attributes that are shared by different
% objects or natural kinds; for example, an object can be in one of seven
% lines of sight. However, only animate objects can move.
%--------------------------------------------------------------------------

% number of: lines of sight, depth, motion and disposition
%--------------------------------------------------------------------------
N     = [ ...
    7,3,5,2;               % an animate object   (e.g., person)
    7,3,1,1;               % an inanimate object (e.g., landmark)
    7,3,1,1];              % an inanimate object (e.g., landmark)


% associate each object (i.e., natural kind) with a latent factor
%--------------------------------------------------------------------------
% This sets up a label structure with the names and cardinality of each
% factor and the accompanying latent states. These are further equipped
% with a number of actions that require a probability transition matrix for
% each allowable action. Here, the actions correspond to turning the head
% in one direction or another - or maintaining head position (the action
% 'stay')
%--------------------------------------------------------------------------
for i = 1:size(N,1)
    label.factor{i} = sprintf('kind %i',i);
    for j = 1:prod(N(i,:))
        label.name{i}{j}   = 'attributes';
    end