Mauch MIREX 2010

function bnet = mk_limid(dag, node_sizes, varargin)

% MK_LIMID Make a limited information influence diagram

%

% BNET = MK_LIMID(DAG, NODE_SIZES, ...) 

% DAG is the adjacency matrix for a directed acyclic graph.

% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.

% For decision nodes, the parents must explicitely include all nodes

% on which it can depends, in contrast to the implicit no-forgetting assumption of influence diagrams.

% (For details, see "Representing and solving decision problems with limited information",

%    Lauritzen and Nilsson, Management Science, 2001.)

%

% node_sizes(i) is the number of values node i can take on,

%   or the length of node i if i is a continuous-valued vector.

% node_sizes(i) = 1 if i is a utility node.

% 

% The list below gives optional arguments [default value in brackets].

% 

% chance   - the list of nodes which are random variables [1:N]

% decision - the list of nodes which are decision nodes [ [] ]

% utility  - the list of nodes which are utility nodes [ [] ]

% equiv_class - equiv_class(i)=j  means node i gets its params from CPD{j} [1:N]

%

% e.g., limid = mk_limid(dag, ns, 'chance', [1 3], 'utility', [2])

n = length(dag);

% default values for parameters

bnet.chance_nodes = 1:n;

bnet.equiv_class = 1:n;

bnet.utility_nodes = [];

bnet.decision_nodes = [];

bnet.dnodes = 1:n; % discrete 

if nargin >= 3

  args = varargin;

  nargs = length(args);

  if ~isstr(args{1})

    if nargs >= 1, bnet.dnodes = args{1}; end

    if nargs >= 2, bnet.equiv_class = args{2}; end

  else    

    for i=1:2:nargs

      switch args{i},

       case 'equiv_class', bnet.equiv_class = args{i+1}; 

       case 'chance',      bnet.chance_nodes = args{i+1}; 

       case 'utility',     bnet.utility_nodes = args{i+1}; 

       case 'decision',    bnet.decision_nodes = args{i+1}; 

       case 'discrete',    bnet.dnodes = args{i+1}; 

        otherwise,  

	error(['invalid argument name ' args{i}]);       

      end

    end

  end

end

bnet.limid = 1;

bnet.dag = dag;

bnet.node_sizes = node_sizes(:)';

bnet.cnodes = mysetdiff(1:n, bnet.dnodes);

% too many functions refer to cnodes to rename it to cts_nodes - 

% We hope it won't be confused with chance nodes!

bnet.parents = cell(1,n);

for i=1:n

  bnet.parents{i} = parents(dag, i);

end

E = max(bnet.equiv_class);

mem = cell(1,E);

for i=1:n

  e = bnet.equiv_class(i);

  mem{e} = [mem{e} i];

end

bnet.members_of_equiv_class = mem;

bnet.CPD = cell(1, E);

% for e=1:E

%   i = bnet.members_of_equiv_class{e}(1); % pick arbitrary member

%   switch type{e}

%     case 'tabular',  bnet.CPD{e} = tabular_CPD(bnet, i);

%     case 'gaussian', bnet.CPD{e} = gaussian_CPD(bnet, i);

%     otherwise, error(['unrecognized CPD type ' type{e}]);

%   end

% end

directed = 1;

if ~acyclic(dag,directed)

  error('graph must be acyclic')

end

bnet.order = topological_sort(bnet.dag);