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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);
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