diff toolboxes/FullBNT-1.0.7/bnt/general/mk_limid.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_limid.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,93 @@
+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);