diff toolboxes/FullBNT-1.0.7/bnt/general/mk_bnet.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_bnet.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,93 @@
+function bnet = mk_bnet(dag, node_sizes, varargin)
+% MK_BNET Make a Bayesian network.
+%
+% BNET = MK_BNET(DAG, NODE_SIZES, ...) makes a graphical model with an arc from i to j iff DAG(i,j) = 1.
+% Thus DAG is the adjacency matrix for a directed acyclic graph.
+% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
+%
+% 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.
+% 
+% Below are the names of optional arguments [and their default value in brackets].
+% Pass as 'PropertyName1', PropertyValue1, 'PropertyName2', PropertyValue2, ...
+% 
+% discrete - the list of nodes which are discrete random variables [1:N]
+% equiv_class - equiv_class(i)=j  means node i gets its params from CPD{j} [1:N]
+% observed - the list of nodes which will definitely be observed in every case [ [] ]
+% 'names' - a cell array of strings to be associated with nodes 1:n [{}]
+%    This creates an associative array, so you write e.g.
+%     'evidence(bnet.names{'bar'}) = 42' instead of  'evidence(2} = 42' 
+%     assuming names = { 'foo', 'bar', ...}.
+%
+% e.g., bnet = mk_bnet(dag, ns, 'discrete', [1 3])
+%
+% For backwards compatibility with BNT2, you can also specify the parameters in the following order
+%   bnet = mk_bnet(dag, node_sizes, discrete_nodes, equiv_class)
+
+n = length(dag);
+
+% default values for parameters
+bnet.equiv_class = 1:n;
+bnet.dnodes = 1:n; % discrete 
+bnet.observed = [];
+bnet.names = {};
+
+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 'discrete',    bnet.dnodes = args{i+1}; 
+       case 'observed',    bnet.observed = args{i+1}; 
+       case 'names',  bnet.names = assocarray(args{i+1}, num2cell(1:n)); 
+       otherwise,  
+	error(['invalid argument name ' args{i}]);       
+      end
+    end
+  end
+end
+
+bnet.observed = sort(bnet.observed); % for comparing sets
+bnet.hidden = mysetdiff(1:n, bnet.observed(:)');
+bnet.hidden_bitv = zeros(1,n);
+bnet.hidden_bitv(bnet.hidden) = 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);
+
+bnet.rep_of_eclass = zeros(1,E);
+for e=1:E
+  mems = bnet.members_of_equiv_class{e};
+  bnet.rep_of_eclass(e) = mems(1);
+end
+
+directed = 1;
+if ~acyclic(dag,directed)
+  error('graph must be acyclic')
+end
+
+bnet.order = topological_sort(bnet.dag);